Hair conditioners typically contain 20-30 ingredients, many derived from petroleum and oleochemicals, raising concerns about sustainability and environmental impact. A new study published in Science Advances, demonstrates that micellar lignin gels can effectively stabilize emulsions with natural oils, reducing the need for synthetic surfactants and complex stabilizers commonly used in commercial formulations. The research team, led by Mika Sipponen at Stockholm University, sought to explore lignin, a common and renewable component in wood biomass, as a multifunctional component for hair conditioning.

“Our findings highlight lignin’s potential as a stabilizer in oil-in-water emulsions, enabling a more natural and sustainable approach to hair conditioning,” says Mika Sipponen. “By using wood-derived lignin directly without any chemical modification, we not only simplify the ingredient list but also eliminate the need for organic solvents, making the process more eco-friendly.”

Comparable to commercial hair conditioners

The lignin gel-based conditioner was tested against a commercial hair conditioner, showing comparable emulsion stability, viscosity, and conditioning performance. A formulation with 6 percent coconut oil effectively lubricated damaged hair, reducing wet combing force by 13 percent, as confirmed by combing force measurements and multiscale microscopy analysis. Importantly, the product was easily rinsed off from paper and skin with cold water despite its dark color, demonstrating practical usability.

New opportunities in cosmetics and food

Ievgen Pylypchuk, who has been instrumental in developing lignin gel as a versatile platform material, highlights its broader potential: “Our lignin gel technology extends beyond personal care applications. Its unique ability to stabilize emulsions and interact with various biomolecules opens opportunities in cosmetics, food, and even biomedical formulations, offering a sustainable alternative to conventional ingredients.”

This innovation paves the way for greener haircare solutions that align with growing consumer demand for sustainable personal care products. The researchers anticipate further exploration of lignin-based formulations for broader applications in the personal care industry.

“We’ve figured out a way for robots to behave more like a material,” said Matthew Devlin, a former doctoral researcher in the lab of UCSB mechanical engineering professor Elliot Hawkes, and the lead author of a paper published in the journal Science. Composed of individual, disk-shaped autonomous robots that look like small hockey pucks, the members of the collective are programmed to assemble themselves together into various forms with different material properties.

Of particular interest to the research team was the challenge of creating a robotic material that could both be stiff and strong, yet be able to flow when a new form is needed. Rather than responding to exterior forces to attain a form, robotic materials ideally would respond to internal signals, Hawkes explained, able to take a shape and hold it, “but also able to selectively flow themselves into a new shape.”

For inspiration, the researchers tapped previous work by Otger Campàs, a former UCSB professor and currently the director of the Physics of Life Excellence Cluster at TU Dresden, on how embryos are physically shaped. “Living embryonic tissues are the ultimate smart materials,” he said. “They have the ability to self-shape, self-heal and even control their material strength in space and time.” While at UCSB, his laboratory discovered that embryos can melt like glass to shape themselves. “To sculpt themselves, cells in embryos can make the tissues switch between fluid and solid states; a phenomenon known as rigidity transitions in physics,” he added.

During the development of an embryo, cells have the remarkable ability to arrange themselves around each other, turning the organism from a blob of undifferentiated cells into a collection of discrete forms — like hands and feet — and of various consistencies, like bones and brain. The researchers concentrated on enabling three biological processes behind these rigidity transitions: the active forces developing cells apply to one another that allow them to move around each other; the biochemical signaling that allow these cells to coordinate their movements in space and time; and their ability to adhere to each other, which ultimately lends the stiffness of the organism’s final form.

In the world of robots, the intracellular forces translate to inter-unit tangential force, enabled by eight motorized gears along each robot’s circular exterior, which allow them to move around each other, pushing off each other, even in tightly packed spaces.

The biochemical signaling, meanwhile, is akin to a global coordinate system. “Each cell ‘knows’ its head and tail, so then it knows which way to squeeze and apply forces,” Hawkes explained. In this way, the collective of cells manages to change the shape of the tissue, such as when they line up next to each other and elongate the body.

In the robots, this feat is accomplished by light sensors on the top of each robot, with polarized filters. When light is shone on these sensors, the polarization of the light tells them which direction to spin its gears and thus how to change shape. “You can just tell them all at once under a constant light field which direction you want them to go, and they can all line up and do whatever they need to do,” Devlin added.

For the cell-cell adhesion the researchers used magnets incorporated into the perimeter of the robotic units, magnets that could be turned to attract any other robot.

In putting the robots through their paces, the researchers found that signal fluctuations — variations in the signals sent to the robots — played a critical role in their ability to take the necessary shapes and formations. “We had previously shown that in living embryos, the fluctuations in the forces that cells generate are key to turning a solid-like tissue into a fluid one. So, we encoded force fluctuations in the robots,” said Campàs.

In the robot collective, the interaction between signal fluctuations and inter-unit forces is the difference between a tightly packed, unmoving collective and a more fluid one. “Basically, as you increase both of those, especially fluctuations, you get a more flowing material,” Devlin said. This allows the collective to change shape. Once in formation, switching off the force fluctuations rigidifies the collective again.

Importantly, these signal fluctuations make it possible for the robot collective to achieve their shape and strength changes with less average power than if the signal were constantly on and the robots were all pushing on each other continuously. “It’s an interesting result that we did not set out looking for, but discovered once we started gathering data on the robot behaviors,” Hawkes said. This is important, he added, for designing robots that may have to run on limited power budgets.

With all this in mind, the researchers were able to tune and control the group of robots to act like a smart material: sections of the group would turn on dynamic forces between robots and fluidize the collective, while in other sections the robots would simply hold to each other to create a rigid material. Modulating these behaviors across the group of robots and over time allowed the researchers to create robotic materials that support heavy loads but can also reshape, manipulate objects, and even self-heal.

Currently, the proof-of-concept robot collective comprises a small number (20) of relatively large units, but simulations conducted by former Campàs laboratory postdoctoral fellow Sangwoo Kim, who is now an assistant professor at EPFL, indicate the system can be scaled to larger numbers of miniaturized units, for a more materials-like aspect.

Beyond robotics, according to the paper, this and robot collectives like it could “enable the study of phase transitions in active matter, the properties of active mechanics in particulate systems and potentially help define hypotheses for biological research.” Combined with current controls and machine learning strategies, working with these robot collectives could yield emergent capabilities in robotic materials that have yet to be discovered and understood.

Their study, published in Endangered Species Research, demonstrates how microphones combined with ML and traditional aerial survey methods can help track right whale populations in Cape Cod Bay, a crucial feeding ground where the whales gather each spring.

To track this endangered species, researchers rely on costly and dangerous surveys by airplanes, or use sound recordings to identify their presence, or absence.

“Using sound recordings to monitor whale populations isn’t new,” said lead author Marissa Garcia of the Cornell Lab of Ornithology’s K. Lisa Yang Center for Conservation Bioacoustics. “What makes our study unique is that we were able to take those recordings and go beyond getting information on the presence or absence of whales to getting an approximate number of whales in an area.”

The team set out an array of marine autonomous recording units (MARU) across Cape Cod Bay to capture right whale sounds.

Following deployment of the MARUs, the team trained, validated and applied a deep-learning model that could automatically detect right whale sounds with 86% precision.

“By analyzing their distinctive upcall vocalizations, we can detect their presence continuously, day and night,” Garcia said. “This kind of round-the-clock monitoring that results from passive acoustic monitoring just isn’t possible with traditional aerial surveys, which can only happen in daylight hours and in good weather.”

Garcia says there’s still some uncertainty in the counts that the team needs to address in future research, but the team is optimistic that monitoring whale vocalizations holds promise for estimating the abundance of right whales to aid in conservation and management efforts.

Having the ability to expand monitoring efforts across larger areas of the ocean will help scientists better assess the species’ population numbers across the full extent of its range. Garcia said right whales have been traditionally thought of as a conservation challenge in New England, but right whales are found all along the East Coast.

“Using passive acoustic data and deep-learning tools, we can expand the area we can safely monitor and keep track of this critically endangered species,” Garcia said.

The work comes at a critical time for North Atlantic right whales, whose population has declined to fewer than 370 individuals due to ship strikes, fishing gear entanglement and changing ocean conditions affecting their food sources.

Analogical reasoning is the ability to draw a comparison between two different things based on their similarities in certain aspects. It is one of the most common methods by which human beings try to understand the world and make decisions. An example of analogical reasoning: cup is to coffee as soup is to (the answer being: bowl)

Large language models like GPT-4 perform well on various tests, including those requiring analogical reasoning. But can AI models truly engage in general, robust reasoning or do they over-rely on patterns from their training data? This study by language and AI experts Martha Lewis (Institute for Logic, Language and Computation at the University of Amsterdam) and Melanie Mitchell (Santa Fe Institute) examined whether GPT models are as flexible and robust as humans in making analogies. ‘This is crucial, as AI is increasingly used for decision-making and problem-solving in the real world’, explains Lewis.

Comparing AI models to human performance

Lewis and Mitchell compared the performance of humans and GPT models on three different types of analogy problems:

1. Letter sequences — Identifying patterns in letter sequences and completing them correctly.
2. Digit matrices — Analyzing number patterns and determining the missing numbers.
3. Story analogies — Understanding which of two stories best corresponds to a given example story.

A system that truly understands analogies should maintain high performance even on variations

In addition to testing whether GPT models could solve the original problems, the study examined how well they performed when the problems were subtly modified. ‘A system that truly understands analogies should maintain high performance even on these variations’, state the authors in their article.

GPT models struggle with robustness

Humans maintained high performance on most modified versions of the problems, but GPT models, while performing well on standard analogy problems, struggled with variations. ‘This suggests that AI models often reason less flexibly than humans and their reasoning is less about true abstract understanding and more about pattern matching’, explains Lewis.

In digit matrices, GPT models showed a significant drop in performance when the position of the missing number changed. Humans had no difficulty with this. In story analogies, GPT-4 tended to select the first given answer as correct more often, whereas humans were not influenced by answer order. Additionally, GPT-4 struggled more than humans when key elements of a story were reworded, suggesting a reliance on surface-level similarities rather than deeper causal reasoning.

On simpler analogy tasks, GPT models showed a decline in performance decline when tested on modified versions, while humans remained consistent. However, for more complex analogical reasoning tasks, both humans and AI struggled.

Weaker than human cognition

This research challenges the widespread assumption that AI models like GPT-4 can reason in the same way humans do. ‘While AI models demonstrate impressive capabilities, this does not mean they truly understand what they are doing’, conclude Lewis and Mitchell. ‘Their ability to generalize across variations is still significantly weaker than human cognition. GPT models often rely on superficial patterns rather than deep comprehension.’

This is a critical warning for the use of AI in important decision-making areas such as education, law, and healthcare. AI can be a powerful tool, but it is not yet a replacement for human thinking and reasoning.

Now research by the University of East Anglia (UEA) in the UK, and Complutense University of Madrid and Seville University in Spain, sheds light on the short-term consequences of workplace bullying on various indicators of sleep.

These include waking up too early (sleep severity), interference with daily life (sleep impact) and dissatisfaction with own sleep (sleep satisfaction).

Writing in the Journal of Interpersonal Violence, the researchers aimed to examine how bullying at work impacts insomnia and to test the mediating role of “anger rumination” — which involves repetitive, persistent thinking about distressing events, such as bullying.

They found the relationship between bullying and sleep increases over time, particularly in relation to sleep onset difficulties, staying asleep and early morning awakening, and is explained by work-related anger felt by the employee and this constant rumination.

They also found evidence of insomnia symptoms being “contagious” between employees and their partners, meaning that the sleep issues (both severity and impact) of one person can influence the other, highlighting how interconnected sleep health can be in relationships.

Lead UK author Professor Ana Sanz-Vergel, from UEA’s Norwich Business School, said: “Our results show that the effects of workplace bullying are time-dependent and accumulative, and go beyond the individual and the work setting, impacting the partner’s sleep as well.

“When individuals experience bullying at work, they may engage in rumination as a way to mentally process and attempt to cope with the negative events. However, this repeated thinking about distressing events can lead to the development of sleep problems such as difficulties in falling asleep, staying asleep, or sleep impact and satisfaction.

“Therefore, rumination can be seen as a maladaptive coping strategy to deal with workplace bullying, meaning that while this type of reflection may initially seem like a way to resolve issues or understand the situation, it can actually lead to more harm in the long run.”

Current knowledge is limited regarding the short-time impact of bullying processes on sleep and the association between workplace bullying and sleep. This is especially important considering that sleep problems are often immediate or short-term responses to stressful situations. There is also limited information about the effects of bullying beyond the individual experiencing it.

To help address this, the team conducted two studies. In the first, 147 employees were followed over five days, and in the second, 139 couples were followed for a period of two months. In both the participants, all from Spain, had to report on their exposure to workplace bullying, work-related anger rumination and different indicators of insomnia.

The first study showed bullying indirectly affected sleep severity through rumination and in the second,also sleep satisfaction and sleep impact, indicating that rumination is a key factor in how bullying affects various aspects of sleep quality.

“It is very interesting that insomnia is contagious,” said Prof Sanz Vergel. “Partners appear to influence each other’s sleep severity and sleep impact, which is not surprising, since one individual’s awakening could cause the other to wake up as well.

“If that’s the case, then both of them can feel that lack of sleep interferes with their daily life. Satisfaction with sleep, however, is less susceptible to this contagion, possibly because it involves more subjective elements.”

The authors recommend that interventions around workplace bullying should be designed both at the organizational and individual levels. From an organizational viewpoint, reducing stressors and fostering a healthy organizational culture become crucial.

At the individual level, interventions should be focused on developing skills to help individuals more effectively deal with stressors.

Prof Sanz Vergel added: “Training on how to disconnect from work has proven efficient and has been shown to minimize the effects of bullying. In addition, couple-oriented prevention programs in the context of the workplace are needed — this could help provide coping strategies to both members of the couple, which would in turn reduce rumination levels and insomnia.”

The research was supported by funding from the Spanish Department of Science and Innovation.

Fires on peatlands, which are carbon-rich, can almost double global fire-driven carbon emissions. Researchers found that despite accounting for only a quarter of the total UK land area that burns each year, dwarfed by moor and heathland, peatland fires have caused up to 90% of annual UK fire-driven carbon emissions since 2001 — with emissions spikes in particularly dry years.

Peat only burns when it’s hot and dry enough — conditions that are occurring more often with climate change. The peatlands of Saddleworth Moor in the Peak District, and Flow Country in northern Scotland, have both been affected by huge wildfires in recent years.

The researchers say land-managers can play an important role in helping to achieve Net Zero climate goals by keeping peatlands wet. This will reduce the likelihood of intense fires and their associated high carbon emissions.

Unlike heather moorland which takes up to twenty years to regrow after a fire, burnt peatland can take centuries to reaccumulate. The loss of this valuable carbon store makes the increasing wildfire frequency on peatlands a real cause for concern.

The researchers also calculated that carbon emissions from fires on UK peatland are likely to rise by at least 60% if the planet warms by 2^oC.

The findings, which are broadly relevant to peatlands in temperate climates, are published today in the journal Environmental Research Letters.

“We found that peatland fires are responsible for a disproportionately large amount of the carbon emissions caused by UK wildfires, which we project will increase even more with climate change,” said Dr Adam Pellegrini in the University of Cambridge’s Department of Plant Sciences, senior author of the study.

He added: “Peatland reaccumulates lost carbon so slowly as it recovers after a wildfire that this process is limited for climate change mitigation. We need to focus on preventing that peat from burning in the first place, by re-wetting peatlands.”

The researchers found that the UK’s ‘fire season’ — when fires occur on natural land — has lengthened dramatically since 2011, from between one and four months in the years 2011-2016 to between six and nine months in the years 2017-2021. The change is particularly marked in Scotland, where almost half of all UK fires occur.

Nine percent of the UK is covered by peatland, which in a healthy condition removes over three million tonnes of carbon dioxide from the atmosphere per year.

The researchers estimate 800,000 tonnes of carbon were emitted from fires on UK peatlands between 2001 and 2021. The 2018 Saddleworth Moor fire emitted 24,000 tonnes of carbon, and the 2019 Flow Country fire emitted 96,000 tonnes of carbon from burning peat.

To get their results, the researchers mapped all UK wildfires over a period of 20 years — assessing where they burn, how much carbon they emit, and how climate change is affecting fires. This involved combining data on fire locations, vegetation type and carbon content, soil moisture, and peat depth. Using UK Met Office data, the also team used simulated climate conditions to predict how wildfires in the UK will change in the future.

The study only considered land where wildfires have occurred in the past, and did not consider the future increases in burned area that are likely to occur with hotter, drier UK summers.

Rewetting peatlands to protecting the carbon they store will require land managers to be incentivised — the researchers say this won’t be easy, but the impact could be big.

“Buffering the UK’s peatlands against really hot, dry summers is a great way to reduce carbon emissions as part of our goal to reach net zero. Humans are capable of incredible things when we’re incentivised to do them,” said Pellegrini.

An average of 5,600 hectares of moor and heathland burns across the UK each year, compared to 2,500 hectares of peatland.

Two hypotheses have been proposed to explain how the brain perceives pain. One is the Estimate Hypothesis, where the brain estimates the intensity of pain based on predictions. The other is the Surprise Hypothesis, where the brain perceives pain as the difference between prediction and reality, otherwise known as the prediction error. In this study, the mechanism underlying the perception of pain were investigated. In the experiment, healthy participants received painful thermal stimuli and reported felt pain intensity while observing painful or non-painful visual stimuli in the virtual reality. The researchers found that the participants strongly perceived pain when the prediction error was large, demonstrating that the Surprise Hypothesis more adequately explains the pain perception mechanism in the brain. The study further confirmed that pain was amplified when unexpected events occurred.

People with chronic pain often experience vague pain-related fears and anxieties. Possibly, this uncertain gap between expectation and reality further increases the perceived intensity of pain. Therefore, reducing the gap between pain expectation and reality or “surprise” is important in reducing pain. A better understanding of pain perception would facilitate the development of new treatments that would enhance recovery from chronic pain and trauma.

This work was supported by JSPS KAKENHI (grant numbers 19H05729 and 23KJ0261).

Thermoelectric coolers, also called solid-state refrigerators, can induce localized cooling by using an electric current to transfer heat from one side of the device to another. Their long lifetimes, invulnerability to leaks, size and shape tunability, and the lack of moving parts (such as circulating liquids) make these devices ideal for diverse cooling applications, such as electronics. However, manufacturing them out of ingots is associated with high costs and generates lots of material waste. In addition, the devices’ performance remains limited.

Now, a team at the Institute of Science and Technology Austria (ISTA), led by Verbund Professor for Energy Sciences and Head of the Werner Siemens Thermoelectric Laboratory Maria Ibáñez, with first author and ISTA postdoc Shengduo Xu, developed high-performance thermoelectric materials out of the 3D printer and used them to build a thermoelectric cooler. “Our innovative integration of 3D printing into thermoelectric cooler fabrication greatly improves manufacturing efficiency and reduces costs,” says Xu. Also, in contrast to previous attempts at 3D printing thermoelectric materials, the present method yields materials with considerably higher performance. ISTA Professor Ibáñez adds, “With commercial-level performance, our work has the potential to extend beyond academia, holding practical relevance and attracting interest from industries seeking real-world applications.”

Pushing the boundaries of thermoelectric technologies

While all materials demonstrate some thermoelectric effect, it is often too negligible to be useful. Materials exhibiting a high enough thermoelectric effect are usually so-called “degenerate semiconductors,” i.e., “doped” semiconductors, to which impurities are introduced intentionally so they behave like conductors. Current state-of-the-art thermoelectric coolers are produced using ingot-based manufacturing techniques — expensive and power-hungry procedures requiring extensive machining processes after production, where a lot of material is wasted. “With our present work, we can 3D print exactly the needed shape of thermoelectric materials. In addition, the resulting devices exhibit a net cooling effect of 50 degrees in the air. This means that our 3D-printed materials perform similarly to ones that are significantly more expensive to manufacture,” says Xu. Thus, the team of ISTA material scientists proposes a scalable and cost-effective production method for thermoelectric materials, circumventing energy-intensive and time-consuming steps.

Printed materials with optimized particle bonding

Beyond applying 3D printing techniques to produce thermoelectric materials, the team designed the inks so that, as the carrier solvent evaporates, effective and robust atomic bonds are formed between grains, creating an atomically connected material network. As a result, the interfacial chemical bonds improve the charge transfer between grains. This explains how the team managed to enhance the thermoelectric performance of their 3D-printed materials while also shedding new light on the transport properties of porous materials. “We employed an extrusion-based 3D printing technique and designed the ink formulation to ensure the integrity of the printed structure and boost particle bonding. This allowed us to produce the first thermoelectric coolers from printed materials with comparable performance to ingot-based devices while saving material and energy,” says Ibáñez.

Medical applications, energy harvesting, and sustainability

Beyond rapid heat management in electronics and wearable devices, thermoelectric coolers could have medical applications, including burn treatment and muscle strain relief. In addition, the ink formulation method developed by the team of ISTA scientists can be adapted for other materials to be used in high-temperature thermoelectric generators — devices that can generate electrical voltage from a temperature difference. According to the team, such an approach could broaden the applicability of thermoelectric generators across various waste energy harvesting systems.

“We successfully executed a full-cycle approach, from optimizing the raw materials’ thermoelectric performance to fabricating a stable, high-performance end-product,” says Ibáñez. Xu adds, “Our work offers a transformative solution for thermoelectric device production and heralds a new era of efficient and sustainable thermoelectric technologies.”