Author: Iñigo Careaga, strategy & communication manager, CIC energiGUNE

Lithium and sodium are neighbours in the periodic table. Both are alkali metals, both have a single electron in their outer shell, and both can be used to shuttle ions between two electrodes to store and release electrical energy. At first sight, replacing lithium with sodium in a rechargeable battery might therefore seem straightforward.

There are good reasons to try. Sodium is widely available and sodium-ion batteries can rely on electrode materials containing abundant elements such as iron and manganese. But chemical similarity does not mean electrochemical equivalence. A sodium ion is larger and heavier than a lithium ion, and its interaction with the materials surrounding it is different. As a consequence, replacing Li⁺ with Na⁺ changes much more than the identity of the charge carrier.

The electrodes have to accommodate a different ion. Their structures may respond differently as sodium is inserted and removed. The electrolyte has to transport and solvate it. New interfaces form between the electrolyte and the electrodes. Even graphite, the standard anode material of most commercial lithium-ion batteries, does not store sodium under conventional conditions nearly as effectively as it stores lithium ¹.

A sodium-ion battery is therefore not simply a lithium-ion battery containing a different element. Understanding why requires looking at what happens to sodium once it enters the battery.

Graphite does not play by the same rules

Charging a conventional lithium-ion battery involves moving lithium ions from the positive electrode through the electrolyte and storing them in the negative electrode. Graphite is particularly good at this job. Its carbon atoms are arranged in sheets, and lithium can reversibly occupy the spaces between them, a process known as intercalation. It would be convenient if sodium behaved in the same way. It largely does not.

The poor ability of conventional graphite to accommodate sodium has long been one of the reasons why lithium-ion concepts cannot simply be transferred to sodium-ion batteries. Instead, one of the most widely studied negative-electrode materials is hard carbon ².

Hard carbon is still carbon, but structurally it is very different from graphite. Rather than a regular stack of extended carbon sheets, it contains disordered graphitic domains, defects and pores. This disordered architecture provides environments in which sodium can be stored, but it also makes the underlying mechanism considerably harder to describe.

Where exactly does the sodium go? Researchers have proposed contributions from adsorption at defects and surfaces, insertion between graphitic layers and storage associated with nanopores. The relative importance of these mechanisms depends on the structure and microstructure of the carbon. This is more than a microscopic detail: where and how sodium is stored determines the voltage profile, reversible capacity, first-cycle losses and, ultimately, the energy that a complete cell can deliver.

Researchers at CIC energiGUNE and collaborators have studied this connection between charge-storage mechanisms, carbon structure and electrochemical performance ³. Their analysis highlights an important point: maximizing the amount of sodium that an anode can store is not enough. The potential at which that storage occurs and the irreversible losses during the first cycle are equally important when the material is incorporated into a full battery.

The problem is therefore not simply to find a carbon that accommodates more sodium. It is to understand how its structure stores sodium and then engineer that structure so that capacity, voltage and efficiency improve together. This has led researchers to explore different ways of controlling hard-carbon structure, including the use of lignocellulosic biomass as a precursor. Work has shown that even apparently simple processing decisions, such as acid treatment of the biomass precursor, can affect the resulting carbon and its behaviour as a sodium-ion anode ⁴.

And the negative electrode is only half of the problem. At the positive electrode, sodium ions have to repeatedly leave and re-enter a solid structure. Layered transition-metal oxides are attractive candidates because sodium can be reversibly extracted from and inserted into spaces between layers of metal and oxygen atoms. Compositions based on relatively abundant elements such as iron and manganese are particularly interesting ⁵.

Again, however, accommodating sodium is not enough. The host structure has to survive the process. A useful example is the layered oxide P2-Na₂/₃[Fe₁/₂Mn₁/₂]O₂, investigated by researchers from CIC energiGUNE and collaborators ⁶. The material can provide high capacity, but its long-term stability strongly depends on the voltage range over which it is operated.

This produces a familiar battery dilemma. Increasing the operating voltage can increase the amount of energy extracted from a material. But pushing the electrode further can also trigger structural transformations that make subsequent cycles less reversible.

The researchers compared cells operating in different voltage windows and combined electrochemical measurements with structural characterization and theoretical calculations. Above approximately 4.0 V versus Na+/Na, the cathode undergoes a high-voltage phase transition. Avoiding this transition improves capacity retention, revealing a direct connection between the structural state of the electrode and its long-term electrochemical behaviour.

Changing the chemical composition can also alter this structural response. In another study, researchers investigated the effect of introducing titanium into sodium manganese-rich layered oxides ⁷. A certain degree of Ti substitution helped the crystal structure accommodate electrochemically induced strain. Too little was insufficient to stabilize it, while further substitution introduced other destabilizing effects.

Other studies have approached the same problem from a different angle, examining how the precise stoichiometry of P2 layered oxides affects their performance ⁸, or following their structural evolution under different current rates and dopants using operando techniques ⁹. The lesson is that there is no single property called “a good sodium cathode”. Composition, atomic arrangement, voltage and cycling conditions are interconnected. Even an element that does not directly provide the stored charge can determine how well the electrode survives sodium repeatedly moving through its structure.

A different chemistry

Layered oxides are not the only possible positive electrodes. Sodium chemistry allows other structural families to be explored.

One particularly instructive example is NaFePO₄, the sodium analogue of the well-known lithium iron phosphate chemistry. Researchers involving CIC energiGUNE have used in situ X-ray diffraction to follow how NaFePO₄ changes during sodium insertion and extraction ¹⁰. They have also shown that the reaction mechanism itself depends on how rapidly the electrode is charged or discharged ¹¹.

This is a subtle but important point. We often describe an electrode material as if it had a fixed reaction mechanism. In practice, what happens inside it can depend on how the battery is operated. Computational work has gone further by investigating sodium diffusion in NaₓFePO₄ ¹². Combining experiments and modelling in this way makes it possible to connect what is observed at the battery level with the motion of ions through an atomic structure.

But even understanding both electrodes is not enough. Between them lies the electrolyte, and the sodium ion spends much of its journey there. It becomes surrounded by solvent molecules, travels across the cell and eventually reaches an electrode surface. At that interface the electrolyte can decompose, producing a solid-electrolyte interphase, or SEI.

This apparently thin layer can have a disproportionate influence on battery behaviour. Researchers at CIC energiGUNE have investigated both the composition and evolution of the SEI in sodium-ion electrodes ¹³ and, more broadly, the relationship between electrode-electrolyte interphases, transport properties and electrochemical performance in non-aqueous sodium-ion batteries ¹⁴.

The interface illustrates particularly well why changing lithium for sodium is not a simple substitution. The chemistry of the ion influences its solvation, its transport and the decomposition reactions that occur when it reaches an electrode. A material that can reversibly store sodium may still perform poorly if the interface around it continuously consumes electrolyte or sodium during cycling.

This is why sodium-ion research has to consider the cell as a system. Graphite may have to give way to hard carbon. Positive electrodes require structures able to tolerate repeated sodium insertion and extraction. Electrolytes and additives have to create suitable transport conditions and stable interfaces.

And every attempt to solve one of these problems can affect the others.

Sodium is not the new lithium

Lithium has a fundamental advantage that materials engineering cannot simply remove: it is the lightest metal and has a very low electrochemical potential. Sodium is heavier and its electrochemical potential is less favourable, placing intrinsic constraints on the gravimetric energy density that sodium-ion batteries can reach.

That does not necessarily make sodium a worse battery chemistry. Energy density is only one parameter of an electrochemical storage system. Cost, raw-material availability, lifetime, power, safety and operating temperature can become equally important depending on the application. A battery designed for a mobile phone, an electric vehicle or stationary storage does not necessarily have to optimize the same variables. And sodium chemistry itself offers several possible ways of balancing them.

CIC energiGUNE researchers have explored, for example, low-cost phosphate-based cathodes for aqueous sodium-ion batteries ¹⁵, Prussian Blue compounds as positive electrodes in aqueous systems ¹⁶, alternative sodium layered oxides [5,8,9], hard-carbon anodes [2,3,4] and even materials such as NaTiOPO₄ designed to modify or potentially avoid some of the interfacial problems normally associated with sodium-ion anodes ¹⁷.

These are not different routes towards exactly the same battery. They represent different ways of deciding which compromises are acceptable.

This changes the original question. The relevant challenge may not be how to make sodium behave as much like lithium as possible. Research on hard carbons shows that sodium may require a different type of host structure. Work on layered oxides shows that extracting more energy from an electrode can alter its structure and compromise its stability. Studies of NaFePO₄ show that the reaction pathway itself can depend on cycling conditions. And research on interfaces shows that storing sodium reversibly is only part of the problem: the chemistry taking place around the active material matters as well.

Sodium-ion batteries are therefore not lithium-ion batteries in which one element has simply been exchanged for another. Changing the ion changes the materials that host it, the structures they adopt, the reactions they undergo and the interfaces they form.

Sodium may not need to beat lithium at its own game. The more interesting question is whether understanding its different chemistry will allow us to play a different one.

 

The BRTA is a consortium that remains a step ahead of future socio-economic challenges worldwide and in the Basque Autonomous Community; it addresses them through research and technological development, thus projecting itself internationally. The BRTA centres collaborate to generate knowledge and transfer it to Basque society and industry so as to make them more innovative and competitive. The BRTA is an alliance of 17 R&D centres and cooperative research centres with the support of the Basque Government, the SPRI and the Chartered Provincial Councils of Araba, Bizkaia and Gipuzkoa.

 

The post Why can’t we simply replace lithium with sodium in batteries? appeared first on Mapping Ignorance.

 

 

About the author: José Luis Granados Mateo is a former secondary-school and sixth-form STEM teacher, having taught physics, chemistry and mathematics. He is currently a postdoctoral researcher in the epistemology of science at the University of the Basque Country (UPV/EHU). 

 

When I taught physics, chemistry and mathematics to secondary-school and sixth-form students, a correct answer was sometimes where the interesting part began. I would ask why they had used a particular equation. A solution that had looked perfectly sound could become much less convincing; occasionally the reverse happened, and a student whose algebra had gone wrong could explain the physics rather well. Nothing on the page had changed. My estimate of what the student understood had.

I have been thinking about those exchanges while reading the research on generative AI in education. Students can now submit a convincing explanation, solve a numerical problem or produce working code with considerable help from a language model. It is easy to see the achievement and much harder to work out what the student has learnt along the way.

That difficulty cannot be settled by appealing to the virtues of an unaided mind. Scientific work depends on instruments, notation, diagrams, software and other people. Delegating part of a calculation may be entirely sensible. Yet teaching has a complication that is easy to overlook from the perspective of an expert: the operation being delegated may be one of the things through which a beginner is meant to acquire expertise.

An experiment by Hamsa Bastani and colleagues brings this problem into unusually sharp focus.¹ Nearly a thousand pupils at a secondary school in Turkey took part in a study comparing two GPT-4 systems with a control condition without AI. GPT Base offered assistance resembling a general-purpose chatbot. GPT Tutor had additional material from teachers, including solutions and common mistakes, and instructions to guide pupils with hints rather than give away the answer.

During practice, both systems helped. The estimated scores were 48% higher than the control mean with GPT Base and 127% higher with GPT Tutor. These are relative differences, not percentage-point gains. On the tests that followed, however, the pupils had to work without assistance. The GPT Base group then scored an estimated 17% below the control mean. GPT Tutor largely mitigated that penalty, but its users showed no statistically significant advantage over the control group. The tests took place within the study sessions, so they tell us about immediate independent performance rather than retention months later.

Had the researchers stopped at the practice exercises, they would have had an encouraging result for both systems. They did not stop there, and the educational interpretation changed. A pupil working with AI had become more successful at the task. Whether that success belonged to the pupil in any lasting sense was another matter.

Psychologists have long distinguished performance during acquisition from learning: a relatively durable change in knowledge or skill. Soderstrom and Bjork review the evidence for that distinction, including cases in which fluent practice leads to disappointing retention and others in which slower, more error-prone practice produces better results later.² Watching someone learn, we inevitably see performance. To find out what remains, we need to look again after a delay, withdraw some of the support or ask for an application in a different context. Even then, what we have is evidence to interpret, not a direct view of understanding.

In STEM, much of the important work happens before the calculation. A mechanics problem requires a choice of system, a judgement about the relevant interactions and some representation of the situation. Chemistry involves moving between observable phenomena, molecular accounts and symbols on a page. A program that runs is the endpoint of decisions about what to separate, what to abstract and how to test it. Nersessian’s work on model-based reasoning gives representations a central place in scientific thought: constructing and revising a model can be part of arriving at an explanation, rather than merely a way of presenting one.³

A language model can help at almost any of these points. Perhaps the student has made the difficult modelling decisions and wants an algebraic check. Perhaps the model has chosen the principle, drawn up the representation, done the algebra and supplied an explanation before the student has formed a view of the problem. A questionnaire asking whether both students “used AI” would record the same answer.

There is an obvious temptation here to defend struggle. I am wary of it. Spending half an hour looking for a formula can be frustrating without being especially instructive, and a teacher who withholds every useful clue has not necessarily protected the student’s learning.

Worked examples offer a familiar counterexample. A novice studying a solution can attend to the structure of a procedure without having to search blindly for it. Barbieri and colleagues’ meta-analysis, covering 43 articles, 55 studies and 181 effect sizes, found a medium benefit for mathematics performance (g = 0.48).⁴ Removing some work can help a learner do other work more productively. The judgement lies in deciding what to remove.

Nor will the right decision remain right indefinitely. In a 2025 meta-analysis of the expertise reversal effect, Tetzlaff and colleagues brought together 60 experiments involving 5,924 participants and 176 effect sizes. Learners with little prior knowledge benefited from greater instructional assistance (d = 0.505); for those with high prior knowledge, lower-assistance conditions were preferable (d = −0.428).⁵ These are heterogeneous results across educational contexts, not estimates specifically for generative AI. Still, they help explain a familiar experience: an explanation that once made a procedure possible can later get in the way of carrying it out.

This is part of what Koedinger and colleagues call the assistance dilemma: when should instruction provide information, and when should it require the learner to generate it?⁶ With a conversational model, the practical difficulty is that help need never run out. Another prompt can produce another hint, or an answer, or a complete solution to a problem the student has scarcely begun to consider.

Pablo Rodríguez Sánchez raised a related question in Mapping Ignorance, in Why teach calculus in the age of AI. Software can compute many standard derivatives and integrals almost immediately, and AI interfaces make such tools easier to consult. That does change the case for teaching manual procedures. It does not dispose of it. Working through integration by parts can make its relation to the product rule visible; substitution can bring the chain rule into focus from the other direction. Some of the value of a procedure lies in the relationships a student encounters while carrying it out. Requiring the procedure without regard to those relationships would miss the point just as thoroughly as abandoning it because a machine is faster.

We already move parts of our thinking into the world. We write a number down so that we do not have to remember it, or set a reminder for something we expect to forget. Risko and Gilbert describe such actions as cognitive offloading: changing the processing demands of a task through physical action and external resources.⁷ Its consequences are worth studying, but the fact that someone has offloaded a task is not in itself evidence of cognitive decline.

For history and philosophy of science (HPS), this should be familiar ground. Giere and Moffatt describe scientific cognition as distributed across people and artefacts.⁸ On this account, instruments, representations and social arrangements can be parts of the system through which scientific knowledge is produced. A physicist who uses a computer to integrate a differential equation has not thereby revealed a failure to understand physics.

She does, though, need some grounds for trusting the result. She must know what calculation is appropriate, which assumptions it involves and when the output ought to make her suspicious. A beginner may still be acquiring those resources. If every mechanics problem arrives already represented and ready for calculation, there may be very little occasion to learn how to represent one.

“Which formula do I use?” was a question I heard often. Usually, in the cases I remember, the difficulty was not simply a forgotten equation. The student had not yet made sense of the situation in a way that gave any equation a purpose. Supplying the formula could rescue the exercise while leaving that difficulty untouched. A question about the forces, or about what was being treated as the system, could sometimes help much more.

The records from Bastani’s experiment are suggestive in this respect. GPT Base users more often requested answers, and the authors interpreted their interaction and error analyses as consistent with copying solutions. GPT Tutor elicited more attempted answers and requests for help. The surveys, administered at the end of the sessions, revealed a further complication. GPT Base users did not perceive the disadvantage visible in their unassisted scores; GPT Tutor users rated their performance more favourably even though no significant exam advantage had been detected.¹ Students’ impressions were not a dependable guide to what the assistance had done for them.

But the evidence does not all point towards disappointment. In an introductory physics course at Harvard, Kestin and colleagues compared a purpose-built AI tutor with an established active-learning class.⁹ Their randomised crossover study covered two lessons, with students experiencing both conditions. The tutor incorporated scaffolding, targeted feedback and self-pacing. Median post-test scores were 4.5 in the AI condition and 3.5 in the classroom condition. Measured against the pooled pre-test median, the gain in the AI condition was more than twice as large. Seventy per cent of students spent less than the 60 minutes allocated to classroom learning on the AI activity; the median was 49 minutes. They also reported greater engagement and motivation.

That is an interesting result on its own terms. It concerns particular content, a carefully prepared tutor and tests taken after the lessons. The intervention also combined several differences, including the setting, pace and prerecorded introductions, so the study cannot tell us how much of the benefit came from the language model alone. Nor does it establish long-term retention. Within those limits, it is good reason to take the design of AI-supported instruction seriously. Efficiency and learning need not be enemies.

Programming provides a less reassuring case. Bassner and colleagues reported a three-arm randomised trial at the Technical University of Munich, with 275 introductory programming students in the reported sample.¹⁰ During a 90-minute exercise, students used either the scaffolded tutor Iris, unrestricted ChatGPT or conventional web resources without AI. Both AI groups achieved higher scores on automated tests of their code and reported less frustration. Neither showed a statistically significant advantage in knowledge gains or code comprehension. Only Iris showed an increase in intrinsic motivation. There was clearer evidence of better task performance than of better understanding, although the absence of a significant learning difference does not establish that the groups learnt exactly the same amount.

Three studies cannot carry a general theory of AI in education. Their students, tasks and measurements are too different, and their time horizons too short. What they do make difficult to ignore is the need to decide whose competence an assessment is measuring. If a tool remains available throughout, successful performance may tell us a great deal about the person–tool combination and rather less about a change in the person.

The larger reviews do not make this much easier. Boolzen and colleagues’ 2026 review identified 85 eligible controlled studies of cognitive outcomes in STEM; 49 studies, yielding 59 effect sizes, entered the meta-analysis.¹¹ A conventional random-effects model gave a positive mean effect, alongside extraordinary heterogeneity (I² = 96.32%). Its prediction interval ran from g = −1.52 to g = 3.20. For comparable settings, the model therefore allows for effects extending from substantial harm to substantial benefit. It would be a mistake to read the positive average as a reliable forecast for a new classroom intervention.

The authors also found funnel-plot asymmetry, consistent with possible publication bias. Using a robust Bayesian adjustment, they obtained an overall estimate close to zero (μ = 0.076), with substantial uncertainty. The heterogeneity remained. Asymmetry need not be caused by publication bias, and the adjusted estimate depends on modelling assumptions; it is not a final answer about whether AI helps students learn.

Their moderator analysis is more helpful for thinking about instruction. They coded the learning outcome and the change in students’ activity relative to the control condition. The patterns favoured augmentation over substitution, but these were comparisons across studies, not randomised tests of those design choices. Much variation remained unexplained, and no combination of the examined moderators reliably supplied a sufficient condition for a large effect.¹¹ The distinction is promising as a research question. We do not yet have a rule for applying it to every lesson.

Wu and colleagues’ 2026 meta-analysis sounds more optimistic: 35 studies, 4,193 participants and a positive mean effect of g = 0.670, with a 95% confidence interval of 0.495–0.844.¹² The two reviews are not estimating quite the same thing. Wu’s synthesis ranges across disciplines and includes motivation, engagement and self-efficacy alongside cognitive outcomes. It also reports very high heterogeneity (I² = 91.444%). Its tests did not detect publication bias, which is different from establishing its absence. Most included studies concerned higher education.

I would hesitate to choose between those reviews on the strength of their averages. An answer-giving chatbot, a tutor that asks for explanations and a debugging assistant may use closely related models while organising the student’s activity very differently. Asking for “the effect of ChatGPT on learning” begins to sound like asking for the effect of books. We would immediately want to know which books, who was reading them and what they were being asked to do.

Meanwhile, students are getting on with using the tools. PISA 2025 cannot resolve the causal questions raised by the experiments, but it gives some sense of their scale. On average across OECD countries, 46% of 15-year-olds reported using AI chatbots at least weekly to help them learn. In Spain, the figure was 54%. The Spanish percentages reported for preliminary research, summarising assigned texts and drafting written work were 42%, 40% and 33%, respectively. Only 8% reported never or almost never using AI for any of the schoolwork purposes examined.^13,14

The achievement patterns deserve attention, though they are easily overread. After adjustment for students’ socioeconomic profile, the OECD-average results generally favoured non-users over those reporting AI use for specific tasks such as drafting or summarising. Weekly use for the broader purpose of helping with learning was associated with roughly similar science performance to non-use. For some specific tasks, moderate users did better than very frequent users.¹³

A pupil who is struggling may seek more help. Schools may encourage AI use in response to difficulties that already exist. And frequency tells us very little about the encounter itself: requesting an answer, interrogating a hint and challenging an explanation can all count as using a chatbot. Those possibilities are not findings that explain away PISA’s associations; they are reasons why the associations cannot establish what AI caused.

PISA also examines opportunities to evaluate AI-generated information at school. Some descriptive patterns look favourable, but the overall positive association with that activity is not observed, on average, after adjustment for students’ and schools’ socioeconomic profiles.¹³ The report gives us no simple causal claim about the benefits of teaching students to check AI output.

There is nevertheless an educational question here that deserves more than a slogan. Mapping Ignorance has discussed it in Critical thinking for the unstoppable use of artificial intelligence. In STEM, asking a student to check an answer presupposes that something is available with which to check it. Does the student know what order of magnitude to expect? Which assumption matters? What evidence would bear on the explanation? A fluent paragraph can be physically impossible, but fluency alone will not tell a beginner where it goes wrong.

It is also worth keeping the chronology straight. Generative AI cannot be the sole explanation for declines in educational achievement that began before ChatGPT was publicly available. What PISA establishes more usefully here is that the practices under discussion are already part of ordinary schoolwork. Teachers have to make decisions while the evidence is still developing.

Some of those decisions will be quite small. A worked solution may be exactly what a pupil needs on Monday. A few weeks later, providing the whole solution may remove an opportunity to reconstruct the method. AI could make it easier to vary the support: ask for a representation before proposing an equation, invite a prediction before showing a simulation, or offer a flawed argument for the student to diagnose. These are plausible uses, not capabilities we should assume a system can deliver reliably simply because it holds a convincing conversation.

Assessment poses a related problem. Students will need to work competently with tools, as scientists and engineers do, and that competence deserves to be assessed. But if we also want to know whether they can perform a particular operation themselves, there must be an occasion when the tool is not doing it for them. We have to say what belongs in the learner’s own repertoire, and then find an assessment that gives us reasonable evidence of it.

For someone working in History and Philosophy of Science, the interest extends beyond the ownership of a correct answer. Scientific competence involves judgement about models, evidence, instruments and the reach of a procedure. A student may know a formula and still not know when it applies. An experienced scientist may quite reasonably delegate its numerical solution while retaining responsibility for the assumptions and interpretation. Learning to make those distinctions is part of becoming competent.

That brings me back to the classroom question. When I asked why a student had used an equation, I wanted access to the reasoning behind the calculation. With generative AI, the visible work may belong to a temporary partnership, and locating that reasoning becomes harder. The partnership itself need not be objectionable. What matters educationally is what repeated participation in it does to the student.

Over time, can the learner represent a problem more confidently, choose a method, recognise an implausible result or decide when help is needed? If so, the tool may be contributing to the acquisition of expertise. If those capacities scarcely change while the submitted answers become more polished, the same outward success has a different meaning.

I do not think the most useful question is whether students should use AI in STEM. It is what they should still be learning to do, how the assistance bears on that learning, and when it ought to recede. A correct answer was never a guarantee of understanding. We now have to be more deliberate about finding out what lies behind it.

 

 

 

References

1. Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö. & Mariman, R. (2025). ‘Generative AI without guardrails can harm learning: Evidence from high school mathematics’. Proceedings of the National Academy of Sciences, 122(26), e2422633122. 10.1073/pnas.2422633122
2. Soderstrom, N. C. & Bjork, R. A. (2015). ‘Learning versus performance: An integrative review’. Perspectives on Psychological Science, 10(2), 176–199. 10.1177/1745691615569000
3. Nersessian, N. J. (2002). ‘The cognitive basis of model-based reasoning in science’. In P. Carruthers, S. Stich & M. Siegal (eds), The Cognitive Basis of Science, pp. 133–153. Cambridge University Press. 10.1017/CBO9780511613517.008
4. Barbieri, C. A., Miller-Cotto, D., Clerjuste, S. N. & Chawla, K. (2023). ‘A meta-analysis of the worked examples effect on mathematics performance’. Educational Psychology Review, 35, Article 11. 10.1007/s10648-023-09745-1
5. Tetzlaff, L., Simonsmeier, B., Peters, T. & Brod, G. (2025). ‘A cornerstone of adaptivity – A meta-analysis of the expertise reversal effect’. Learning and Instruction, 98, 102142. 10.1016/j.learninstruc.2025.102142
6. Koedinger, K. R., Pavlik, P. I. Jr., McLaren, B. M. & Aleven, V. (2008). ‘Is it better to give than to receive? The assistance dilemma as a fundamental unsolved problem in the cognitive science of learning and instruction’. In B. C. Love, K. McRae & V. M. Sloutsky (eds), Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 2155–2160. Full text
7. Risko, E. F. & Gilbert, S. J. (2016). ‘Cognitive offloading’. Trends in Cognitive Sciences, 20(9), 676–688. 10.1016/j.tics.2016.07.002
8. Giere, R. N. & Moffatt, B. (2003). ‘Distributed cognition: Where the cognitive and the social merge’. Social Studies of Science, 33(2), 301–310. 10.1177/03063127030332017
9. Kestin, G., Miller, K., Klales, A., Milbourne, T. & Ponti, G. (2025). ‘AI tutoring outperforms in-class active learning: An RCT introducing a novel research-based design in an authentic educational setting’. Scientific Reports, 15, 17458. 10.1038/s41598-025-97652-6
10. Bassner, P., Lenk-Ostendorf, B., Beinstingel, R., Wasner, T. & Krusche, S. (2026). ‘Less stress, better scores, same learning: The dissociation of performance and learning in AI-supported programming education’. Computers and Education: Artificial Intelligence, 10, 100537. 10.1016/j.caeai.2025.100537
11. Boolzen, C., Kuhn, J., Flegr, S., Rott, E.-M., Stausberg, N. & Küchemann, S. (2026). ‘Evidence of impact and interpretational limits of generative AI in STEM education: a systematic review and meta-analysis on cognitive learning outcomes’. Artificial Intelligence Review. Advance online publication. 10.1007/s10462-026-11665-9
12. Wu, X., Zhu, P., Zhang, J., Yin, M. & Wang, Y. (2026). ‘ChatGPT’s impact on student learning outcomes: a meta-analysis of 35 experimental studies’. Humanities and Social Sciences Communications, 13, 684. 10.1057/s41599-026-07019-z
13. OECD (2026). PISA 2025 Results (Volume I): Future-Ready Students. Paris: OECD Publishing. 10.1787/73451bc5-en
14. OECD (2026). PISA 2025 Results (Volume I): Spain. Country note. OECD. Country note

 

The post AI, problem solving, and the future of STEM education appeared first on Mapping Ignorance.

Author: José R. Pineda got his Ph.D. from University of Barcelona in 2006. Since 2007 he has worked for Institut Curie and The French Alternative Energies and Atomic Energy Commission. Currently he is a researcher of the EHU. He investigates the role of stem cells in physiologic and pathologic conditions.

 

One of the ideas most of us learn early in biology is that only a few specialized cells can communicate through electrical signals. Neurons do it. Muscle cells do it. Epithelial cells, the cells that form our skin and line most organs, supposedly do not. However, a recent study by Yu and Granick demonstrates that this picture may not be entirely correct. In fact, these seemingly ordinary cells appear capable of generating electrical spikes of their own when tissue integrity is disturbed. Despite  epithelial cells have traditionally been viewed as passive structures whose main functions are to form protective barriers, absorb nutrients, and maintain tissue integrity, Yu and Granick challenges this long-standing idea in a striking way. Their work demonstrates that epithelial cells can generate electrical spikes resembling action potentials, revealing a previously unrecognized form of bioelectrical communication that may help coordinate tissue responses after injury ¹.

The finding is surprising because epithelia are among the most abundant tissues in the body. They line the skin, digestive tract, airways, and many internal organs. Although these tissues are known to participate in wound healing and cellular communication, they were not believed to possess the kind of rapid electrical activity usually associated with the nervous system. The new study suggests that this assumption may have been incomplete. Under certain conditions, epithelial cells appear capable of producing coordinated electrical signals that spread across remarkably large distances. To investigate this phenomenon, the researchers employed two different epithelial models: the first model consisted of primary human skin keratinocytes (the major cellular component of the epidermis). The second model used Madin-Darby Canine Kidney (MDCK) cells (a classical epithelial cell line frequently employed in cell biology research). Both cell types were grown until they formed dense, confluent monolayers resembling miniature sheets of epithelial tissue.

The key experimental tool was a multielectrode array, often abbreviated as “MEA”. This technology can be imagined as a highly sensitive electronic “listening device” containing dozens of microscopic electrodes embedded in a culture surface. When cells generate electrical activity, even extremely small voltage fluctuations can be detected simultaneously at multiple positions. Multielectrode arrays are routinely used to study neurons, but their application to epithelial tissues is relatively uncommon. After establishing epithelial monolayers on these arrays, the researchers created a small and highly localized injury using a focused laser beam. The laser produced controlled damage in a tiny region at the center of the culture while leaving surrounding cells intact. This experimental design allowed the team to observe how cells located at various distances reacted to the injury. Electrical signals were then monitored continuously for one hour. What they observed was unexpected: within the epithelial sheets, electrical spikes appeared shortly after injury and persisted for more than sixty minutes!! These spikes displayed a characteristic sequence of depolarization, repolarization, and hyperpolarization, terms commonly used to describe action potentials in neurons. However, there was one major difference, while neuronal action potentials last a few milliseconds, the epithelial spikes unfolded over one to two seconds, making them roughly one thousand times slower. Despite their slower kinetics, the electrical signals were surprisingly robust, measurements showed that spikes could be detected as far as 740 micrometers from the injury site, a distance corresponding to dozens of cell diameters (Figure 1). Even more remarkably, their amplitude did not progressively decrease with distance as one would expect from a simple wave spreading outward from a central source. In some cases, signals recorded farther away were as large as, or even larger than, those detected closer to the wound. This observation suggested that distant epithelial cells were not merely receiving a weakened signal from injured neighbors. Instead, they may have been generating their own electrical responses as part of a collective tissue-wide process.

 

Discovering the spikes was only part of the story. The obvious next question was what actually produced them. Were epithelial cells using the same electrical machinery found in neurons, or was something entirely different at work? To answer this, the researchers began dissecting the molecular mechanisms one by one. They began by testing the role of calcium (one of the most important intracellular messengers in biology). Calcium ions regulate a wide range of cellular activities, including contraction, migration, secretion, and gene expression. To determine whether calcium was necessary for epithelial spiking, the researchers added EDTA (a molecule that binds calcium and reduces its availability). Surprisingly, once calcium availability was reduced, the electrical activity virtually vanished!! This immediately suggested that calcium was not just associated with the phenomenon but was an essential part of it. Next, the team examined the contribution of cellular mechanics, because it is known that epithelial cells continuously generate mechanical forces through interactions between actin filaments and myosin motor proteins, a system known as “the actomyosin network”. This machinery allows cells to contract, change shape, and exert forces on neighboring cells. To disrupt this process, the researchers treated cultures with blebbistatin (a selective inhibitor of myosin II activity). Once again, electrical spiking was largely abolished. The similarity between the effects of calcium depletion and myosin inhibition suggested that electrical activity was intimately linked to the mechanical state of the tissue. This observation raised an intriguing possibility: perhaps epithelial electrical signaling is not driven by voltage-gated ion channels (as in neurons), but instead by channels that respond to mechanical forces. To test this hypothesis, the investigators focused on mechanosensitive ion channels, specialized proteins embedded within cell membranes that open when cells are stretched, compressed, or subjected to mechanical stress. Using the inhibitor GsMTx4 (which blocks several mechanosensitive channels including Piezo1), the researchers observed a partial reduction in electrical activity. While spiking was not completely eliminated, both amplitude and coordination decreased substantially. This result implicated mechanosensitive channels as key components of the signaling pathway. To really prove their implication, next the researchers activated them directly using specific pharmacological compounds. One molecule, GSK1016790A, activates the TRPV4 channel. Another, Yoda-1, specifically activates Piezo1. Remarkably, activating these channels was sufficient to generate large electrical spikes even in the absence of any injury. In other words, simply stimulating mechanosensitive channels could reproduce the electrical behavior observed after tissue damage. This provided strong evidence that mechanical sensing lies at the heart of epithelial electrical excitability.

 

Based on their findings, the authors proposed a positive feedback mechanism: 1) mechanical stress activates mechanosensitive ion channels, allowing calcium to enter the cells. 2) rising calcium levels stimulate actomyosin contraction, generating additional mechanical stress. 3) this new stress then activates more mechanosensitive channels, sustaining the cycle. Such a feedback loop could explain why spiking persists for long periods and why cells distant from the wound remain electrically active. One particularly interesting aspect of the study is its potential relevance to wound healing. Successful tissue repair requires large numbers of cells to coordinate their behavior across considerable distances. Cells must migrate, proliferate, and remodel tissue structures in a synchronized manner. Traditional explanations have emphasized biochemical signaling molecules and calcium waves. The electrical spikes described here introduce an additional layer of communication that may operate on intermediate spatial and temporal scales. According to the authors, these signals could complement chemical and mechanical communication systems, helping tissues organize collective responses after injury. However, the researchers are careful to acknowledge the study’s limitations. Their experiments were performed in cultured epithelial monolayers rather than living organisms. Although the results clearly demonstrate intrinsic electrical excitability in epithelial cells, it remains unknown whether the same phenomenon occurs in intact tissues during natural wound healing. Furthermore, the exact relationship between these electrical spikes and cellular behavior has not yet been determined. It is still unclear whether the spikes actively drive tissue repair or simply accompany mechanical disruption. Additional work involving animal models, voltage-sensitive imaging, and targeted genetic manipulation of mechanosensitive channels will be required to answer these questions.

 

In conclusion, the study by Yu and Granick reminds us that biology still contains unexpected surprises, even in tissues that have been studied for decades. A simple layer of epithelial cells, once considered electrically silent, may in fact possess its own form of excitability. Rather than transmitting information through millisecond action potentials like neurons, epithelial tissues appear to communicate through slower, mechanically driven electrical spikes capable of coordinating activity across hundreds of micrometers. Whether these signals prove to be a central component of tissue physiology remains to be seen. What is already clear, however, is that the electrical life of cells extends far beyond the nervous system!!

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Authors: Mary M. Hausfeld, Assistant Professor in Management, University of Limerick and Jochen Menges, Director of the Center for Leadership in the Future of Work, University of Zurich

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Authors: Eider Fernandez & Igor Baroja-Careaga, Esneki Zentroa (Leartiker S.COOP)

 

Probiotics are part of a rapidly expanding field of research. They are live microorganisms that, when administered in adequate amounts, may confer health benefits[1]. Among the most extensively studied probiotics, some species of Lactobacillus and Bifidobacterium have been associated with the maintenance of gut microbiota balance and with several physiological functions[2]. Producing these microorganisms usually requires nutrient-rich culture media. But what if such bacteria could be obtained from a food by-product nowadays regarded as waste? This question formed the starting point of a study carried out by Esneki Dairy Centre – Leartiker to assess the potential of cheese whey as a substrate for producing microbial biomass enriched in microorganisms with probiotic potential.

The challenge of cheese whey

Cheesemaking inevitably generates substantial amounts of whey: a yellowish liquid released from the curd that still carries lactose, soluble proteins, minerals and other valuable compounds. In fact, whey can represent around 85% of the original milk volume used in cheese production[3].

Despite this nutritional richness, whey remains a difficult stream to manage for many artisanal dairies. This is particularly true in the Basque Country, where many cheesemaking farms are small, family-run businesses located in rural areas[4]. Its highly perishable nature means that whey must be preserved or processed soon after production, making storage, collection and transport especially challenging when volumes are small and geographically dispersed.

Moreover, uncontrolled whey discharge results in a high organic load that can affect aquatic ecosystems. Paradoxically, the same nutrients that make cheese whey a potential pollutant are also what give it considerable biotechnological value.

A natural culture medium

From a microbiological standpoint, cheese whey is not merely a residual stream, but a naturally nutrient-rich matrix[5]. Its lactose content, together with soluble proteins, minerals and other available compounds, provides a favourable environment for the growth of lactic acid bacteria, widely used microorganisms in fermented foods and increasingly explored for their probiotic potential.

The working hypothesis was simple: by steering whey fermentation under controlled conditions, it could be possible to recover a biomass enriched in microorganisms of interest, while at the same time increasing the stability and value of the by-product itself.

To explore this possibility, several fermentation strategies were tested using whey generated during the production of blue cheese from pasteurised cow’s milk. The study compared three different approaches:

• Fermentation of previously pasteurised whey (Protocol 1).
• Fermentation of whey after a deproteinisation step (Protocol 2).
• Direct fermentation of raw whey, without any prior treatment (Protocol 3).

Two different blends of commercial microorganisms, inspired by the naturally diverse microbial communities found in kefir, were also evaluated. These included; Choozit Kefir DV LYO 1000L (Danisco) (starter culture A) and Ferlac Kefir Type C (Abiasa) (starter culture B). Additionally, unfermented whey (that is, whey to which no starter cultures were added and which was not subjected to fermentation) from each protocol was analysed as a control sample. Controlled fermentation processes were carried out under monitored conditions. Throughout the fermentation, key parameters were monitored, including temperature (25 ± 1 °C), incubation time (16 ± 1 h for starter culture A and 21 ± 1 h for starter culture B) and pH evolution, with fermentation being considered complete once the pH fell below 4,6. These measurements allowed the identification of the conditions that most effectively supported the growth and enrichment of the target microbial populations.

 

Exploring the microbiome through metagenomics

However, assessing the success of a fermentation process requires more than simply determining how many microorganisms grow. It is also essential to identify which species are present and how the microbial community changes over time.

Traditional culture-based microbiological methods remain fundamental tools for quantifying and isolating viable microorganisms. Yet they have important limitations when complex microbial communities need to be characterised, as they only detect microorganisms able to grow under the selected culture conditions. As a result, part of the microbial diversity present in the sample may remain undetected. To obtain a more comprehensive view of the microbial community, the study incorporated high-throughput amplicon sequencing, an approach that enables the direct analysis of DNA present in a sample and the identification of microorganisms regardless of their ability to grow under laboratory conditions.

Bacterial communities were characterised by amplifying the V4 region of the 16S rRNA gene, one of the most widely used markers in bacterial diversity studies. Fungal and yeast communities were analysed through the ITS region — the Internal Transcribed Spacer — which is considered the standard marker for fungal diversity studies.

Sequencing was performed using Illumina technology, generating approximately 100,000 reads per sample for bacteria and around 40,000 reads for fungi and yeasts. These sequences were then compared against specialised databases to determine the identity of the microorganisms present and estimate their relative abundance.

Before interpreting the results, sequencing depth was assessed using rarefaction curves. These curves showed a clear trend towards saturation, indicating that most of the microbial diversity present in the samples had been adequately captured.

Which protocol worked best?

High-throughput amplicon sequencing revealed clear differences among the fermentation strategies tested.

Fermentations carried out using raw whey (Protocol 3) maintained a more diverse bacterial community than those in which the whey had first been subjected to thermal treatment (Protocol 1 and Protocol 2), as can be seen in Figure 1, as can be seen in Figure 1, the number of observed microbial taxa increases with sequencing depth. This suggests that the partial preservation of the native microbiota originating from the cheesemaking process may play an important role in shaping the fermentation outcome.

 

 

 

In contrast, the diversity of moulds and yeasts decreased. This trend may be related to the origin of the whey, which was obtained from blue cheese production, where specific mould (Penicillium roquefoti) species are deliberately enriched during manufacture. The competitive advantage of these dominant fungi appears to favour the development of a more specialised microbial community, reducing the abundance and diversity of other microbial populations present in the whey (Figure 2).

 

 

Principal coordinates analysis (PCoA) based on Jaccard distances (Figure 3) showed a clear separation among the different fermentation protocols, indicating that each strategy gave rise to distinct microbial communities. In parallel, the relative abundance of profiles (Figure 4) made it possible to visualise how particular species increased or decreased depending on the conditions applied.

 

 

 

 

Amplicon sequencing also enabled the identification of the dominant microorganisms during the process. In most samples, Lactococcus lactis was particularly abundant. This result was expected, as this bacterium was included in the commercial starts cultures used and is widely employed in dairy fermentations. However, some fermentation conditions, particularly those involving starter culture B, also favoured the development of species such as Lacticaseibacillus rhamnosus and Bifidobacterium animalis, wich were also present in the commercial starter culture. These microorganisms are frequently associated with probiotic applications[6]^,[7]^,[8].

To identify the most suitable protocol for producing a fermented whey enriched in microorganisms with probiotic potential, both microbial abundance (including aerobic mesophilic bacteria, yeasts and moulds) and the diversity of the resulting microbial communities were evaluated. The highest microbial counts were obtained in whey samples produced using Protocol 3, which was based on raw whey, and fermented with starter culture B. (Average counts of 8.2 log CFU/mL were obtained with starter culture A, and average counts of 8.5 log CFU/mL with starter culture B).

Furthermore, as mentioned previously, the highest levels of microbial diversity were observed in the samples produced using Protocol 3, where fermentation was carried out directly on raw whey without prior heat treatment.

 

Overall, the results pointed to the most effective strategy also being the simplest from a technological perspective: the direct fermentation of raw whey, without prior thermal treatment using starter culture B. This approach maintained high microbial diversity while reducing energy requirements and simplifying the overall process.

An opportunity for the circular economy

Beyond the microbiological results, the broader relevance of this work lies in showing that the role of whey within the cheesemaking chain can be reconsidered.

Rather than being treated as a waste stream associated with management costs, whey can be viewed as a raw material capable of supporting valuable biotechnological processes. Controlled fermentation not only helps stabilise whey but also makes it possible to harness the nutrients it still contains to generate added-value microbial biomass.

Such strategies are closely aligned with the principles of the circular bioeconomy, in which by-products are no longer seen as the end of a production chain, but as the starting point for another. In a context where sustainability and efficient resource use are increasingly important, initiatives of this kind show how microbiology and omics technologies can help turn an environmental challenge into an opportunity for innovation.

 

The PRO2O project (grant agreement No. 00003-BIO2022-48) was funded through the 2022 Bioeconomy Innovation Projects funding programme of the Basque Government’s Department of Economic Development, Sustainability and Environment.

References

[1] Guarner F, Requena T, Marcos A. Consensus statements from the Workshop “Probiotics and Health: Scientific evidence”. Nutr Hosp. 2010 Sep-Oct;25(5):700-4. PMID: 21336423.

[2] Martin, A. J. M., Serebrinsky-Duek, K., Riquelme, E., Saa, P., & Garrido, D. (2023). Microbial interactions and the homeostasis of the gut microbiome: the role of Bifidobacterium. Microbiome Research Reports, 2. https://doi.org/10.20517/mrr.2023.10

[3] González Siso, M. I. (1996). The biotechnological utilization of cheese whey: A review. Bioresource Technology, 57(1), 1–11. https://doi.org/10.1016/0960-8524(96)00036-3

[4] Esneki Zentroa – Leartiker (2022). “El sector quesero en Euskadi 2021”. Available at: El-sector-quesero-en-Euskadi.pdf

[5] Figueroa Pires, A., garcía Marnotes, N., Díaz Rubio, O., Cobos García, A., Dias Pereira, C. (2021). Dairy by-products: A review on the valorization of whey and second cheese whey. Foods, 10: 1067

[6]Neethu, M., Bunt, C., Hussain, M., 2015. Comparison of Microbiological and Probiotic Characteristics of Lactobacilli Isolates from Daity FOod Products and Animal Rumen Contens Microorganims 3, 198-212.

[7] Rama, G.R., Kuhn, D., Beux, S., aciel, M.J., Volken de Souz, C.F., 2019. Potential apllications of dairy why for the production of lactic acid bacteira cultures. Int Dairy J.

[8] Rodriguez-Palacios, 1., Staemplfli, H.R., Duffield, T., Weese, J.S., 009. Isolation of bovine intestinal Lactobacillus plantarum and Pediococcus acidilactici with inhibitory activity against Escherichia coli O157 and F5. J. Appl. Mcrobiol. 106, 393-401

 

The BRTA is a consortium that remains a step ahead of future socio-economic challenges worldwide and in the Basque Autonomous Community; it addresses them through research and technological development, thus projecting itself internationally. The BRTA centres collaborate to generate knowledge and transfer it to Basque society and industry so as to make them more innovative and competitive. The BRTA is an alliance of 17 R&D centres and cooperative research centres with the support of the Basque Government, the SPRI and the Chartered Provincial Councils of Araba, Bizkaia and Gipuzkoa.

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Author: Deven Desai, Professor of Business Law and Ethics; Associate Director for Law, Policy, and Ethics at the Machine Learning Center, Georgia Institute of Technology

The post The ‘WarGames’ problem: AI agents don’t go rogue appeared first on Mapping Ignorance.

Author: José Luis Granados Mateo is a postdoctoral researcher in the Department of Philosophy at the University of the Basque Country (EHU) and a member of the Integrated History and Philosophy of Science (iHPS) research group. His work focuses on history and philosophy of science, science and values, and the epistemology of scientific practices.

 

Water is H₂O. Few scientific formulas look more familiar, more compact, or more final. Two atoms of hydrogen, one of oxygen: the phrase seems to say what water really is beneath the shifting appearances of ice, vapour, rain, rivers and the liquid in a glass.

Yet that little subscript was not easy to earn.

By the early nineteenth century, chemists had good reasons to treat water as a compound of hydrogen and oxygen. The old idea of water as a simple element had lost its authority, as we saw in Is Water H₂O? (1) From Element to Compound. Electrolysis then gave that claim a new experimental force, making hydrogen and oxygen appear under electrical action, although not without raising puzzles of its own, as discussed in Is Water H₂O? (2) The Puzzle of Water Electrolysis. What remained unsettled was a more delicate question. Not whether water had constituents, but how many of each entered one molecule.

Was water HO? H₂O? Something else?

Today the answer feels almost automatic. In the first decades of atomic chemistry, it was not. Dalton’s atomic theory made HO the natural formula: one atom of hydrogen, one atom of oxygen. Avogadro proposed something much closer to the modern answer very early, but his proposal did not settle the matter. For more than half a century, some of the best chemists in Europe disagreed over the formula of one of the most ordinary substances in the world.

The confusion was public enough to become comic. In the 1860s, when agreement was finally beginning to form, an author identifying himself as a certified lunatic at Hanwell Asylum wrote to Chemical News mocking the chemists’ inability to agree even about water. One could contemplate the bewildering variety of formulas, he suggested, until the brain first became confused, then swam, and finally softened.

The joke had bite because the embarrassment was real. Chemical tables were full of combining weights, volumes, radicals, equivalents and atomic symbols. Water had long ceased to be elementary. Yet the old difficulty remained: to know the formula, chemists had to count atoms; to count atoms, they would have had to see them. And they could not.

Liebig put the matter painfully in 1851. Chemists possessed no direct means of ascertaining the number of atoms even in the simplest compound, because that would require seeing and counting them. Atomic chemistry needed numbers for entities no one could inspect.

So the problem was not that the answer lay beyond all reach. It lay beyond direct reach. Chemists had to learn to count invisible things by making them answer to operations that could be performed.

 

A number, and two worlds

The difficulty begins with a ratio.

Hydrogen and oxygen combine in fixed proportions by weight. In round modern numbers, water contains one part hydrogen for eight parts oxygen. Chemists could establish the ratio; the difficulty began when that ratio had to be translated into atoms.

Suppose water is HO. One atom of hydrogen combines with one atom of oxygen. Since the oxygen contribution weighs eight times as much as the hydrogen contribution, the oxygen atom must weigh eight times as much as the hydrogen atom.

Now suppose water is H₂O. Two atoms of hydrogen combine with one atom of oxygen. The same measured ratio now yields a different atomic weight: oxygen must weigh sixteen times as much as hydrogen.

The laboratory ratio has not changed. The invisible architecture has.

That was the circular trap. Molecular formulas required atomic weights; atomic weights required molecular formulas. Measurement gave combining proportions, not little labelled particles waiting to be counted. A consistent chemistry could be built with HO and oxygen as 8. Another could be built with H₂O and oxygen as 16. Once the difficulty spread across many compounds, chemists were no longer facing a local uncertainty about water. They were choosing among possible atomic worlds.

The difficulty appeared at the worst possible place: the point where atomic chemistry had to begin producing reliable numbers.

 

Dalton’s necessary guess

Dalton knew that atomic theory needed a starting point.

He approached water from within a broader problem. Chemical substances combined in fixed proportions; in some cases, the same elements combined again in different but still simple ratios. Atomic theory offered a machinery for these regularities. If matter consisted of particles with definite weights, then chemical combination could be understood as their joining in determinate numbers, and the arithmetic of chemistry began to point towards an unseen order. What it still failed to provide was the count itself: the number of atoms entering each compound.

Dalton therefore introduced rules of simplicity. They were not decorative assumptions added to an otherwise complete theory. They were what allowed the theory to produce numbers at all. When only one compound of two elements was known, Dalton assumed the simplest possible atomic constitution: one atom of each. Since water was the known compound of hydrogen and oxygen, its formula became HO. Hydrogen could be assigned atomic weight 1, oxygen 8, and the system could begin to move.

Judged from later chemistry, Dalton’s rule has an air of innocence. It was a methodological expedient rather than an experimental result, a way of allowing atomic theory to proceed when direct counting was unavailable. Dalton was not looking into water molecules and counting badly; he was working with weights, tables and regularities, in a period before spectroscopy, quantum theory or a settled molecular chemistry had made atoms answerable in other ways.

HO was wrong. It was not random. It belonged to a method that could bring order to combining weights before chemistry had better ways of counting atoms.

 

What gas volumes seemed to promise

Another route opened through gases.

Volumes could be measured, and gases combined with a striking regularity. Gay-Lussac showed that gases entered reactions in simple volume ratios. Two volumes of hydrogen combine with one volume of oxygen to form water vapour; other gases display similarly neat relations. To anyone looking for a way to count invisible particles, this was tempting.

If equal volumes of gases contained equal numbers of particles, volume could become a counting device. It would not show atoms individually, but it might give chemists a way to infer how many particles were involved.

Avogadro made that move. Equal volumes of gases, under the same conditions, contain equal numbers of molecules. Grant that, and the formation of water vapour changes shape. Two volumes of hydrogen and one volume of oxygen produce two volumes of water vapour. The arithmetic works if hydrogen and oxygen gases are themselves molecular, H₂ and O₂, and if water is H₂O:

2H₂ + O₂ → 2H₂O

There it is, almost modern chemistry.

Yet the hypothesis did not conquer the field. The reasons were not merely psychological, nor reducible to conservatism. Avogadro solved one problem while creating others that looked serious at the time. Dalton saw no obvious reason why atoms of the same element should join into stable pairs. In his picture, atoms were surrounded by repulsive atmospheres of caloric; piling like atoms together was hardly the natural assumption. For electrochemical dualists, the difficulty sharpened further. Like atoms ought to have like electrical characters, and like electrical characters ought to repel. Why should oxygen atoms pair with oxygen atoms? Why should hydrogen atoms pair with hydrogen atoms? Why stop at pairs?

The hypothesis also asked chemists to treat elementary gases as molecules containing several atoms of the same element, and to imagine those molecules splitting during chemical combination. To those who trusted weights more readily than invisible gas particles, this was a large demand.

Even sympathetic chemists found the route uneven. Dumas tried, for a time, to develop Avogadro’s approach, but vapour-density results did not behave with perfect neatness. Mercury, phosphorus and sulphur made it hard to treat every elementary vapour on the model of hydrogen or oxygen.

Avogadro had offered a powerful clue. It was not yet the common language of chemistry. Water could have been H₂O in 1811; historically, that was not enough.

 

Atoms with handles

Behind the disagreement over water lay a larger problem. Nineteenth-century chemists were learning how to handle atoms at all: as weights, as volumes, as equivalents, as electrical units, as radicals, as centres of substitution.

For some, atoms were bearers of weight. For others, units in an electrochemical opposition. In Avogadro’s programme, gases offered a way of counting particles by volume. In organic chemistry, atoms and radicals became things that could be moved, exchanged and followed through reactions. Later structural chemistry would give them positions and patterns of connection.

Atoms entered chemistry through these practices unevenly: as things to be weighed, counted, exchanged, classified or connected.

A formula could be useful in one setting and awkward in another. Atomic weights could seem natural when derived from one class of operations and arbitrary when forced into another. Equivalents worked well for neutralisation and replacement. Volumes worked well for gases, though not without trouble. Substitution worked powerfully in organic chemistry. Electrochemical dualism arranged substances by polarity and affinity.

No single practice simply revealed atoms as they were. Each gave atoms a handle.

That is why the history of H₂O is not just a story about choosing the correct formula. The formula became convincing when atoms could be made to enter operations: weighed in one context, counted in another, substituted in a third, arranged elsewhere by their capacities for connection. Seeing was unavailable; work had to do the job of sight.

 

The caution of equivalents

One response to the circular trap was to avoid stepping fully into it.

Many chemists were cautious about atoms. Atomic language sounded metaphysical, or at least more speculative than the discipline required. Equivalent weights appeared safer. One substance could neutralise, replace or combine with another in a definite proportion, and those relations could be measured without deciding what atoms really were or how many of them sat inside a molecule.

Equivalents allowed calculation while holding invisible architecture at arm’s length. A chemist could say how much of one substance answered to another in reaction, without committing to a microscopic count.

This caution was productive. It helped chemists calculate, compare and classify. It also preserved ambiguity. In practice, an equivalent could behave rather like an atomic weight, even when chemists insisted that they were avoiding speculative atomism.

Water once again exposed the difficulty. If oxygen’s equivalent was 8 relative to hydrogen, HO looked natural. If oxygen’s atomic weight was 16, H₂O became natural. The issue depended on the passage from observable combining relations to invisible atomic units.

The appeal to equivalents allowed chemists to postpone bolder claims about atoms, while leaving a theoretical judgment embedded in the passage from measurable reactions to invisible units.

 

The detour through organic chemistry

The decisive work did not come from staring harder at water.

That is one of the surprises in this history. H₂O gained its strength elsewhere: in the movement of radicals, in substitutions, in families of organic compounds. Organic chemistry gave chemists a way to manipulate groups of atoms and to follow their replacements through reactions.

The key idea was the water type.

If water is H₂O, oxygen can be treated as holding two units together. In water, the two units are hydrogens. In alcohols and ethers, one or both hydrogens can be replaced by organic radicals. Water, alcohols and ethers could therefore be seen as members of a family, built around oxygen’s capacity to connect two branches.

At first this sounds like a tidy diagram. It became much more than that.

Williamson’s work on etherification gave the water type experimental substance. The older Liebig-Dumas view treated etherification largely as a removal of water from alcohol under the dehydrating action of sulphuric acid. Williamson rewrote the process as a traffic of groups. Sulphuric acid took an ethyl group from alcohol, leaving water behind; the resulting sulphovinic acid then handed that ethyl group to another alcohol molecule, taking hydrogen in return. Sulphuric acid was regenerated, and ether was produced.

The formula was doing work. It allowed chemists to follow the movement of groups through a sequence of transformations instead of merely recording elemental proportions at the beginning and the end.

Then came the sharper test. If the water-type picture was right, oxygen could hold two different radicals at once. Williamson therefore contrived ethers containing different combinations of methyl, ethyl and amyl groups. These were mixed ethers, not mere mixtures of symmetrical ethers. Under the rival Liebig-Dumas scheme, such mixed ethers should not have appeared as distinct compounds at all; at most, the experiments should have produced mixtures of symmetrical ethers.

Their production gave the water type operational force.

 

H₂O gained strength because it travelled. It helped chemists understand not only water, but alcohols, ethers and families of organic compounds. It made substitutions intelligible. It made room for oxygen as an atom with a definite combining capacity.

The problem had widened. Water was no longer an isolated substance to be analysed, but the simplest expression of oxygen’s capacity to bind, exchange and organise chemical groups.

 

Counting by substitution

Substitution reactions gave atoms a kind of experimental visibility.

No one saw oxygen holding two hydrogens. No one watched radicals attach themselves like beads on a wire. But chemists could replace one group with another and see what sort of compound resulted. If an atom or radical could be substituted without the molecule falling apart, that said something about the architecture of the compound. If replacing a central atom broke the molecule into two products, that said something else.

Here the language of valency began to acquire force. Oxygen and sulphur behaved as if they could bind two units. Chlorine behaved differently, as if it had only one hand. The metaphor is crude, but useful; later it would be disciplined into valency.

Water became the simplest member of a larger argument. If oxygen could connect two radicals in ethers, then the two hydrogens in water no longer looked like an arbitrary doubling. They were the simplest expression of oxygen’s twofold combining capacity.

The older picture of a central binding atom would later lose its privileged status. What remained was more durable: a number attached to an element’s capacity for combination.

This was a different kind of counting from Dalton’s simplicity rule or Avogadro’s gas volumes. Dalton counted by assuming the simplest combination. Avogadro counted through equal volumes. Organic chemists counted through substitutions, replacements and families of compounds. None of these methods made atoms visible. Each made them usable in a different way.

H₂O became embedded in a network of operations in which oxygen’s twofold capacity could be tested across many compounds. The formula began to earn its keep.

 

Cannizzaro and the story we like to tell

There is a familiar heroic version of the story. Avogadro discovered the key; chemists forgot it; Cannizzaro revived it at Karlsruhe in 1860; the fog lifted; H₂O won.

There is some truth in this. Not enough.

Cannizzaro’s intervention mattered. His presentation of atomic and molecular weights was clear, forceful and timely. By using Avogadro’s ideas, he gave chemists a way to distinguish atoms from molecules, molecular weights from atomic weights, formulas from combining proportions. His pamphlet circulated after Karlsruhe and influenced younger chemists who were looking for order in a confusing field.

The achievement was real. It was not miraculous.

By the time of Karlsruhe, many of the crucial battles had already been fought elsewhere. Organic chemistry had done a great deal of the work. Substitution, types, radicals and valency had given chemists practical ways to count and sort atoms. Cannizzaro clarified and spread a system that was already becoming workable; he did not unlock the problem by himself.

Nor did his account contain the strongest organic side of the story. Williamson, types, radicals and substitution had already made H₂O useful in ways that a purely Avogadrian argument could not.

Consensus emerged because several practices began to reinforce one another. Volumes, atomic weights, substitution patterns, organic structures and valencies gradually pulled in the same direction. H₂O did not win because an old hypothesis was finally remembered. It won because chemists had learned to make the formula work across a widening range of operations.

A formula becomes harder to doubt when it begins to do work elsewhere.

When H₂O became workable

By the 1860s, H₂O was no longer an isolated claim about water. It belonged to a broader chemical world.

Hydrogen was monovalent. Oxygen was divalent. Water belonged to a family of compounds in which oxygen could connect two units. Gas volumes supported the distinction between atoms and molecules. Organic substitutions gave chemists ways to test combining capacities. Atomic weights and molecular formulas could now be adjusted together with less arbitrariness than before.

Agreement on H₂O did not mean agreement on the reality of formulas. Some chemists treated them as useful instruments; others took them as claims about molecular architecture; many occupied positions somewhere between those poles. Chemical atomism became operationally secure before philosophical realism about atoms became uncontested.

The settlement also did not turn chemistry into a single perfectly unified language. Equivalent weights, atomic weights, empirical formulas, structural formulas and physical atomism did not collapse into one view. The field was rearranged. H₂O became stable within that rearrangement, not because every older tension disappeared, but because enough practices now supported the same count.

Once that happened, HO no longer looked like a reasonable simplification. It looked wrong. But it looked wrong from inside a later practice.

 

What H₂O hides

H₂O looks almost weightless on the page. Three symbols, one subscript, a formula learnt so early that it seems to have no history at all.

It has a long one.

Before chemists could write that subscript with confidence, water had to lose its ancient simplicity, acquire hydrogen and oxygen as constituents, and then submit to a harder demand. Its invisible parts had to be counted.

That last demand was the strangest. Weights could be measured, gases collected, volumes compared, substances replaced, compounds arranged into families. Yet no one could open a molecule and inspect its furniture. The number two had to come from elsewhere.

It came from operations. From volumes that made gases countable; from equivalents that allowed calculation; from substitutions that let radicals change places; from families of compounds in which oxygen repeatedly behaved as a twofold centre of combination. A count that no eye could read became stable through chemical work.

This is what the formula now conceals. H₂O is neither a convention nor a snapshot of an invisible object. It is a compressed history of practices, a small sign in which balances, gases, paper formulas, ethers, substitutions and valencies have disappeared into familiarity.

Water is H₂O. The difficulty lies in remembering how much chemistry had to be invented before that sentence could become obvious.

 

References

Avogadro, A. (1811). ‘Essai d’une manière de déterminer les masses relatives des molécules élémentaires des corps, et les proportions selon lesquelles elles entrent dans ces combinaisons’. Journal de physique, de chimie, d’histoire naturelle et des arts, 73, pp. 58–76.

Cannizzaro, S. ([1858] 1910). Sketch of a Course of Chemical Philosophy. Alembic Club Reprints, no. 18. Edinburgh: The Alembic Club.

Chang, H. (2012). Is Water H₂O? Evidence, Realism and Pluralism. Dordrecht: Springer. DOI 10.1007/978-94-007-3932-1.

Dalton, J. (1808). A New System of Chemical Philosophy, Part I. Manchester: Printed by S. Russell, for R. Bickerstaff, Strand, London.

Gay-Lussac, J. L. (1809). ‘Mémoire sur la combinaison des substances gazeuses, les unes avec les autres’. Mémoires de physique et de chimie de la Société d’Arcueil, 2, pp. 207–234.

Klein, U. (2003). Experiments, Models, Paper Tools: Cultures of Organic Chemistry in the Nineteenth Century. Stanford: Stanford University Press.

Liebig, J. von. (1851). Familiar Letters on Chemistry, in Its Relations to Physiology, Dietetics, Agriculture, Commerce, and Political Economy. 3rd ed. London: Taylor, Walton, & Maberly.

Rocke, A. J. (1984). Chemical Atomism in the Nineteenth Century: From Dalton to Cannizzaro. Columbus: Ohio State University Press.

Rocke, A. J. (2010). Image and Reality: Kekulé, Kopp, and the Scientific Imagination. Chicago: University of Chicago Press.

Williamson, A. W. (1850). ‘Theory of Ætherification’. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 3rd series, 37(251), pp. 350–356.

Williamson, A. W. (1852). ‘On Etherification’. Quarterly Journal of the Chemical Society of London, 4, pp. 229–239. DOI 10.1039/QJ8520400229.

 

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Authors: Elisa Rigosi, Researcher in Sensory Biology, Department of Biology, Lund University and David O’Carroll, Professor of Sensory Biology, Department of Biology, Lund University

The post Bees view the world in surprisingly different ways appeared first on Mapping Ignorance.

Author: José Luis Granados Mateo is a postdoctoral researcher in the Department of Philosophy at the University of the Basque Country (EHU) and a member of the Integrated History and Philosophy of Science (iHPS) research group. His work focuses on history and philosophy of science, science and values, and the epistemology of scientific practices.

By 1800, water had already changed once. It was no longer one of nature’s simple terms, but a compound of hydrogen and oxygen (see Is Water H₂O? (1) From Element to Compound). The new chemistry had a powerful account of that change. What it still wanted was a more direct experiment: water itself, giving up its constituents.

Then came Volta’s pile.

It was not a spectacular object. Discs of metal stacked in pairs, pieces of moist material between them. Yet it altered the experimental life of electricity. Static machines produced sparks, shocks and theatrical moments. The pile offered something steadier. Electricity could now be made to act for long enough to enter chemistry.

Nicholson and Carlisle tried it on water almost at once. They placed wires from the pile in the liquid. Gas appeared, or rather the two sides of the reaction began to declare themselves. Hydrogen was released at one wire; oxygen, in the earliest arrangement, fixed itself to the other, nearly two inches away. Later, with gold or platinum electrodes, oxygen could be collected as gas. The awkward fact remained. The two supposed products of one decomposition appeared at opposite electrical ends of the apparatus.

At first glance, this looked like the experiment the new chemistry had been waiting for.

The trouble was that it looked a little too good.

 

A cleaner decomposition

The first article in this series followed water from element to compound. That change had not been a simple passage from error to fact. It involved a wider transformation of chemical practice: new gases, new names, balances, combustion experiments, recomposition, and a growing confidence in quantitative composition.

Electrolysis seemed to bypass much of that history. No flame, no combustion vessel, no burning of inflammable air with oxygen, no droplets collected on glass. The battery appeared to make water surrender its constituents directly. If water was composed of hydrogen and oxygen, here was synthesis in reverse: pass electrical action through the liquid and the two gases would come out.

Strictly speaking, electrical decomposition of water was not new. In 1789, Adriaan Paets van Troostwijk and Jan Rudolph Deiman had used repeated sparks of static electricity to obtain a mixture of hydrogen and oxygen from water, then recombined that mixture into water. Their experiment mattered. It was also awkward. The gases came together, in small quantities, and separating them for testing was difficult.

Nicholson and Carlisle’s version looked sharper. The Voltaic pile produced sustained action. The products could be made to appear separately. Hydrogen here, oxygen there. Each could be collected, examined and identified.

That was the improvement. It was also where the puzzle began.

 

When neatness becomes suspicious

Suppose water is decomposed. What, exactly, has been decomposed? A particle of water, perhaps. Yet if that particle is split into hydrogen and oxygen, why do the products not appear together? Why does hydrogen emerge at one wire and oxygen at the other? Why should the layout of the battery govern the place where each product appears, while the water between the wires shows no visible sign of being torn apart?

This was not an experiment going wrong. The gases appeared reliably. They appeared at the expected poles. That was precisely what made the situation uncomfortable.

Nicholson noticed the difficulty at once. Hydrogen appeared at one wire; oxygen was found at the other, nearly two inches away. The space between them did not look like a region where water was being visibly pulled into its parts. No trail of bubbles marked the route. The products simply appeared at opposite ends of the electrical arrangement, as if the battery had assigned them places.

Others saw the same embarrassment. Cuvier put it bluntly: if oxygen and hydrogen came from the same particle of water, why did they appear at two distant points, each always at its own wire? Haüy pressed the problem in molecular terms. If one molecule of water was decomposed, why were its products separated? If two molecules were decomposed, why did one give only hydrogen and the other only oxygen?

The question was almost childishly simple. That was why it would not go away.

Today we speak of ions, electrodes and charge transport. Those words make the old difficulty vanish before we have properly felt it. Around 1800, chemists had poles, attractions, affinities, particles, electrical fluids, acids, alkalis, metals, and a growing suspicion that electricity and chemistry belonged together. The experiment was clear enough. The mechanism was not.

Chang calls this the distance problem. The phrase is exact. The gases appeared with too much order for a simple story of local decomposition.

 

The distance problem

The obvious interpretation could still be defended. Perhaps electricity pulled the constituents of water apart. Oxygen went to one pole, hydrogen to the other. Many experiments already suggested that substances had different electrical tendencies, so the idea was hardly absurd.

It left the hardest part untouched.

What travelled through the water? Hydrogen? Oxygen? Something already separated? Something charged? If the constituents were detached somewhere between the electrodes, why did they not appear on the way? If they moved invisibly, why did they not recombine? And if decomposition happened only at the wires, how could it be the decomposition of the same portion of water?

Changes in apparatus did not remove the difficulty. Davy tried water held in separate gold cups, connected only by a moist bridge of fine asbestos, an arrangement he attributed to William Hyde Wollaston. The products still appeared at distant sites. The more carefully experimenters separated the places of action, the more pressing the question became: where, exactly, was water being decomposed?

 

That was the oddity. Electrolysis was not messy. If anything, it was too orderly. Hydrogen went one way, oxygen another. The battery gave a geography to the products of decomposition.

A flame did nothing like this. When inflammable air burned with oxygen, water appeared as the product of combination. There were difficulties, of course, but not difficulties of distance. Electrolysis changed the spatial form of chemical evidence. Water did not answer in one place. It answered at two.

Under electrical action, water no longer seemed merely something to be composed or decomposed. It seemed to contain routes.

 

Ritter changes the verb

Johann Wilhelm Ritter changed the verb.

For Lavoisierian chemists, electrolysis was analysis. Electricity decomposed water into hydrogen and oxygen. Ritter proposed the reverse. Electrolysis was synthesis.

At the negative pole, water combined with negative electricity and yielded hydrogen. At the positive pole, water combined with positive electricity and yielded oxygen. No product needed to travel from a decomposed particle somewhere in the middle of the liquid. The gases were made where they appeared.

It was a radical move. It was not foolish. Ritter took the most striking feature of the experiment seriously: the gases emerged at different poles. He did not treat that separation as an inconvenience to be patched up later. He built his interpretation around it. If hydrogen appears here and oxygen there, perhaps the two gases are being formed locally by two different electrical processes.

The price was high. Water became elementary again. Hydrogen and oxygen became compounds of water and electricity.

To modern chemistry, this looks perverse. Seen from the apparatus, it had one plain advantage: the gases were produced where they were found. Electricity was still widely treated as something with material agency. If a pile drove something into a liquid, why could that something not combine chemically with the liquid? Why must electricity be only the knife, and never part of what is made?

The phlogistonist echo was clear enough. If inflammable air could be read as water combined with phlogiston, hydrogen could be read as water combined with negative electricity. Identify phlogiston with negative electricity, and Ritter’s scheme comes surprisingly close to older anti-Lavoisierian accounts of water.

Priestley, from America, also saw that the new experiment did not force the Lavoisierian reading. He followed the debate through Nicholson’s journal and argued that electrolysis need not be understood as the decomposition of compound water. His intervention matters because it shows how differently the same electrical process could be read. For Lavoisierians, the gases were constituents released from water. For Priestley, they could still be products of another chemical-electrical process.

Ritter’s view did not last. Chemistry was becoming more firmly Lavoisierian, and elementary water had little room left in the emerging discipline. Still, electrolysis itself had not supplied a neat refutation. Ritter had solved the distance problem rather well. His view lost ground as chemists built an electrochemical practice around compound water.

Keeping water a compound

The Lavoisierian problem was delicate. Chemists had to hold three claims together. Water was composed of hydrogen and oxygen. Electricity decomposed it. Yet the products appeared at different electrodes, with no visible decomposition in between.

So the theory needed help.

One option was invisible transfer: one constituent moved through the liquid to its proper pole. Another was a chain of decompositions and recompositions, in which neighbouring particles handed hydrogen and oxygen along until the products emerged at the ends. In related versions, the whole liquid was imagined as electrically ordered, so that the final gases appeared only where that order met the metal.

These were rescue hypotheses. The phrase can sound dismissive, although it need not be. They were attempts to make a powerful practice intelligible. Electrolysis worked. Its effects could be repeated, varied and extended. What was missing was a mechanism that made the spatial separation of the gases sit comfortably with compound water.

The chain idea had a certain elegance. No single particle of hydrogen or oxygen needed to travel the whole distance. A constituent could be passed along through a succession of exchanges, like a message moving through a crowd. The gas would appear only at the end of the chain.

But what exactly was being passed? How were the exchanges coordinated? Why did the process end at just the right pole? The explanation helped, though it left plenty in the dark.

The transfer idea had its own attraction. If chemical particles had electrical characters, perhaps they could be drawn through the liquid. Again, however, the path remained hidden. The water between the wires did not display the process one wanted to see. Without a developed theory of ions and charge transport, the mechanism remained speculative.

None of this made the hypotheses useless. They kept research going. They suggested new apparatuses, new distances, new electrodes, new liquids and new comparisons. An experiment need not be fully understood before it becomes useful.

That is more or less what happened.

 

When water stopped being negotiable

By the early nineteenth century, compound water was becoming part of the working furniture of chemistry. Not every detail had been explained. The distance problem remained. Yet fewer and fewer chemists were prepared to reopen the older question of whether water might be elementary.

Water as a compound was no longer simply a hypothesis under trial. Increasingly, it was a condition for further work.

That shift mattered. Once water was taken as compound, electrochemistry could ask other questions. What moved towards each pole? What kinds of substances were decomposed by the pile? How did acids, alkalis, salts and metals behave under electrical action? What changed when the electrode, distance or solution changed? How much chemical change corresponded to how much electrical action?

The material practice hardened. Trough batteries became easier to handle. Gold and platinum electrodes helped prevent the products from disappearing into reactions with the metal. Separate cups connected by wet asbestos became a standard arrangement. The theory remained unsettled, but the apparatus was becoming disciplined.

The unresolved puzzle did not stop electrochemistry. Experimenters varied the arrangement. They separated vessels, introduced bridges, changed electrodes, added acids and salts, and studied what appeared where. The field learnt to handle the phenomena before it possessed a settled account of what moved inside the liquid.

Later developments brought more discipline. Faraday gave electrolysis a quantitative discipline in the 1830s. By the end of the nineteenth century, Arrhenius’s theory of ionic dissociation would help make sense of processes that had long remained obscure. That later clarity, though, should not be read backwards into 1800. For decades, electrochemistry advanced with a problem at its centre.

 

What electricity did to water

Electrolysis changed what water could be made to do in the laboratory. It did not simply reveal a truth waiting inside the liquid. It altered the route by which that truth could be pursued.

The first article in this series followed water into the balance sheet of chemistry. Water became a substance that could be produced, analysed, recomposed, weighed and named through its constituents. Electrolysis added another sort of depth. Water could now be interrogated electrically. Its constitution became a problem of poles, currents, attractions, transport and hidden motion.

Lavoisierian chemistry had given water constituents. Electrochemistry gave it pathways.

The familiar classroom demonstration hides that difficulty. The bubbles are easy to see. Their meaning was not. Hydrogen here, oxygen there: that simple separation forced chemists to imagine invisible processes inside a liquid that otherwise looked continuous and unchanged.

Electrolysis made compound water harder to abandon, without making it transparent. It turned water into an electrochemical object: something that could conduct, polarise, transport, release and exchange. The question was no longer only whether hydrogen and oxygen belonged to water. It was how electrical action could make them appear at all.

Still, electrolysis could not settle the formula. It could make water yield hydrogen and oxygen. It could not tell chemists how those elements should be counted.

That was the next difficulty. Once water had constituents, chemistry still had to learn how to count them.

 

References

Chang, H. (2012). Is Water H2O? Evidence, Realism and Pluralism. Dordrecht: Springer. doi: 10.1007/978-94-007-3932-1.

Davy, H. (1807). ‘The Bakerian Lecture, on Some Chemical Agencies of Electricity’. Philosophical Transactions of the Royal Society of London, 97, pp. 1–56. doi: 10.1098/rstl.1807.0001.

Faraday, M. (1834). ‘Experimental Researches in Electricity.—Seventh Series’. Philosophical Transactions of the Royal Society of London, 124, pp. 77–122. doi: 10.1098/rstl.1834.0008.

Nicholson, W. (1800). ‘Account of the New Electrical or Galvanic Apparatus of Sig. Alex. Volta, and Experiments Performed with the Same’. Journal of Natural Philosophy, Chemistry, and the Arts, 4, pp. 179–187.

Singer, G. J. (1814). Elements of Electricity and Electro-Chemistry. London: Printed for Longman, Hurst, Rees, Orme, A. Brown, and R. Triphook.

Volta, A. (1800). ‘On the Electricity Excited by the Mere Contact of Conducting Substances of Different Kinds’. Philosophical Transactions of the Royal Society of London, 90, pp. 403–431. doi: 10.1098/rstl.1800.0018.

Wilkinson, C. H. (1804). Elements of Galvanism, in Theory and Practice: With a Comprehensive View of Its History, from the First Experiments of Galvani to the Present Time. 2 vols. London: John Murray.

 

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Author: Shireen Kassam, Visiting Professor, Health and Wellbeing Research Group, University of Winchester and Dagfinn Aune, Research Fellow, School of Public Health, Imperial College London

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Author: Sally Christine Reynolds, Associate Professor in Hominin Palaeoecology, Bournemouth University

 

Take a walk in London’s Trafalgar Square this summer and wilting under the heat will be all sorts of body sizes, some tall, some short: a real mix of sizes.

Picking just two from the crowd to represent the whole human species would be a tall order. But that is exactly what we do when we find fossils of ancient hominins (human relatives).

Anthropologists can estimate a person’s height with reasonable accuracy from the long bones – your femur (thigh bone) works well. On average, the size of this bone
scales well with overall height, given a large samples of bones.

This is exactly what a group of scientists have done using fossils dredged from the seafloor off Taiwan. To add to the mystery, these bones are those of Denisovans, currently one of the most enigmatic species of hominins.

The Denisovans were a widespread species of Homo (the genus which encompasses our own species, as well as close relatives such as Neanderthals). They were initially known only from genetic sequences that had been extracted from isolated fragments of material, such as a small finger bone, at Denisova Cave in the Altai Mountains of Siberia.

The apparent absence of substantial fossil material from this species made them mysterious, although in recent years, fresh discoveries have added to our understanding. These include the Dragon Man (or Harbin) skull, recovered from a river in northeast China. DNA and protein analysis has now linked this large skull to the Denisovans.

The remarkable feature of the fossils analysed by Yousuke Kaifu, from the University of Tokyo, and colleagues, is that they were dredged from the Penghu Channel in the Taiwan Strait.

Most rare hominin fossils are carefully excavated, allowing researchers to record their exact position within layers of sediment and their relationship to associated animals, artefacts and environmental evidence.

These bones were instead recovered from the seabed among the fossilised remains of terrestrial ice age mammals, probably deposited when sea levels exposed the channel as dry land around the time when the ice age was at its height. Their precise discovery locations and geological contexts were not recorded.

This makes the fossils scientifically unusual: their anatomy and preserved proteins contain valuable evidence. However, much of the contextual information normally supplied by an archaeological excavation has been lost.

Recovered long bones

The bones – an incomplete femur (thigh bone) and tibia (the largest bone in the shin) – have been assigned to two Denisovans on the basis of analyses of their recovered ancient proteomes. A proteome is the collection of proteins preserved within a fossil. These proteins can retain evidence of evolutionary relationships, even when DNA has been lost.

These bones produced slightly different proteome signals, which is why the authors suggest that these represent two individuals. The newly described femur and tibia are likewise not directly dated.

Because they were recovered by dredging and lack secure geological provenance, they can only be placed broadly within the Middle to Late Pleistocene, using the animal remains and preservation evidence. The Pleistocene epoch lasted from around 2.58 million years ago until 11,700 years ago.

Aside from assigning the bones to Denisovans, the researchers have used them to infer the stature of the two individuals. They are incomplete, which complicates matters somewhat, but skilled anatomists can be reasonably confident on the complete bone sizes.

The lack of context for the fossils – normally provided by the sediments in which they are recovered from – means we don’t know if the two individuals lived at the same time or at least a similar interval.

But the bones are long and when the other body elements are scaled based on the leg bones, it suggests that these individuals were tall. In the new paper, Yousuke Kaifu and colleagues say that the estimated height of Penghu 2 (one of the newly described specimens) was approximately 1.8 metres (5’11”), with a weight of around 83kg.

Their estimates for the other fossil, Penghu 3, give a height of about 1.9 metres (6’3″) and a weight of about 91kg. These estimates, the team writes in their study, “are comparable to the largest known Pleistocene Homo from Africa and Europe”.

Inferring tallness as a species-level trait based on two individuals is challenging, although it has not prevented some headlines from claiming that Denisovans were tall. Think back to our walk in Trafalgar Square.

Proceed with caution

The dangers of reconstructing stature are illustrated by the famous Turkana Boy or Nariokotome Boy, a remarkably complete Homo erectus skeleton discovered in Kenya in 1984. He was unusual because his skull, teeth and long bones were all complete and available for study.

He was already around 1.6 metres tall when he died, but estimates of his age have differed. Dentally, he appears younger than his skeleton suggests. His skeletal development appeared comparable to that of a modern human aged around 11-13, while microscopic growth increments in his teeth suggested that he was only about eight or nine.

The discrepancy reflects uncertainty over whether Homo erectus grew slowly like us, including an adolescent growth spurt, or matured more rapidly.

Different assumptions consequently produced different projections of his adult height, although more recent modelling suggests he would probably have reached approximately 1.76–1.80 metres.

He is the only example of such a complete Homo erectus skeleton, but does this mean that all members of his species would have been as tall? We should be sceptical – just as we should be about the height of Denisovans.

 

This article is republished from The Conversation under a Creative Commons license. Original article.

The post Were Denisovans tall compared to other ancient humans? appeared first on Mapping Ignorance.

Author: José Luis Granados Mateo is a postdoctoral researcher in the Department of Philosophy at the University of the Basque Country (EHU) and a member of the Integrated History and Philosophy of Science (iHPS) research group. His work focuses on history and philosophy of science, science and values, and the epistemology of scientific practices.

 

Take a glass of water. Nothing about it seems to ask for a theory. It has no colour worth naming, no dramatic smell, no visible architecture. It runs, freezes, boils, evaporates, dissolves, erodes, circulates and nourishes. It belongs to weather, geology, physiology, cooking and plumbing before it belongs to chemistry.

Then science gives us the famous abbreviation: H₂O.

There is a curious finality in those three symbols. They seem to do what ordinary experience cannot. Rain, steam, ice, rivers, blood and laboratory wash bottles are all brought under a clean chemical statement. Two atoms of hydrogen, one atom of oxygen. For many people, H₂O is more than a formula. It is almost an emblem of scientific knowledge, the moment when science gets behind appearances and tells us what something really is.

Yet the formula stands at the end of a long story. Before water could be written as H₂O, it had to acquire a chemical interior. Chemists first had to learn to treat the most familiar liquid in the world as something that could be taken apart, recomposed and assigned constituents. The first transformation was not from one formula to another. It was from element to compound.

 

This is the starting point of Hasok Chang’s Is Water H₂O?, a book that does for water something close to what Inventing Temperature did for thermometry. Temperature now looks like a number waiting to be read from a scale. Chang showed that the scale had to earn that appearance of simplicity. Fixed points had to be stabilised, instruments compared, extrapolations justified. Measurement became routine only after it had first been constructed as a reliable practice.

Water presented another kind of difficulty. The problem was not how to make a phenomenon answer numerically, but how to make a familiar substance answer analytically. Chemists had to learn to look at water as more than something that flowed, froze, boiled, dissolved, condensed or could be purified. They had to ask whether it had an inside: whether it could be taken apart, made again, and understood through its constituents.

To us, that question feels almost unavoidable. In the eighteenth century, it was anything but.

 

When water had no parts

For much of European natural philosophy, water was an element. That claim can sound childish to us, though only because we hear it through the categories that later displaced it.

The idea was older than Aristotle. Thales had imagined water as the origin of things; Empedocles later placed it among the four basic “roots” of the world, alongside earth, air and fire. Aristotle gave that inherited scheme a more systematic form. But these were not elements in the modern chemical sense. No one was classifying them by atomic number, nuclear charge or molecular structure. They belonged to a physics of qualities and transformations, of hot and cold, wet and dry. Water was simple because, in that world, it occupied a basic place in the order of natural change.

By the eighteenth century, that older grammar had been heavily rewritten. Chemistry had become crowded with acids, alkalis, precipitates, metallic calxes, distillations and newly isolated “airs”. Water was everywhere in this world. It served as solvent, medium, reagent, product and experimental environment. It could be purified, frozen, boiled, condensed and recovered.

Still, none of that made it automatically compound. A substance can be experimentally familiar and still have no recognised chemical interior.

The difficulty is easy to miss because we already speak the language that solved it. We say that hydrogen burns in oxygen to form water. But eighteenth-century chemists did not begin with hydrogen and oxygen. They had inflammable air, dephlogisticated air, fixed air, nitrous air and other gaseous bodies whose identities were still unsettled. The experiment did not come with modern subtitles. The vocabulary in which its meaning now appears self-evident was itself one of the products of the revolution.

Water became a compound when certain operations began to count as analysis and synthesis: taking water apart, producing it again, and treating the substances involved as its constituents. This was more than a taxonomic adjustment. It changed the standard of chemical knowledge. To know a substance now meant, increasingly, to know what it was made of.

 

The wrong theory that worked

The defeated rival was phlogiston theory. Posterity has enjoyed defeating it again and again.

In the usual caricature, phlogiston was a ghostly fire-stuff. Combustible bodies contained it and released it when they burned. Metals contained it too. When heated in air, they lost phlogiston and became calxes. Heat the calx with charcoal, and the metal returned because phlogiston had been restored. Then Lavoisier introduced oxygen, and the ghost vanished.

What makes this story so satisfying? Precisely what makes it misleading. It tells the Chemical Revolution from the winner’s seat, where oxygen looks inevitable and phlogiston appears only to make its exit.

From inside the eighteenth-century laboratory, the story looks less like a comedy of error. Phlogiston was not a fantasy floating above the experiments. It belonged to things chemists and metallurgists handled every day. Metals dulled and lost their malleability when calcined. Calxes could be brought back to metal with charcoal. Ores became usable metals in furnaces. Fire, air, metals, respiration and reduction seemed to be tied together by a common traffic of activity. Phlogiston named that traffic. It was a mistaken name for a real pattern.

No one shows this better than Joseph Priestley. He was not a dim survivor of pre-modern chemistry, nor an experimental genius trapped in mere confusion, but one of the great investigators of the eighteenth century, a dissenting minister, political radical and virtuoso of pneumatic chemistry. In Leeds, living beside a brewery, he studied the “fixed air” rising from fermentation, now carbon dioxide. Later, by heating the red calx of mercury, he obtained a far more remarkable gas. Flames burned in it with unusual brilliance; animals could breathe it for longer than ordinary air; Priestley even tried it himself, noticing what he described as a peculiar lightness in his chest.

We call that gas oxygen. Priestley did not. To him, it was dephlogisticated air.

 

 

The name sounds absurd only when detached from the operations that made it meaningful. In phlogiston chemistry, ordinary air could become loaded with phlogiston. Air deprived of phlogiston would therefore be especially ready to absorb it again. It should support combustion powerfully and sustain respiration unusually well. That is exactly what Priestley saw. His interpretation was wrong, but it was not arbitrary. It made sense of real experimental behaviour.

Nor was the winning term free of theory. Lavoisier called the gas oxygen, meaning “acid-generator”, because he believed oxygen was the universal principle of acidity. That too was false. The defeated word preserved one mistake; the victorious word preserved another. One became a fossil of discarded chemistry. The other became familiar enough to hide its own history.

 

A few drops on the glass

The next turn in the story came from a gas with a wonderfully plain eighteenth-century name: inflammable air. We now call it hydrogen.

Henry Cavendish had studied this light, combustible gas in the 1760s. It could be produced by reacting metals with acids, and it burned readily. When inflammable air was burned together with dephlogisticated air, something extraordinary happened. Water appeared. Two invisible airs had produced the most familiar liquid in the world.

It is hard, now, not to let modern chemistry finish the sentence for us. Hydrogen combines with oxygen to form water. Of course. But the droplets on the glass did not settle the matter by themselves. They had to be interpreted.

A phlogistonist could tell the story differently. Perhaps inflammable air was water combined with phlogiston. Perhaps dephlogisticated air was water deprived of it, or at least air peculiarly ready to receive it. On that reading, the experiment did not reveal that water was composed of two gases. It showed that ordinary water could be restored through the right exchange of phlogiston.

That may sound evasive from our side of the Chemical Revolution. At the time, it was not an absurd reply. A flame, a gas, a misted vessel, a change in weight. None of these comes with its meaning attached. Evidence becomes evidence inside a working system of concepts, operations and standards. What counts as a substance? What counts as a component? What counts as analysis rather than transformation? Which variable should be trusted most, appearance, activity, weight, or reproducibility?

The new chemistry changed the setting in which the experiment was read. The appearance of water was no longer merely a striking event at the end of combustion. It became part of a quantitative account. How much inflammable air had disappeared? How much oxygen had been involved? How much water had been produced? Could water be decomposed and then recomposed, with the numbers matching closely enough to make the same substance appear on both sides of the operation?

At that point, water was no longer simply appearing on the glass. It was entering the balance sheet of chemistry.

 

The balance makes composition visible

A metal is heated in air. It dulls, loses its shine, gives up its malleability and turns into a calx. In phlogiston chemistry, this looked like a loss. The metal had given up the principle that made it metallic.

Then came the awkward fact on the bench. The calx was heavier.

Heavier, after a supposed loss.

That did not destroy phlogiston theory in a single blow. Good chemists could still defend it, and did. Yet the weight gain gave oxygen chemistry an opening. Perhaps calcination was better understood as combination with something from the air. Follow the weights carefully and chemical change begins to look different. Appearances still matter. Activity still matters. So do colour, heat, flame, residue and reversibility. But weight gives the whole operation a new discipline. It turns transformation into material accounting.

 

 

The same habit of attention changed the status of water. A substance that can be broken down into measured quantities of other substances, and then produced again from them, starts to lose its old simplicity. It has become analysable. It has constituents.

This was the strongest hand of the new chemistry. Combustion, calcination, gases and water could now be drawn into the same quantitative order. Chemical names changed too. A name was expected to do more than preserve origin, appearance, use or inherited convention. Ideally, it should tell chemists what a substance contained.

The point needs care. This was never a struggle between careful measurers and careless speculators. Cavendish was one of the most exact experimentalists of the century. Richard Kirwan, among the serious defenders of phlogiston, treated weight as a genuine difficulty. The disagreement concerned the authority of weight. Should it count as one experimental constraint among others, or should it become the privileged route to chemical identity?

That question slowly shifted the terrain. Phlogiston chemistry was most at home in a language of principles, powers and transformations. What gives a body combustibility? Why does a metal lose its metallic character? How can a calx be revived? What passes from one substance to another when reaction occurs?

The new chemistry pressed elsewhere. What does this substance contain? In what proportions? Can those constituents be separated and brought together again while the material account remains intact?

Once that style of reasoning became dominant, water’s old simplicity became harder to defend. Water could still be boiled, condensed, purified and recovered, just as before. Now it could also be weighed into a different story. It could be decomposed, recomposed, renamed and treated as a body with an interior.

 

No single knockout blow

The usual story of the Chemical Revolution likes a clean scene. Priestley isolates oxygen and fails to recognise it. Lavoisier understands what Priestley could not. Phlogiston collapses. Modern chemistry begins.

History was less obliging.

Priestley knew the new chemistry and resisted it. Cavendish remained cautious. Other competent chemists hesitated, adopted parts of the oxygen system, or kept some version of phlogiston alive. Their reluctance was not simple blindness. Phlogiston theory still connected combustion, respiration, calcination, reduction and the behaviour of airs. It had also helped produce many of the experimental facts that later counted in favour of oxygen chemistry.

Lavoisier’s system, for all its power, carried its own burdens. His theory of acidity was wrong. The very word oxygen preserved that mistake, since it named the gas as the supposed generator of acids. His chemistry also relied on caloric, an imponderable fluid of heat. Seen from the present, caloric does not look obviously more respectable than phlogiston. Lavoisierian chemistry was elegant, disciplined and transformative. It was not modern chemistry arriving whole and immaculate.

So the real historical question is more interesting than the textbook one. Why did one way of doing chemistry become more convincing, more portable, more teachable and more productive than another?

Much of the answer lies in the rise of compositionism. Oxygen chemistry made substances legible as combinations of measurable constituents. Reactions became material accounts. Chemical names began to function as compressed statements of composition. Laboratory operations, weights, gases and nomenclature could now be made to speak the same language.

Phlogiston still made sense of many transformations, but it sat uneasily in that emerging world. It named a principle of activity, a way of thinking about combustibility, metallic character, reduction and chemical power. The new chemistry increasingly wanted components. It asked what substances were made of, how much of each constituent they contained, and whether those constituents could be separated and brought together again.

As compositionism gained ground, phlogiston began to lose its natural habitat. It could still speak about combustion, reduction and chemical activity, but chemistry was moving towards another language: constituents, proportions, balances, material accounts. The revolution changed the answer, certainly. More importantly, it changed the kind of answer chemistry wanted.

The first metamorphosis of water

When water became a compound, it did more than shift from one scientific category to another. Its identity for chemistry changed.

Before this transition, water could pass through laboratory practice while keeping its apparent simplicity. It could be boiled, frozen, distilled, condensed, purified and recovered. It appeared everywhere, as liquid, vapour, solvent, medium, residue or product. Familiarity gave it stability. It did not yet give it an interior.

After the Chemical Revolution, the same substance began to occupy a different role. Water could be produced from other bodies, analysed through them, and assigned constituents. The liquid that returned from vapour or appeared after combustion now carried a hidden depth. It was no longer merely recovered; it could be accounted for.

Nobody saw that hidden constitution. There were no water molecules under a microscope, no direct image of hydrogen and oxygen joined together. The evidence came through laboratory work: gases prepared and collected, vessels sealed, mixtures burned, droplets condensed, weights compared, names revised. Compound water became credible through practice before it became imaginable as a molecular structure.

This is why the story should not run too quickly towards H₂O. Lavoisierian chemistry helped establish water as a compound of hydrogen and oxygen. It did not settle the formula. It was one thing to say that water was made from hydrogen and oxygen; it was another to decide how many atoms of each entered the compound. That question would take nineteenth-century chemistry into a different set of difficulties.

First came the more basic metamorphosis. Water ceased to be one of nature’s simple terms. It became a substance that could be decomposed, recomposed and written in the language of other substances.

 

The cost of clarity

The new chemistry clarified immensely. It disciplined measurement, stabilised nomenclature and helped make possible the modern science of composition. But clarity selects. It illuminates some things by pushing others into shadow.

Phlogiston theory had been concerned with chemical activity. Why do substances burn, reduce, revive, calcine, combine and transform? Early compositional chemistry was much better at saying what entered and left a reaction than at explaining why the reaction happened at all. It provided an excellent account book, though not yet a full theory of chemical agency.

Some questions once handled by phlogiston later returned in different forms. Chemical potential energy, oxidation-reduction and electrons all came to occupy parts of the conceptual space that phlogiston had once held. That does not vindicate phlogiston as a literal substance. It suggests something subtler and more useful. A false theory can preserve real questions. A victorious theory can be right and still leave work unfinished.

This is a more realistic picture of scientific progress. Oxygen chemistry won for good reasons. But those reasons were not contained in one devastating experiment before which only fools could hesitate. They belonged to a wider reorganisation of chemical practice.

Water’s first great chemical transformation was therefore not the discovery of a formula. It was the making of a new kind of object. The familiar liquid became something that could be analysed, recomposed, weighed and named through its constituents. Only after that could the later question become pressing: not whether water was compound, but what exactly its composition was.

Electricity seemed about to settle the matter. It made water yield hydrogen and oxygen directly, as if nature had finally supplied the decisive experiment.

Instead, the proof produced a new puzzle.

 

References

Chang, H. (2012). Is Water H₂O? Evidence, Realism and Pluralism. Springer. doi 10.1007/978-94-007-3932-1.

Chang, H. (2004). Inventing Temperature. Measurement and Scientific Progress. Oxford University Press.

Cavendish, H. (1784). ‘Experiments on Air’. Philosophical Transactions of the Royal Society of London, 74, 119–153.

Kirwan, R. (1789). An Essay on Phlogiston and the Constitution of Acids. London, J. Johnson.

Lavoisier, A.-L. ([1789] 1965). Elements of Chemistry. Dover.

Priestley, J. ([1796] 1969). Considerations on the Doctrine of Phlogiston, and the Decomposition of Water. Kraus Reprint.

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Author: Richard de Grijs, Professor of Astrophysics, Macquarie University

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Authors: Phil Wiseman, Senior Research Fellow and Ernest Rutherford Fellow in Physics and Astronomy, University of Southampton and Mark Sullivan, Professor, University of Southampton

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Author: Melissa Lee, Senior Lecturer, School of Mathematics, Monash University

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Authors: Kai Riemer, Professor of Information Technology and Organisation, University of Sydney and Sandra Peter, Director of Sydney Executive Plus, Business School, University of Sydney

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Authors: María-Luisa Chas-Amil, Catedrática de Economía Aplicada, Universidade de Santiago de Compostela and Julia María Touza, Professor of Environmental Economics, University of York

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Author: Marta Sánchez López, Postdoctoral researcher at Research Centre for Basque Language and Texts (CNRS-IKER) – Université Bordeaux Montaigne. She also collaborates with the The Bilingual Mind Research Group (Gogo Elebiduna), University of the Basque Country. 

In our daily life, we perceive many different events around us with different participants involved, such as a woman dancing in a theater, or a man enjoying an ice cream. To produce and understand the sentences that express these events, it is crucial to identify the number of participants involved and the role that each one plays. As in a drama, we have to identify the role that each character has to understand the story.

Agents, patients and experiencers

In some events, there are two characters involved, as in the girl kicks the ball, with the girl and the ball; in others, instead, there is only one, as in the woman dances or the man falls down, with the woman or the man. Each of these participants has a different role. For example, the woman is an agent, because she is doing the action, just the opposite of the man, who is a patient, because he is the one affected. In the case of the girl has fun, this participant has the role of experiencer, the one who experiences the enjoyment.

In psycholinguistics, the mechanisms involved in the processing of agent, patient, and experiencer roles have been investigated through different experiments such as reading sentences while participants’ brain activity or eye-movements are recorded. In these studies, it has been found that agent and patient categories play a crucial role in language comprehension and production. These two roles are used when reading, listening to, and producing sentences, and different mechanisms are involved in their processing (e.g., ¹²³⁴⁵). Therefore, they form two different categories within the event role repertoire ⁶. They are two different characters within the drama. However, as I will discuss, there is no experimental evidence for a third character, the experiencer role.

One well-established result regarding the processing of agents and patients comes from experiments where participants are asked to read sentences while their brain activity is recorded by means of electrodes, known as EEG studies. Results of these studies show that the processing of patients elicited an N400 component (a negative brain response that occurs around 400 milliseconds after a stimulus is processed) compared to the processing of agents. This effect is associated with the processing of unexpected elements. For example, when processing the day was breezy so the boy went outside to fly, it is expected to read next a kite based on the preceding context; hence, if an airplane is read instead, as this is unexpected, then an N400 effect appears ⁷. Something similar happens with the processing of agents and patients: agents are expected, whereas patients unexpected.

Across languages, speakers tend to interpret the first ambiguous noun phrase of a sentence as an agent instead of as a patient, due to the agent preference ⁸: agents are preferred and are cognitively more salient than patients. Imagine that someone reads the sentence Mary falls down. Before encountering the verb, the reader cannot know whether Mary is an agent or a patient. However, as agents are preferred over patients, the reader interprets that it is an agent by default. Once the verb is reached, there are two options: confirm that Mary is an agent (the expected role) or recategorize it as a patient (the unexpected role). In Mary falls down, it is the second option, Mary is categorized as a patient, because the verb selects this role. This recategorization is reflected in an N400 component. This result shows that agents and patients have different processing mechanisms and that they form two different categories. Furthermore, this effect has been largely found in different languages such as, Basque (Isasi-Isasmendi et al., 2024), Spanish ⁹, German (Haupt et al., 2008), Hindi (Bickel et al., 2015), or Aïwoo (Sauppe et al., 2023), supporting its robustness.

Therefore, these experimental findings show that there is a preference toward agents over patients. The agent is the protagonist of the show, while the patient plays a secondary role.

What about experiencers?

Linguists have proposed that there are other roles within the event role repertoire, for example, the experiencer role ¹⁰.This role corresponds to the participant who experiences a mental state denoted by the verb. Participants of events like loving someone or enjoying an ice cream have this role. The experiencer is the participant who loves or enjoys.

But does experimental evidence show that the experiencer role is a specific role category within the event role repertoire, as agent and patient roles do?

In Sánchez-López (2026), I addressed this question. I investigated whether speakers use a third category, the experiencer role, when processing language. In three reading eye-tracking experiments and in one EEG experiment, I asked Spanish native speakers to read sentences, while their eye-movements and their brain activity were recorded.

In the first two eye-tracking experiments, I compared de processing of sentences with two participants with different combinations of agent, experiencer, and patient roles: (i) María golpea a Juan “Mary hits John”, with an agent (María) and a patient (Juan); (ii) María asusta a Juan “Mary frightens John”, with an agent (María) and an experiencer (Juan); and (iii) María ama a Juan “Mary loves John”, with an experiencer (María) and a patient (Juan).

These two experiments revealed that Spanish native speakers produced higher fixation times on verbs selecting agent-experiencer roles (ii), than on the other two sentence types (i, iii), independently of whether the subject was animate or inanimate (e.g., María/La situación asusta a Juan “Mary/The situation frightens John”). Interestingly, no processing differences were found between sentences selecting agent-patient (i) and experiencer-patient (iii). These findings show that the combination of agent-experiencer involves higher processing cost than other combinations.

But why is the agent-experiencer combination costlier than the others?

A possible explanation could be that the experiencer role is not a specific category within the event role repertoire. As Dowty ¹¹ proposed, agents and experiencers are both grouped within the same category, the one of the proto-agent. Based on this proposal, there are only two roles in the event role repertoire, proto-agent and proto-patient, and proto-agents always become subjects. Hence, in sentences like Mary frightens John, both participants are proto-agents. Since they are both possible candidates to become subject, a competition for subject assignment appears. This competition is reflected in higher processing cost compared to those sentences without competition, sentences with only one proto-agent participant (e.g., Mary hits John or Mary loves John).

To better understand this competition effect, imagine that the proto-agent is the hero of the story and the proto-patient is the villain. In a drama, if there is a hero, there is also a villain. If there are two heroes and no villain in the story, it would be unusual, and probably, it would take more time to understand the story, since this is not expected. Hence, the same happens with sentences like Mary frightens John, which have two proto-agents; this combination is unexpected and it entails higher processing cost than when there is only one proto-agent and one proto-patient.

In another reading experiment using both eye-tracking and EEG techniques, I compared the processing of the three roles, agent, experiencer, and patient again to determine whether this experiencer role behaves like the agent role or not. In these experiments, I used sentences with only one participant involved: (iv) María baila “Mary dances” with an agent, (v) María disfruta “Mary has fun” with an experiencer, and (vi) María se cae “Mary falls down” with a patient. The use of these sentences allowed a more direct comparison of the processing of these roles, since the three roles are subjects. The structure of all sentences is the same (one participant and the verb), and only the event role category changes.

Results revealed that participant looked more at sentences with agents and experiencers than at sentences with patients. Moreover, I found that participants exhibited a preference to interpret the first ambiguous noun phrase (i.e., María) as an agent/experiencer; hence, when they realized that it was a patient instead, an N400 component appeared. But this effect only appeared with patients, not with experiencers, showing that participants processed similarly agents and experiencers, and both different from patients.

What does this evidence show?

These results align with the hypothesis that there are only two role categories within the event role repertoire (Dowty, 1991): proto-agent and proto-patient. Therefore, when the sentences Mary dances or Mary has fun are processed, in both cases, Mary is interpreted as a proto-agent, whereas in Mary falls down, she is interpreted as a patient instead.

All in all, this evidence suggests that agents and experiencers belong to the same role category, the one of the proto-agent. Therefore, when we process language, it seems that we do not differentiate between agents and experiencers, rather we process them similarly. Experiencers are grouped together with agents, being the same character in the event, both proto-agents.

So, proto-agents are the protagonists of the story, the ones that spectators prefer to pay attention to, whereas proto-patients are the secondary characters who complement the story. Regarding experiencers, they do not play a different character in the story, instead, they are also the protagonists.

 

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Author: José R. Pineda got his Ph.D. from University of Barcelona in 2006. Since 2007 he has worked for Institut Curie and The French Alternative Energies and Atomic Energy Commission. Currently he is a researcher of the EHU. He investigates the role of stem cells in physiologic and pathologic conditions.

 

For decades, Alzheimer’s research has been dominated by a seemingly intuitive idea: the disease is largely explained by the accumulation of abnormal proteins in the brain, primarily beta-amyloid and tau. However, while this view has been useful, it has fallen short in explaining certain fundamental aspects. One of the most striking is that Alzheimer’s affects women far more than men. This is not a minor difference: roughly two-thirds of cases occur in women, and the clinical progression tends to be faster and more severe. The work by López-López and colleagues addresses this imbalance from a different angle. Instead of focusing exclusively on protein accumulation, it shifts attention to the brain’s immune system—specifically to a molecular pathway known as interferon signaling. What the study proposes is that the brain’s immune response, far from being a mere bystander, could be a central driver of the disease, and one that operates differently in men and women ¹.

To understand the importance of this idea, it helps to briefly consider what interferons are. They are molecules that form part of the body’s first line of defense against infections, especially viruses. They activate genetic programs that help cells detect and neutralize threats. Under normal conditions, this system is essential for survival. The problem arises when it becomes chronically or inappropriately activated, as is the case in several inflammatory diseases ². So, the study begins with an analysis of human brain tissue from Alzheimer’s patients. Using transcriptomics (techniques that measure the activity of thousands of genes at once) the authors identified a clear pattern: in the brains of women with Alzheimer’s, genes associated with the interferon response were much more strongly activated than in men (Figure 1). This pattern was not explained by differences in disease severity, since both groups showed comparable levels of neuropathological damage. Instead, it points to an intrinsic difference in how the brain responds to the disease. This finding is significant because it suggests that women not only develop Alzheimer’s more often, but may do so through a partially distinct biological pathway. In other words, the same clinical diagnosis may conceal different underlying mechanisms depending on sex.

To move beyond correlation and explore causality, the researchers turned to animal models. They used genetically modified mice (APP/PS1), widely employed in Alzheimer’s research because they develop amyloid plaques similar to those seen in patients. Once again, the same pattern emerged: female mice showed much stronger activation of the interferon pathway, along with increased brain inflammation, neuronal damage, and functional impairment. An especially interesting point is that these differences were not limited to amyloid plaque levels. Indeed, female mice did have more plaques, but the study shows that neuronal damage and inflammation cannot be explained solely by amyloid accumulation. This supports a growing idea in the field: that Alzheimer’s toxicity depends not only on protein buildup, but also on how the brain responds to it. In this context, microglia (the brain’s resident immune cells) become key players. Cell-level analyses revealed that these cells are the main drivers of interferon activation. In essence, microglia detect signals of damage or stress and launch an inflammatory response which, if sustained over time, can ultimately harm neuronal tissue. To test whether this pathway is not only associated with disease but actually contributes to it, the authors performed direct manipulation experiments acutely activating interferon signaling in healthy animals using an immune stimulus. To do that, they did intraperitoneal administration of polyinosinic:polycytidylic acid (poly(I:C)) to induce inflammation, but also they performed crossings between the APP/PS1 mice and a microglia-specific Rela conditional knockout line (AD hiIFN). The result was striking: within hours to days, the brains of these animals began to show changes resembling Alzheimer’s pathology, including altered neuronal activity, synaptic disruption, and markers of axonal damage. This results suggest that activation of this pathway is not a secondary phenomenon, but may be sufficient to induce key features of the disease. In parallel, the researchers performed the inverse experiment: they chronically enhanced interferon signaling in microglia within APP/PS1 mice (an Alzheimer’s disease model). This led to a worsening of neurodegenerative features, even though, paradoxically, amyloid plaque levels did not increase—and in some cases even decreased. This last finding is particularly revealing because it challenges a widespread assumption: that fewer plaques necessarily means a healthier brain. Here, the opposite happens!! The animals had less amyloid burden, yet more neuronal damage and worse function. This points to inflammation, and specifically interferon signaling, as an independent driver of degeneration.

Next, the key question is whether this pathway can be targeted therapeutically. To address this, the study explores a pharmacological intervention aimed at the cGAS–STING pathway (a molecular system that senses cellular damage and triggers interferon production). Using a specific inhibitor (STING inhibitor C-176), the researchers demonstrated by RNA-seq and immunohistochemistry that the treatment was able to reduce interferon signaling in the brain. The results were consistent: inflammation decreased, neuronal integrity was better preserved, and cognitive performance improved in memory tests (very important from the therapeutic and functional perspective). More importantly, these effects were far more pronounced in females, which aligns with the idea that this pathway is more active in them and therefore more impactful when blocked. Notably, these improvements occurred without significant changes in amyloid plaque levels. Once again, the conclusion is clear: modulating the immune response may be as important as, or even more important than, targeting protein aggregates. Taken together, this work proposes a shift in how Alzheimer’s is understood. Rather than viewing it solely as a disease of protein accumulation, it suggests it is also—critically—a disorder of immune dysregulation. More specifically, one in which excessive interferon signaling plays a central role, particularly in women.

The practical implications are twofold. First, it opens the door to therapies that modulate immune pathways in the brain, something already feasible in other diseases using drugs that target similar mechanisms. Second, it highlights the importance of incorporating sex as a biological variable in both research and treatment design. What works in men may not work the same way in women, and vice versa. As with any preclinical study conducted in mice we must be cautious when extrapolating to humans: results in animal models do not always translate directly, and manipulating the immune system carries inherent risks, given its essential role in protecting against infections. However, the value of this work lies not only in its potential clinical applications, but in the conceptual framework it offers.

By identifying interferon signaling as a key factor in sex-specific vulnerability to Alzheimer’s, the study provides a mechanistic explanation for a long-observed but poorly understood phenomenon. Perhaps more importantly, it suggests that intervening in this pathway could alter disease progression even without changing classical amyloid pathology. In a field where many clinical trials have failed by focusing solely on plaque removal, this perspective is not just novel, it may be necessary to explore it in the future.

 

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