Large language models (LLMs), such as ChatGPT and Claude, are increasingly being used to provide health information to the public. Although this is beneficial for improving access to treatment information, their limitations were revealed by a new study that found LLMs were biased in their treatment choices and using these outputs in practice could drive up treatment costs.

A research team led by Professor Daiju Ueda at Osaka Metropolitan University’s Graduate School of Medicine in collaboration with The University of Texas’ MD Anderson Cancer Center asked five LLMs (GPT-5.4, Claude Sonnet 4.6, Gemini 3.1 Flash, Llama 4, and MedGemma) to recommend up to three FDA-approved drug products for a range of simulated scenarios ¹.

Although an average of 5.5 FDA-approved products were available for each specified active ingredient, a single product accounted for an average of 97.5% of first-choice recommendations across the tested scenarios and models.

“The recommendations were mostly for brand-name drugs, those highly visible in Google searches, and treatments with dedicated Wikipedia pages,” Professor Ueda said.

The LLMs also tended not to recommend products with later FDA approval and those more commonly prescribed in actual physician practice. The study also identified recommendations that were inconsistent with regulatory information, including discontinued products.

The researchers then evaluated how much this could potentially cost patients using US data. They looked at a scenario where physicians’ prescribing behavior was performed in accordance with the LLMs’ recommendations.

“We found that annual acquisition costs would increase by approximately US$55.6 million,” Professor Ueda summarized. “This represents a 1,019% increase in the cost of purchasing these prescriptions.”

When instructed to prioritize lower-cost or generic products, the models shifted their recommendations accordingly; however, recommendations remained concentrated on a limited set of products.

“Our findings highlight the need to evaluate healthcare AI for biases in product selection and the handling of cost and regulatory information,” Professor Ueda said. “Combining AI systems with trusted drug information sources and human oversight may help support more appropriate medication choices.”

The post The expensive AI’s preference for drugs with a strong online presence appeared first on Mapping Ignorance.

A gene identifier enters a spreadsheet and emerges as a date. The transformation can look like a clerical nuisance, remote from the serious business of scientific reasoning. Yet a 2021 study of PubMed Central articles from 2014–2020 found gene name errors in the supplementary Excel files of 3,436 of the 11,117 publications containing such gene lists: 30.9% of that sample (Abeysooriya et al., 2021; Figure 1). The finding does not mean that a third of the scientific conclusions were false. It shows how an operation treated as routine can alter the material on which later reasoning depends. The scientific question begins with the damaged identifiers, but it need not end there. Why was a familiar way of handling data trusted to preserve their meaning? Gaston Bachelard helps us to pursue that question beyond the recommendation to be more careful. His account of scientific education asks how competent researchers learn to recognise the assumptions embedded in what they already know how to do. That is a problem for the laboratory, the field station and the research group working through a shared dataset today.

Gaston Bachelard and epistemological obstacles

Bachelard (1884–1962) taught physics and chemistry before becoming a university philosopher, first at Dijon and then at the Sorbonne. This trajectory matters because the classroom remains a place of philosophical discovery throughout his work: understanding a scientific claim involves changing how one thinks, and the resistance to that change deserves investigation. His 1938 book, La formation de l’esprit scientifique, usually rendered in English as The Formation of the Scientific Mind, makes this resistance its subject. The word formation carries considerable weight. A scientific mind is formed through work on concepts, problems and evidence; possession of information alone does not secure it. Nor should mind suggest a mysterious faculty owned by a special class of people. What interests Bachelard is the activity through which thinking becomes capable of correcting itself. His account is exacting because that activity cannot simply be completed during training and then taken for granted throughout a career.

He calls the difficulties arising within knowing itself epistemological obstacles. The expression can initially sound like an elaborate name for ignorance, but its point is almost the reverse. Ignorance leaves something unknown; an obstacle can provide an answer that prevents us from recognising what remains unknown. The obstacle may be a persuasive image, a habitual classification, or an explanatory principle whose earlier usefulness encourages us to extend it too far. In the opening chapter of The Formation of the Scientific Mind, Bachelard locates the problem with unusual precision:

It is not a matter of considering external obstacles, such as the complexity or fleeting character of phenomena, nor of blaming the weakness of the senses or of the human mind: it is within the very act of knowing that, through a kind of functional necessity, impediments and confusions arise. It is there that we shall identify causes of stagnation and even regression; there that we shall discern causes of inertia which we shall call epistemological obstacles. (Bachelard, 2000, p. 15)

This distinction concerns the source of a difficulty. A software conversion is not, simply by occurring, a Bachelardian epistemological obstacle. The philosophical problem arises when a habit of understanding makes the conversion difficult to recognise: for example, treating a spreadsheet as a transparent container rather than a system that interprets entries. This is my extension of Bachelard’s argument, not a claim about a technology he discussed. Supported versions of Excel now provide controls over automatic data conversions (Microsoft, n.d.); a means of preventing an error does not establish that a particular dataset escaped it. For researchers, the useful response is to determine which transformations occurred and how the identifiers can be checked against their source. The wider lesson concerns expertise. Only knowledge of what the identifiers mean makes this particular damage intelligible, yet expertise must also examine the habits that make its everyday work possible. An obstacle can operate before there is a conclusion to favour, shaping what enters the investigation as an apparently settled fact.

A question

The place to begin is therefore the question. ‘For a scientific mind, all knowledge is an answer to a question. If there has been no question, there can be no scientific knowledge,’ Bachelard writes (2000, p. 16). Consider an ecologist asked whether a population is doing better. The apparent simplicity of the question conceals decisions about what improvement would mean, over which interval it should be assessed, and what evidence could distinguish a durable change from a temporary fluctuation. Specifying these decisions does more than tidy up language. It changes what must be observed and what an answer could legitimately establish. Bachelard’s claim should not be turned into a prohibition on exploratory work, as though every discovery required a hypothesis written in advance. An unexpected observation can force the invention of a question. The point is that the observation acquires a determinate role in knowledge when we can articulate what it bears upon. A collection of results cannot do that intellectual work on our behalf.

This emphasis helps explain his abrasive treatment of opinion. On the same page, he declares that opinion ‘translates needs into knowledge’ (Bachelard, 2000, p. 16). The target is a form of conviction in which what matters to us supplies the apparent reason for believing something. His argument does not require the absurd claim that an everyday judgement can never happen to be correct. He explicitly allows that science may vindicate an opinion, while insisting that its justification will differ from the grounds on which the opinion was held. Nor does the argument authorise scientists to dismiss a patient’s experience, a farmer’s observation or a technician’s practical judgement. Those may disclose phenomena that an established procedure has missed. What requires examination is the passage from experience to an explanatory claim: which distinctions make the claim assessable, and what could establish its scope? Read this way, Bachelard’s severity concerns the grounds of assent rather than the social standing of the person speaking.

Teaching as overcoming obstacles

Teaching makes the stakes especially clear. Bachelard criticises the assumption that explaining the same demonstration again will necessarily produce understanding. His objection is that learners already inhabit a world of explanations. Of teachers who overlook this, he writes:

They have not reflected on the fact that the adolescent comes to the physics classroom with empirical knowledge already constituted; the task, then, is not to acquire an experimental culture, but to change an experimental culture, to overturn the obstacles accumulated by everyday life. A single example: the equilibrium of floating bodies is the object of a familiar intuition that is a tangle of errors. (Bachelard, 2000, p. 21)

His example is a piece of wood pressed beneath the water. The hand encounters resistance, and it is tempting to attribute an upward activity to the wood itself. Archimedes’ principle requires a different account, in which the surrounding fluid and the volume displaced become essential to explaining the buoyant force. A floating body and a fully submerged body obey the same principle, although the volume of fluid displaced need not be the same. The educational difficulty lies in reorganising the explanation, rather than merely attaching a formula to the familiar sensation. Bachelard is diagnosing a relation between an intelligible everyday experience and a more exact conceptual account of it (2000, p. 21). Applied to advanced training, the lesson is equally demanding. A doctoral researcher may reproduce a procedure accurately while interpreting its output through an unsuitable model. A useful discussion then has to uncover that model. Asking the researcher to predict what would change under altered conditions may reveal more than another demonstration of the correct calculation.

Language and numbers

Language can make such obstacles remarkably durable. Bachelard devotes a chapter to the sponge, tracing how a familiar image could appear to explain quite different phenomena simply by making them recognisable. Naming an effect in terms of a sponge can leave the actual process of absorption, compression or penetration unexamined (2000, pp. 87–88). His example invites an uncomfortable inspection of our own vocabulary. A contemporary researcher might say that a system stores information or that an organism follows a programme. Such expressions can be productive, provided the proposed correspondence is made explicit and its limits remain available for examination. The difficulty begins when familiarity with the word is mistaken for knowledge of the mechanism. These contemporary examples are extensions of Bachelard’s diagnosis, not examples from his book. I would take his warning as a reason to put metaphors to work under supervision: ask what they enable us to distinguish, what relations they specify, and where they cease to help. A metaphor that generates a discriminating experiment has a different status from one that merely makes an explanation feel complete.

Numbers offer no automatic escape from this problem. ‘Magnitude is not automatically objective,’ Bachelard cautions in his discussion of quantitative knowledge (2000, p. 248). The warning concerns the relationship between a numerical determination and the object it is meant to describe. A highly repeatable index of environmental quality, for example, may still leave unresolved why those components were selected, why they were combined in that way and which changes the index cannot register. More decimal places will not settle those questions. Bachelard also insists that the precision of a measurement must be related to the sensitivity of the measuring method and to the conditions under which the object remains stable (2000, p. 250). His terminology should not be treated as a substitute for a modern account of measurement uncertainty. The enduring point is that the warrant for a number belongs to a method, an object and specified conditions. Reproducible calculation, suitable measurement and a justified interpretation require connected but distinguishable arguments. A scientific education that attends only to the first leaves considerable room for numerical confidence without understanding.

Predictive modelling supplies a contemporary instance. Kapoor and Narayanan’s 2023 survey of earlier reviews identified data leakage affecting 294 papers across 17 fields. Those documented cases do not provide a representative failure rate for all research using machine learning. One form of leakage occurs when information from the data reserved for testing influences the construction of a model, making its performance look better than it would on genuinely new observations. Selecting predictive variables using the whole dataset before separating training and test data is one route to the problem. In the authors’ reanalysis of civil war prediction studies, correcting the errors removed the claimed substantial advantage of complex models over logistic regression (Kapoor and Narayanan, 2023). This finding is specific to the comparisons investigated; it does not establish the general superiority of simpler models. What interests me philosophically is the change in the question. A performance score becomes evidence only when the conditions of its production support the inference being made. Bachelard’s concern with the formation of a problem is therefore quite practical: what, exactly, has the model been given the opportunity to predict?

The constructive burden of a scientific ‘no’

These examples can give Bachelard an undeserved reputation as a philosopher of permanent demolition. If knowledge repeatedly becomes an obstacle, must the scientific mind always oppose what is accepted? His La philosophie du non, first published in 1940, addresses precisely that misunderstanding. The title can sound like a celebration of refusal, yet his description of the activity is carefully bounded:

The philosophy of no is not a will to negate. It does not proceed from a spirit of contradiction that contradicts without proof and raises empty objections. It does not systematically evade every rule. On the contrary, it is faithful to rules within a system of rules. It does not accept internal contradiction. This philosophy does not deny just anything, at any time and in any manner. Only at certain well-defined junctures does it initiate the inductive movement that characterises it and bring about a reorganisation of knowledge on a broader basis. (Bachelard, 2003, p. 112)

The constructive burden of a scientific ‘no’ is considerable. One must identify what requires revision, give reasons for revising it and determine what follows from the change. Mere dissent contributes none of this. Bachelard’s examples include the movement beyond Euclidean geometry and Newtonian mechanics, understood as enlargements that also reassess the status of earlier knowledge (2003, p. 114). Their philosophical interest lies in the possibility that a previously unrestricted claim becomes intelligible within specified conditions. Recognising the validity of a theorem under particular axioms differs from treating those axioms as the only conceivable organisation of space. Likewise, identifying the domain in which an approximation works can preserve its usefulness while changing our understanding of what it establishes. This is why rectification deserves a richer meaning than the replacement of a wrong answer with a right one. It can alter the relations among concepts and the conditions of their use. There is no reason to assume that every scientific advance follows this pattern, but it offers a valuable standard for criticism: an objection should make the problem more intelligible, not merely display the independence of its author.

Reason itself is implicated in this work. In Le rationalisme appliqué, published in 1949, Bachelard develops an account of rational activity answerable to particular investigations. His applied rationalism is best approached through the interaction between a theoretical requirement and an experimental operation. A theory helps determine what an instrument must distinguish; the instrument’s behaviour, once understood and checked, may require the theory’s claims to be reformulated. Bachelard locates scientific work in this reciprocal activity, where rational organisation becomes effective through technical application and remains open to new determinations from experience (1986, pp. 3–4). This differs from imagining reason as a finished set of instructions that the sciences merely execute. It also gives experimental work an intellectual status that disappears when it is described as the passive collection of facts. Designing a control, establishing a calibration or finding the appropriate variable can change the content of the question under investigation.

The role of instruments

His term phenomenotechnique brings the role of instruments into focus. In the physical sciences on which he concentrates, investigation often involves technically producing the conditions under which a phenomenon becomes accessible to precise study (Bachelard, 1986, pp. 2–4). The James Webb Space Telescope offers a useful contemporary comparison, provided we keep an important distinction in view: a telescope does not produce the nebula it observes. It produces records through a particular arrangement of optics, filters and detectors. The two views of the Pillars of Creation in Figure 2, released in 2022, make the consequences visible. NIRCam, operating in the near infrared, reveals many stars; the MIRI view at longer infrared wavelengths gives a markedly different prominence to the dusty structures (ESA/Webb, 2022a, 2022b). The difference is evidence about how the region appears in those wavelength bands, not a disappearance of the stars between pictures. Their visible colours represent infrared measurements: they are assigned during image processing rather than recorded as the colours a human observer would see (ESA/Webb, n.d.).

The relevance of Bachelard here is deeper than the familiar reminder that images are processed. An observational arrangement determines which differences can become available for scientific interpretation. Its construction does not make the evidence arbitrary; it specifies some of the conditions under which an interpretation can be challenged. For a scientist examining a microscopy image, a spectrum or a remotely sensed map, the corresponding question is what the visible contrast allows one to infer. Which differences in the object would produce it, and which features of the measurement could produce something similar? Those questions belong inside the scientific account of the image. They require knowledge of the instrument and of the object together. I would read the Webb comparison as an invitation to recover that joint reasoning, rather than as an excuse to distrust pictures. A beautiful image can be entirely faithful to its measurements while still needing an exact explanation of what has been made visible.

Methods

There is a practical consequence that deserves a place in every research training programme. Checking that a method has been followed correctly is different from checking the method. Bachelard develops this distinction through levels of intellectual vigilance in Le rationalisme appliqué. At one level we attend to an identifiable event; at another we examine the rigorous application of a method. A further level begins ‘when one scrutinises not merely the application of the method, but the method itself’ (Bachelard, 1986, p. 80; see also pp. 78–79 and Figure 3). Return to the prediction study. Rerunning shared code and obtaining the same score can establish that a calculation is reproducible; it does not establish that the evaluation answers the scientific question. That requires examining what was available during training and why the test represents the situation about which the claim is made. Bachelard did not formulate the technical rules for this evaluation. His distinction helps explain why inspecting those rules is an intellectual task in its own right. A dependable procedure can become especially difficult to question because its execution no longer causes trouble.

Vigilance of this kind also changes the role of other people. The phrase ‘scientific mind’ can suggest a drama taking place inside an isolated head, but Bachelard’s texts resist that reading. Near the end of The Formation of the Scientific Mind, he explicitly turns to the scrutiny of others as a condition for escaping the authority of one’s first impressions (2000, p. 283). In Le rationalisme appliqué, he writes: ‘Scientific thought needs a social reality, the assent of a community of physicists and mathematicians’ (1986, p. 6). The language of assent requires care: agreement is not an instrument that converts a false statement into a true one. The epistemic value lies in the work through which a claim is made understandable, examinable and answerable to people other than its originator. Someone else can reveal that what appeared to be an observation already contained an unacknowledged interpretation. They may also expose a limitation that an entire group has learned to overlook. The social character of objectivity therefore raises questions about how criticism is organised, not merely about how many researchers agree.

An ethical demand

For a working scientist, this introduces an ethical demand into an apparently technical discussion. An error that threatens a valued result may be difficult to encounter with emotional indifference. Bachelard’s vocabulary of a ‘psychoanalysis’ of objective knowledge registers his concern with the attachments that accompany our explanations, including the satisfaction of finding the world familiar or manageable (2000, pp. 21, 282–283). We need not accept that vocabulary as a clinical diagnosis in order to recognise the problem. The scientific issue is what happens to the objection once it becomes uncomfortable. Does it receive an appropriate test? Can a junior colleague state it without having to turn the discussion into a contest of rank? The questions I am drawing from Bachelard concern the conditions under which rectification can occur. They do not make error admirable in itself. Errors become instructive through the work that identifies their grounds and limits, and a research culture deserves credit for making that work possible rather than for displaying a general enthusiasm for failure.

There are limits to the reach of this philosophy, and scientists should insist on them. Bachelard’s strongest examples arise from mathematics, physics and chemistry; their forms of abstraction and experimental control should not be imposed indiscriminately on every discipline. A historical reconstruction of an ecosystem, an observational study and a mathematical proof establish their claims through different kinds of reasoning. Bachelard himself acknowledges that other sciences would require different philosophical analyses, even while according the physical sciences an especially extensive range (1986, p. 7). His sweeping opposition between immediate experience and scientific knowledge also leaves room for argument about the value of trained perception, field familiarity and practical judgement. I would resist turning that opposition into a licence to devalue knowledge acquired outside formal scientific institutions. What travels productively across disciplines is the demand to examine how a claim acquires its warrant, what its methods make visible and where its concepts require revision. The answer must remain specific to the inquiry. A philosophy that foreclosed those differences would reproduce the rigidity Bachelard asks us to overcome.

That demand also motivates the contemporary extension I proposed in ‘Del espíritu científico al espíritu tecnocientífico: Una filosofía del no para el tercer entorno’ (Granados Mateo, 2026). There I ask how Bachelard’s account of formation and vigilance changes when consequential cognitive operations are distributed across simulations, computational procedures and institutions. The question is no longer exhausted by what an individual investigator understands. It also concerns whether a chain of operations can be traced, whether its outputs can be subjected to meaningful comparison, and who has the capacity to initiate a correction. This is a proposed reconstruction of Bachelard’s problem under contemporary conditions, rather than an account of technologies he supposedly anticipated. It extends a difficulty already present in his work: the scientific mind has to be formed in relation to the actual means through which knowledge is produced and assessed.

The open-minded scientist

The familiar image of an open-minded scientist is consequently too undemanding if it refers only to personal willingness. A researcher may welcome criticism while depending on procedures that no member of the immediate team can inspect adequately. What matters then is how the relevant expertise, records and opportunities for independent checking are brought into the inquiry. Responsibility for understanding cannot mean that every participant must know every operation in equal detail; it requires an arrangement in which consequential questions can reach someone capable of investigating them. A useful research meeting might therefore follow one figure backwards: from the claim it supports, through the transformations of the data, to the measurement or observation. This is a proposal for organising criticism, not a method guaranteed to discover every error. Its educational value lies in making colleagues explain the transitions they usually perform without discussion. We form people, but we also construct the situations in which their judgement can be exercised.

To read Bachelard well is to feel the pressure of this distinction in an ordinary piece of research. Before asking whether a result confirms what we expected, we may need to ask whether our way of posing the problem has given a genuine alternative room to appear. Before congratulating ourselves on the reliability of a procedure, we may need to establish what that reliability entitles us to infer. These questions do not suspend investigation in endless philosophical hesitation. Answered with sufficient precision, they help determine the next useful observation, calculation or discussion. The formation of a scientific mind takes place in that movement from an apparently settled understanding towards a better articulated problem. Its most persuasive evidence may be a modest event: a researcher recognises why a familiar explanation has ceased to be adequate, changes the inquiry accordingly, and makes the reasons for that change available to others.

Note on quotations and editions

All English quotations from Bachelard have been translated for this essay. Those from The Formation of the Scientific Mind and The Philosophy of No are translated from the Spanish editions by José Babini and Noemí Fiorito de Labrune respectively; those from Le rationalisme appliqué are translated directly from French. The wording is therefore not attributed to a published English translation. Page references identify the printed pages of the editions listed below, not the page counter of a PDF viewer. Original publication dates are distinguished from the dates of the editions consulted. The empirical cases have their own sources; their connections with Bachelard are interpretations developed in this essay. Hypothetical teaching examples are distinguished from those documented cases.

References

Abeysooriya, Mandhri, Soria, Megan, Kasu, Mary Sravya, and Ziemann, Mark (2021). ‘Gene name errors: Lessons not learned’. PLOS Computational Biology, 17(7), e1008984. https://doi.org/10.1371/journal.pcbi.1008984.

Bachelard, Gaston (1986). Le rationalisme appliqué. 6th edition. Presses Universitaires de France. First published in 1949.

Bachelard, Gaston (2000). La formación del espíritu científico: Contribución a un psicoanálisis del conocimiento objetivo. Translated by José Babini. 23rd Spanish edition. Siglo XXI Editores. Original French work published in 1938.

Bachelard, Gaston (2003). La filosofía del no: Ensayo de una filosofía del nuevo espíritu científico. Translated by Noemí Fiorito de Labrune. First Spanish edition, fourth reprint. Amorrortu. Original French work published in 1940.

Bibliothèque nationale de France (n.d.). ‘Bachelard, Gaston (1884–1962)’. Authority record, Catalogue général. https://catalogue.bnf.fr/ark:/12148/cb11889795z. Source for the biographical dates and teaching appointments; accessed 11 October 2026.

ESA/Webb (2022a). ‘Webb’s Instruments Showcase the Pillars of Creation (NIRCam)’. Image weic2218d, 28 October. https://esawebb.org/images/weic2218d/.

ESA/Webb (2022b). ‘Webb’s Instruments Showcase the Pillars of Creation (MIRI)’. Image weic2218c, 28 October. https://esawebb.org/images/weic2218c/.

ESA/Webb (n.d.). ‘Image Processing’. https://esawebb.org/about/general/image-processing/. Accessed 11 October 2026.

Granados Mateo, J. L. (2026). ‘Del espíritu científico al espíritu tecnocientífico: Una filosofía del no para el tercer entorno’. Revista Iberoamericana de Ciencia, Tecnología y Sociedad – CTS. Advance online publication. https://ojs.revistacts.net/index.php/CTS/article/view/1215.

Kapoor, Sayash, and Narayanan, Arvind (2023). ‘Leakage and the reproducibility crisis in machine-learning-based science’. Patterns, 4(9), 100804. https://doi.org/10.1016/j.patter.2023.100804.

Microsoft (n.d.). ‘Data import and analysis options in Excel’. https://support.microsoft.com/en-us/excel/data-import-and-analysis-options-in-excel. Accessed 11 October 2026.

The post Gaston Bachelard and the making of a scientific mind appeared first on Mapping Ignorance.

Graphene, a honeycomb sheet of carbon atoms just one atom thick, was first isolated in 2004 with adhesive tape and a lump of graphite, work that earned a Nobel Prize in 2010. Large sheets of graphene conduct electricity superbly, but they are not magnetic. Shrink a piece to a few nanometres, however, and its boundary begins to matter. Along one particular kind of edge, theory has long predicted a surprise: carbon, an element not normally associated with magnets, can become magnetic.

A new study shows ¹ that this magnetism-like behaviour can survive in tiny graphene islands sitting on a metal, which had been the main obstacle to studying it.

Why edges matter

Cut graphene across its honeycomb and the edge can take different shapes. In the “zigzag” edge, the outermost atoms form a row of teeth like a saw blade. In the mid-1990s, theorists predicted that electrons crowd into special states hugging such an edge. Every electron also carries spin, a quantum property that makes it behave like a tiny compass needle with two possible orientations. Because electrons repel one another, calculations indicate that those at a zigzag edge can arrange their spins in an orderly pattern, producing magnetism from carbon alone. A mathematical theorem from 1989 adds that triangular flakes with zigzag edges should carry a net magnetic moment. For a small triangle, calculations give a strength equal to six unpaired electron spins.

The difficulty is that edges are fragile. Graphene islands built from molecules on gold surfaces show edge magnetism that is weakened or erased by the metal’s electrons. In one earlier experiment it returned only after a single graphene ribbon was dragged onto an insulating film with a microscope tip, one ribbon at a time. Growing islands by chemical vapour deposition requires a more reactive metal than gold, like nickel, to grip graphene tightly, reshaping its electronic structure and altering its edges.

Growing islands, then cushioning them

The solution the team has found has two stages. First, propylene or ethylene gas is dosed onto a clean nickel crystal in vacuum and heated. The gas molecules break apart, and the carbon atoms assemble into graphene islands. Heating to 500–550 °C yields triangles with zigzag edges, while 600–620 °C gives hexagons. Next, gold is evaporated onto the surface and warmed to about 380–400 °C. Gold atoms slip beneath the islands, a process called intercalation, forming a cushion that separates the graphene from the nickel. Most islands end up sunk into the top layer of gold like tiles set in a floor, while 10–25 percent rest on top.

Two observations indicate that the islands are indeed decoupled. The electrons confined to the gold surface behave almost as they do on bare gold, and the characteristic electrons of graphene itself reappear.

Listening to the edge

The islands were examined with a scanning tunnelling microscope. Its atomically sharp metal tip hovers a fraction of a nanometre above the surface without touching it. Electrons cross the gap through a quantum effect called tunnelling, and the resulting current depends on distance and on which energy levels are available beneath the tip. Invented in 1981 and recognised with a Nobel Prize in 1986, the technique can image individual atoms and, by varying the voltage, reveal where electrons can sit in energy.

At the island edges, the measurements revealed two sharp peaks, one from occupied states and one from empty states, separated by about 1.2 electronvolts. The two peaks were absent in the island interior and on bare gold. Both were squeezed into roughly 0.4 nanometres from the edge, and the signal repeated every 0.246 nanometres, exactly the spacing of the zigzag teeth. The separation stayed close to 1.2 electronvolts for edges between four and nine nanometres long, and across edge types ranged only from 1.10 to 1.42.

Such a pair is what is expected when interactions push electrons of opposite spin to different energies. The gap is the largest reported for graphene edges on a metal and approaches the 1.5 electronvolts measured for ribbons on an insulating salt film.

Magnetism is delicate

Quantum-mechanical simulations reproduced a pair of edge states with spin concentrated on the outermost carbon atoms. Basic versions of the calculation underestimate the gap, but a refined treatment that includes screening by the metal predicts about 1.4 electronvolts, close to the measurement.

The simulations also show why the magnetism is delicate. It depends on how far the edge carbons sit from the gold. Islands embedded in the top gold layer, where the carbon is about 3.4 ångströms away, keep a larger magnetic moment than islands on top, at about 3.1 ångströms. Pushing the atoms closer destroys the moment. Real islands are slightly warped: their corners cling to the metal while the middle of each edge lifts away and stays magnetic. A repeating pattern in the gold, left over from its interface with nickel, adds a gentle ripple of about 70 millielectronvolts to the gap along the edge.

A magnetic interpretation

The experiment recorded energies and positions of electronic states, not the direction of the spins themselves, so the magnetic interpretation rests on theory and on comparison with earlier work. The edge carbon atoms are also thought to carry hydrogen atoms. This is inferred from indirect evidence rather than imaged, but it matters, because calculations for bare edges show the magnetism quenched and no matching pair of peaks.

Chemical vapour deposition is already used to make large graphene sheets, so the method could be scaled up in ways that molecule-by-molecule assembly cannot easily match. The gold layer may also allow the islands to be lifted onto other surfaces. The study shows that, at the nanoscale, behaviour depends not only on which atoms are present but also on exactly how the boundary sits on its surroundings.

Author: César Tomé López is a science writer and the editor of Mapping Ignorance

Disclaimer: Parts of this article may have been copied verbatim or almost verbatim from the referenced research paper/s.

The post Magnetic edges in graphene islands survive on a metal appeared first on Mapping Ignorance.

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.

An international team led by University of California, Berkeley, Lawrence Berkeley National Laboratory and the University of Oxford has uncovered ¹ a novel microscopic mechanism that allows two metal alloys to remain remarkably damage-tolerant at just 20 kelvin, or -253°C.

Most metals become stronger when they are cooled, but some pay a price. The class of metals called body-centred cubic metals (such as iron, tungsten and chromium) have a particular atomic structure which can make them increasingly brittle as temperatures fall. At extremely low temperatures, a crack that might normally be absorbed by the metal bending or deforming can instead cause it to fracture.

The new study discovered that two experimental niobium-titanium-based alloys can avoid this problem even at just 20 kelvin, or -253°C. The metals remained remarkably resistant to cracking, with no measurable transition to brittle behaviour at the lowest temperature tested. The researchers found that this is due to the alloys effectively switching how they deform as they enter the deep freeze.

Normally, bending and deformation of body-centred cubic metals relies heavily on the movement of line-like defects in their crystal structure known as dislocations. At low temperatures, these become increasingly difficult to move, restricting the material’s ability to deform around a crack and making a fracture more likely.

In the alloys studied, however, a different process takes over. The researchers observed nanoscale ‘twinning’, in which small regions of the crystal reorganise into mirror-image orientations, coupled with a local transformation of the crystal structure. When these newly formed structures reach the boundaries between neighbouring crystals, they create concentrated points of stress that trigger new defects in the metal. These defects then allow the material to keep deforming rather than cracking.

The process is a little like a road network responding to frozen, blocked roads by opening new routes. As the conventional routes for deformation become increasingly difficult to use, the formation of twins creates new sites from which the defects needed for deformation can emerge. This helps the metal to absorb the forces building up around a crack, rather than breaking.

The discovery could provide scientists with a new strategy for designing metals capable of operating in exceptionally cold environments, including for energy, transport and aerospace sectors.

To find out how the alloys behaved in such extreme cold, the researchers used the Engin-X instrument at the ISIS Neutron and Muon Source in Oxfordshire. They cooled samples to around -253°C using gaseous helium. They then exposed the samples to a beam of neutrons while a testing machine pulled them from either end, precisely measuring how much force they could withstand and how far they stretched before breaking.

The team then used powerful electron microscopes to examine the tested metal at extremely small scales, allowing them to see the tiny structural changes that had taken place around cracks and inside the material. They also used atom-probe analysis to map the distribution of individual elements, alongside computer modelling to understand how the different mechanisms worked together to prevent brittle failure.

The study involved researchers from the University of California, Berkeley; Lawrence Berkeley National Laboratory; the University of Oxford; the University of Bristol; the ISIS Neutron and Muon Source, Science and Technology Facilities Council; the University of California, Irvine; Pacific Northwest National Laboratory; Texas A&M University; and Nanyang Technological University in Singapore.

The post Metal stays tough at cryogenic temperatures appeared first on Mapping Ignorance.

Once upon a time, in a world very far from ours, there were a group of scientists devoted to the study of bacteria, or prokaryotes, who called themselves ‘biologists’, and a different group of researchers, who studied eukaryote organisms, and who received the name of ‘organismologists’. Organismologists were very proud of the obvious differences between their discipline and that of the biologists, and flagged those differences every time they needed to show that organismology was a science wholly independent of biology, completely ‘irreducible’ to it, and who demanded a totally different set of concepts, principles, and research methods, far more significant than the more or less trivial similarities between both disciplines and their objects. After all, this was a matter of common sense: for example, the concept of an ‘organ’ or ‘digestive system’, or the principles of sexual reproduction, do not apply to bacteria save in a very metaphoric sense; indivual organisms of the same species (for example, siblings) have usually great and important differences between them to an extent that bacteria of the same type have not; and also most multicellular organisms, contrarily to prokaryotes, can often be observed and manipulated without the need of very sophisticated instruments. Organismologists accepted, as a matter of course, that eukaryote organisms had evolved millions of years ago from bacteria, that both types of beings shared a common biochemistry and genetics (though much more complex in the case of eukaryotes), and that both types of beings engaged in lots of mutual relations, so strongly that, in general, bacteria are fundamental for the life of eukaryotes, either as prey, as parasites, or as symbionts. But all this was not relevant enough to turn into ashes the obvious truth that the systems in which multicellular organisms (and even unicellular eukaryotes) consisted were so complex and radically different from the simplicity of bacteria, as to deserve a completely independent field for their systematic study. ‘We would not understand anything important about organisms’, leading organismologits used to proclaim, ‘if we were forced to describe them as mere aggregates of individual bacteria… what very probably they are, of course’.

I think that nearly everybody that knows something about biology in our own world would easily realised that organismologists were right in some sense (eukaryotes, and particularly multicellular organisms, are terribly more complex than bacteria, and ‘irreducible’ to these in many ways), but wrong in other sense (their fields of study are not really that ‘heterogeneous’). I shall argue in this series that the situation of social scientists resisting the ‘naturalisation’ of their disciplines is similar to the resistance of organismologists to be considered as just a ‘branch’ of a deeper and more general discipline called ‘biology’: they are right in pointing to the significant differences between ‘social systems’ and ‘(simpler?) living systems’, but these differences do not ground the conclusion that social science is not a ‘natural’ science. I will also try to argue, however, that, due to some features of social systems, the undavoidable ‘naturalistic nature’ of social sciences gives little comfort to those optimists who might have thought that social sciences could turn ‘more scientific’ by recognising their own naturalness. So, my conclusion will be something like acknowledging that, it is not that  social sciences ‘should’ be naturalised, but that they are ‘natural sciences’ as they are, but that they probably cannot become significantly more ‘successful’ in an empirical or theoretical sense as they are now.

Naturalism (see, e.g., Ross 2011) is usually seen by many social scientists and philosophers of the social sciences as a frightening menace, and often as a completely wrong and misguided approach. It is usually amalgamated with other philosophical monsters, such as positivism or reductionism, and at times even with ‘realism’ or ‘rationalism’ (forming with them a very poisonous combination often referred to as ‘scientism’). I will discuss in this series the often proclaimed conflict of social, or human, sciences with naturalism, reductionism and positivism, and why I think that social scientists should not worry at all at any of these three supposed Behemoths. Let’s start with positivism. So many horrible things have been said about positivism, that now it passes as a matter of fact that one should not be a positivist; particularly, the story is often told that those sciences, both natural, social or hermeneutic, that tried to rigidly follow the precepts of positivism, ended in something similar to a ‘brain death’ state, with nothing interesting to discover about their realm of study. For many of these critics, as well as for some visionary scientists that tried to apply those precepts, ‘positivism’ seemed to be reducible to the boring inductivist strategy of collecting as many single data one can, and then finding out by mere statistical recounting all the ‘regularities’ those data might happen to exhibit. Of course, no real science actually works in this naively Baconian way, nor most self proclaimed (and much less those merely referred to by others as) ‘positivist’ philosophers, have defended such a caricaturesque idea of the scientific method. But even when a more liberal and benevolent description of the positivist admonitions is adopted, most people in the social sciences still reject that positivism is a sensible methodological approach. Just to take an example of this criticism, consider a paper by Isaac Reed (2010). Reed starts by making a distinction between the ‘context of investigation’ (i.e., what the social scientist does) and the ‘context of explanation’ (i.e., what the social scientist is trying to explain, the object she studies). According to Reed, the basic mistake of what he calls ‘positivism’ is the normative relation that is assumed to exist between both contexts:

Positivism begins with a reduction of the context of investigation to the rules of logic and to a precise language whose meanings are transparent to those in the scientific community who use it. It also proposes that the context of explanation be reduced to that which can be observed—and, to a great degree, that which can be measured. Then, through the process of inductive generalization, covering laws are imported from the context of explanation to the context of investigation. (Reed, 2010, 24; bold font words are original’s italics).

Besides the fact that it seems to be inconsistent to assert that “positivism reduces the context of investigation to the rules of logic and to a precise language” just a few lines after having asserted that “the context of investigation would include both of Reichenbach’s contexts (of discovery and justification)” (my italics), for positivists’ ‘context of discovery’ was explicitly taken as not reducible to logical rules, Reed’s explanation is rather obscure about what the ‘importation’ from one context to the other would consists of. Perhaps this simply means that the regularities that are observed in the ‘context of explanation’ are ‘accepted’ or at least ‘proposed’ in scientific papers or books (but, if it is so, what’s wrong with it?). And Reed makes a caricature of the process of ‘importation’ by reducing it to ‘inductive generalization’, overlooking the essential role of hypotheses and theories, as well as of abductive and analogical methods (that only in the most extreme and unrealistic views of science of some positivist philosophers were considered as illegitimate, if at all). The consideration of hypotheses also allows to see that it is an oversimplification to assert that in the context of explanation only ‘what can be observed’ counts: if by ‘observation’ it is understood exclusively what is observable ‘by the naked eye’, then the assumption is false about how science, even social science, works (for all ‘observation’ is ‘theory laden’, and most data are extremely sophisticated entities; consider, for example, empirical data inflation rates); if, instead, ‘observation’ is taken to mean ‘anything that is intersubjectively detectable by some means’, then one does not see what’s wrong in taking into account only ‘observable’ things in what you want to scientifically describe and explain: is Reed suggesting that social scientists must assert things about which there is absolutely no way to empirically determine whether they are as we are asserting? I doubt it.

References

Reed, I., 2010, “Epistemology Contextualized: Social Scientific Knowledge in a Post-Positivist Era.” Sociological Theory. 28, 61-80.

Ross, D., 2011, “Naturalism: the place of society in nature”, in Jarvie, I., and J. Zamora Bonilla (eds.), The SAGE Handbook of Philosophy of Social Sciences, London, Sage, 121-136.

The post Why social sciences are natural ones (1) appeared first on Mapping Ignorance.

In 2004, physicists isolated graphene, a sheet of carbon just one atom thick. It launched the study of two-dimensional (2D) materials: crystals a few atoms thick in which electrons are confined to a plane and can behave quite differently from those in ordinary solids. The family of known and predicted 2D crystals has since grown to thousands of members. Two recent studies show how computation can search that family for the most promising ones, the first for unusual electronic “topology”, the second for crystals well suited to twisting.

A hidden property of electrons

Electrons in a crystal can only have energies within allowed ranges called bands, separated by forbidden gaps. If a gap separates the filled electrons from the empty states, the material is an insulator. In a semimetal, filled and empty bands just touch, so a few electrons can move freely.

Two insulators can still differ at a deeper level, described by topology: a property of the whole set of electron states that cannot change smoothly, only by closing the gap or breaking a protecting symmetry. A doughnut and a coffee mug are topologically alike because each has one hole, and neither can become a sphere without tearing.

The idea took off with the quantum spin Hall effect. Proposed for graphene in 2005, it proved unobservable there because the effect is far too weak. A 2006 proposal for thin layers of mercury telluride was confirmed in 2007. In such a topological insulator the interior blocks current, yet the edges conduct, with electrons of opposite spin travelling in opposite directions, which protects them against many kinds of imperfection.

Symmetry-based methods known as topological quantum chemistry in 2017 made it possible to decide whether a computed electronic structure is topological. Applied to three-dimensional crystals, they pointed to thousands of candidates. Flat crystals lagged behind, with only about 400 known topological or related monolayers.

The first study, “Two-dimensional Topological Quantum Chemistry and Catalog of Topological Materials” ¹, adapts these methods to 2D crystals and applies them to 8,872 entries in two computational databases. It finds 4,073 with nontrivial topology or a related property called obstruction, including 905 topological insulators, 2,129 topological semimetals and 1,003 obstructed atomic insulators, plus 36 close variants. In an obstructed insulator the electrons are centred on empty positions between the atoms rather than on the atoms themselves. The bulk looks ordinary, but cutting the crystal along certain lines produces edge states, and small flakes can carry fractions of an electron’s charge at their corners. The study also calculates edge states for selected materials. Its catalogue includes over 100 monolayers already made in laboratories, but most entries are computer predictions whose properties remain to be tested.

Twisting layers

Stack two atomic sheets and rotate one slightly, and the mismatch creates a large interference pattern, called a moiré pattern, much like two overlapping window screens. Electrons feel this longer repeating pattern, and under the right conditions their bands become nearly flat. Electrons in a flat band have little energy of motion, so their mutual repulsion dominates and strongly correlated behaviour can emerge.

In 2011, calculations predicted that two graphene sheets twisted by about 1.1 degrees, a “magic angle”, would have such flat bands. In 2018, experiments at that angle revealed insulating states caused by electron repulsion, and superconductivity. Twisted layers of other materials, such as MoTe₂ and WSe₂, have since shown many correlated phases, though the mechanisms are still debated.

Most work has focused on these few materials, because most crystals have electronic structures too complicated to predict once twisted. The second study, “2D theoretically twistable material database” ², therefore defines a material as theoretically twistable if its low-energy electrons admit a simple description. In a metal, the relevant electrons must sit at isolated points, as in graphene. In an insulator, the top of the filled band or the bottom of the empty band must stand clear of other bands. Insulators must also have gaps between 0.2 and 3 electronvolts, because larger gaps make electrical contacts difficult. The search was limited to crystals with at most 12 atoms in the repeating cell and four chemical elements.

Starting from the first study’s database, it identifies 61 semimetals and 1,568 insulators. It ranks them with a score and sorts them by lattice shape (about half are hexagonal), by where in momentum space the relevant electrons sit, and by the strength of spin-orbit coupling, the link between an electron’s spin and its motion. These choices shape the moiré pattern, which can resemble honeycomb, kagome or square lattices. Square-lattice moiré systems have been proposed but not yet realized, and could help simulate models relevant to cuprate superconductors.

Calculations on twisted bilayers of SnSe₂, SnS₂, ZrS₂, HfS₂ and Sc₂CCl₂ at a twist of about 7 degrees show isolated flat bands, as the database predicts. Experiments connect the theory to the laboratory. Bulk crystals of SnSe₂, SnS₂, HfS₂, GaTe and ZrNCl were grown. Single layers of several candidates, including SnSe₂, HfSe₂, ZrS₂ and WS₂, were peeled off with a gold-assisted technique that yields larger, cleaner flakes. A dual-gated two-layer SnSe₂ device was built for electrical measurements, and a twisted two-layer HfS₂ device was used for scanning tunnelling measurements near absolute zero.

The direction is clear. Instead of discovering materials by trial and error, it is becoming possible to search systematically for crystals with chosen properties, then test them layer by layer.

Author: César Tomé López is a science writer and the editor of Mapping Ignorance

Disclaimer: Parts of this article may have been copied verbatim or almost verbatim from the referenced research paper/s.

The post Computation sorts thousands of 2D crystals for topology and twisting 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.

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!!

The post When skin cells behave like neurons: The surprising discovery of electrical spikes in epithelia appeared first on Mapping Ignorance.

Who comes to mind when you think of someone emotionally intelligent? Someone who can read a room, defuse an argument, and who knows exactly what to say when a friend is struggling? For many people, that person is a woman. It’s such a widely held belief that popular psychology treats it as fact.

We decided to put that assumption to the test. Our new study reviewed nearly three decades of research, from 1995 to 2023, across 76 countries and more than 600,000 people. We wanted to answer a simple question: are women really more emotionally intelligent than men? The headline answer is yes, on average, women score higher.

But the more useful finding is the type of emotional intelligence women seem to excel at, and what it potentially costs the people who have it.

Emotional intelligence is the ability to recognise, understand and regulate emotions in yourself and in others. When we looked at each element of emotional intelligence, the female advantage turned out to be concentrated in the “others’ feelings” half of the equation.

This means recognising what someone else is feeling, understanding why and helping them manage it. A child has a meltdown, a colleague goes quiet after their idea gets dismissed in a meeting, a negotiation gets tense. Plenty of men are excellent at this too, and plenty of women aren’t; these are averages across hundreds of thousands of people, not predictions about any one person in the room.

Is that gap simply biology? We can’t rule biological or hormonal factors out entirely. But two patterns in our data point elsewhere. The female advantage is far smaller in children than in adults, and this gap has been narrowing across the three decades our data spans. That’s not what you’d expect from something fixed at birth. Although the data in our study cannot tell us the exact reasons these differences emerge, this pattern aligns with what you’d expect from something shaped through practice.

There’s a plausible source for that practice, too. Other research has found that parents use a wider vocabulary of emotion words with daughters than sons, and more often encourage girls to reflect on and articulate their feelings.

Boys, meanwhile, are more often steered toward managing emotions by suppressing them. Their sisters are encouraged to work through it with other people – exactly the kind of social and emotional skill our data shows women excel at.

Put simply, girls appear to get more practice reading and responding to other people’s emotions, starting early in life. Like any skill built through repetition, whether it’s learning a new language or picking up a new instrument, the more you practice, the more natural it becomes.

In fact a 2019 meta-analysis of 58 studies found that emotional intelligence can be trained. Gender didn’t seem to impact the effectiveness of learning how to be more emotionally intelligent.

That’s good news. If emotional intelligence is a trainable skill rather than a fixed trait, the gap isn’t destiny. That means men can benefit from these skills too, including in negotiations and at work.

But there’s a less comfortable implication, too. If women’s advantage here goes unrecognised as a skill and gets treated instead as “how women are”, it becomes very easy to take for granted. The colleague who’s good at defusing tension becomes the one who’s always expected to do so. A capability quietly turns into an obligation: unpaid and unrewarded.

This plays out in the workplace every day. Organisations benefit when employees manage team tension, support stressed colleagues and smooth over conflict. It is work that keeps a team functioning but almost never shows up in a performance review.

Two practical shifts need to follow our study findings. First, employers should name this work and reward it, in reviews and in promotion decisions. Second, invest in developing these abilities in all staff.

The same logic holds outside the office. In families and friendships, the person who remembers a friend is having a bad week and checks in without being asked, who notices when someone stops responding in the group chat, who always asks a family member about their day is doing real work too, and that work is often taken for granted precisely because it looks effortless.

None of this means women don’t have a real strength here. Our data shows a female advantage in emotion abilities. But a strength that’s assumed rather than trained, and expected rather than shared, isn’t something to reinforce. It’s untapped potential for men. The person everyone turns to when things get hard isn’t lucky or simply built that way: they’ve probably had more practice.

What we haven’t done is give everyone else that same opportunity or incentive to practice, or made clear emotional intelligence was a skill worth building in the first place.

 

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post Women are more emotionally intelligent than men, and that’s a problem appeared first on Mapping Ignorance.

Galaxies rotate faster than the matter we can see should allow. Evidence for an unseen, gravitating substance began to build in the 1930s with Fritz Zwicky’s studies of galaxy clusters, and strengthened in the 1970s as Vera Rubin measured how stars orbit within spiral galaxies. Nearly a century later, dark matter is still known almost entirely through its gravity. It holds galaxies together and shaped the web of cosmic structure, yet its nature remains unknown.

Axions, plural

One candidate has an unusual origin. In the late 1970s, physicists proposed a new particle to solve a puzzle in the theory of the strong nuclear force. Frank Wilczek named it the axion, after a laundry detergent, because it promised to clean up the problem. By 1983 theorists had shown that axions about a millionth of an electronvolt in mass could make up cold dark matter, the slow-moving kind. Later, string theory, an attempt to unify gravity with quantum physics, suggested that nature may contain not one axion but many, with masses spread across a vast range. Some of these particles could be extraordinarily light: around 10⁻²² electronvolts, more than 10²⁷ times lighter than an electron.

Such lightness has a striking consequence. The quantum wavelength of a particle grows as its mass shrinks, and for these axions it stretches to astronomical size, comparable to the roughly half-kiloparsec cores of small dwarf galaxies, about 1,600 light-years. Dark matter would then behave less like a swarm of tiny grains and more like a vast wave, often called wave or fuzzy dark matter. Simulations from 2014 showed that every galaxy halo would have a dense central core with a flat density profile, surrounded by a shimmering pattern of interfering waves. The flat core contrasts with the steep central spike predicted by the simplest cold dark matter models. The same wave nature also prevents very small clumps from forming, which may help explain why fewer small satellite galaxies are seen around our Galaxy than those models predict.

There is, however, a serious difficulty. Light from distant quasars passes through clouds of hydrogen that imprint absorption lines on it, the Lyman-alpha forest, which records how matter was clumped on small scales in the early Universe. If a single ultralight species made up all dark matter, these data demand a considerably larger mass. One influential analysis set a lower limit of about 2×10⁻²⁰ electronvolts, some two hundred times heavier than the favoured value. Separately, deep surveys show no clear drop in the number of faint galaxies, which argues against single axions lighter than about 2.5×10⁻²² electronvolts. Both conclusions assume one kind of axion.

A new analysis ¹ asks what changes if dark matter is a mixture. On large scales, it finds, all species feel the same gravitational field, so they respond together and behave much like one effective axion. The mass of that stand-in depends on each species’ mass and on its share of the total dark matter, with lighter species weighing more heavily. The recipe was tested against a detailed calculation for two species, 30% at 10⁻²² electronvolts and 70% at five times that mass. It reproduced the overall suppression of structure well, missing only some fine oscillations, and the resulting difference in predicted mass fluctuations was below 0.1%. If this holds, a multi-axion universe could build large-scale structure much like a single-axion one, with the cut-off in faint galaxies set by the effective mass. The method extends to any number of species and could seed future simulations.

Inside galaxies the picture changes. On these smaller scales the species act independently, and each weaves its own interference pattern with its own grain size. Projected along the line of sight, these produce density fluctuations of only a few percent, yet they bend light enough to blur the lines of greatest magnification in a gravitational lens, called critical curves, into corrugated bands.

The Dragon test

The Dragon Arc offers a test. This is a spiral galaxy, seen when the Universe was half its present age, stretched and magnified by the galaxy cluster Abell 370. In 2025, observations with the James Webb Space Telescope revealed more than forty individual stars in it, each appearing as a brief flicker when magnified by intervening stars in the cluster. Their positions scatter broadly around the critical curve, and an earlier analysis found this matches wave interference. Cold dark matter clumps would instead confine them to a narrower band skewed in the opposite direction. Reading the scatter as a wavelength of about ten parsecs gives a lensing effective mass of roughly 2.4×10⁻²² electronvolts, assuming about 2.4×10¹⁴ solar masses lie inside the critical curve. This differs from the large-scale effective mass, because lensing depends on each species’ share of the projected density. It is not a detection of axions but a model-dependent interpretation. If the two species suggested by dwarf galaxies, about 1.9×10⁻²² and 2.3×10⁻²¹ electronvolts, made up the halo, the lighter would supply roughly 89% of the projected density near the arc.

The most distinctive prediction concerns position. Heavier axions are expected to gather near the centre of a halo and lighter ones to dominate farther out, so the lensing effective mass should change from place to place. A single species cannot do this. The Cosmic Horseshoe, a lensed galaxy where the telescope may find around sixty flickering stars per pointing, could test it by comparing two positions about five arcseconds apart. A mismatch would hint at several species.

This work shows how a mixture of ultralight particles could look like a single one across the cosmos while revealing its true complexity inside galaxies, perhaps reconciling cosmic constraints with galactic evidence. Proving it will require better simulations, more realistic models of multi-species haloes, and further lensing observations.

Author: César Tomé López is a science writer and the editor of Mapping Ignorance

Disclaimer: Parts of this article may have been copied verbatim or almost verbatim from the referenced research paper/s.

The post When dark matter comes in more than one wave appeared first on Mapping Ignorance.

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.

The post Turning cheese whey into value: a sustainable substrate for probiotic biomass production appeared first on Mapping Ignorance.