Aurelia Butler

Recording the activity of large populations of single neurons in the brain over long periods of time is crucial to further our understanding of neural circuits, to enable novel medical device-based therapies and, in the future, for brain-computer interfaces requiring high-resolution electrophysiological information.
But today there is a tradeoff between how much high-resolution information an implanted device can measure and how long it can maintain recording or stimulation performances. Rigid, silicon implants with many sensors, can collect a lot of information but can’t stay in the body for very long. Flexible, smaller devices are less intrusive and can last longer in the brain but only provide a fraction of the available neural information.
Recently, an interdisciplinary team of researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), in collaboration with The University of Texas at Austin, MIT and Axoft, Inc., developed a soft implantable device with dozens of sensors that can record single-neuron activity in the brain stably for months.
The research was published in Nature Nanotechnology.
“We have developed brain-electronics interfaces with single-cell resolution that are more biologically compliant than traditional materials,” said Paul Le Floch, first author of the paper and former graduate student in the lab of Jia Liu, Assistant Professor of Bioengineering at SEAS. “This work has the potential to revolutionize the design of bioelectronics for neural recording and stimulation, and for brain-computer interfaces.”
Le Floch is currently the CEO of Axoft, Inc, a company founded in 2021 by Le Floch, Liu and Tianyang Ye, a former graduate student and postdoctoral fellow in the Park Group at Harvard. Harvard’s Office of Technology Development has protected the intellectual property associated with this research and licensed the technology to Axoft for further development.
To overcome the tradeoff between high-resolution data rate and longevity, the researchers turned to a group of materials known as fluorinated elastomers. Fluorinated materials, like Teflon, are resilient, stable in biofluids, have excellent long-term dielectic performance, and are compatible with standard microfabrication techniques.

The researchers integrated these fluorinated dielectric elastomers with stacks of soft microelectrodes — 64 sensors in total — to develop a long-lasting probe that is 10,000 times softer than conventional flexible probes made of materials engineering plastics, such as polyimide or parylene C.
The team demonstrated the device in vivo, recording neural information from the brain and spinal cords of mice over the course of several months.
“Our research highlights that, by carefully engineering various factors, it is feasible to design novel elastomers for long-term-stable neural interfaces,” said Liu, who is the corresponding author of the paper. “This study could expand the range of design possibilities for neural interfaces.”
The interdisciplinary research team also included SEAS Professors Katia Bertoldi, Boris Kozinsky and Zhigang Suo.
“Designing new neural probes and interfaces is a very interdisciplinary problem that requires expertise in biology, electrical engineering, materials science, mechanical and chemical engineering,” said Le Floch.
The research was co-authored by Siyuan Zhao, Ren Liu, Nicola Molinari, Eder Medina, Hao Shen, Zheliang Wang, Junsoo Kim, Hao Sheng, Sebastian Partarrieu, Wenbo Wang, Chanan Sessler, Guogao Zhang, Hyunsu Park, Xian Gong, Andrew Spencer, Jongha Lee, Tianyang Ye, Xin Tang, Xiao Wang and Nanshu Lu.
The work was supported by the National Science Foundation through the Harvard University Materials Research Science and Engineering Center Grant No. DMR-2011754. More

What happens when you expose tellurite glass to femtosecond laser light? That’s the question that Gözden Torun at the Galatea Lab, in a collaboration with Tokyo Tech scientists, aimed to answer in her thesis work when she made the discovery that may one day turn windows into single material light-harvesting and sensing devices. The results are published in PR Applied.
Interested in how the atoms in the tellurite glass would reorganize when exposed to fast pulses of high energy femtosecond laser light, the scientists stumbled upon the formation of nanoscale tellurium and tellurium oxide crystals, both semiconducting materials etched into the glass, precisely where the glass had been exposed. That was the eureka moment for the scientists, since a semiconducting material exposed to daylight may lead to the generation of electricity.
“Tellurium being semiconducting, based on this finding we wondered if it would be possible to write durable patterns on the tellurite glass surface that could reliably induce electricity when exposed to light, and the answer is yes,” explains Yves Bellouard who runs EPFL’s Galatea Laboratory. “An interesting twist to the technique is that no additional materials are needed in the process. All you need is tellurite glass and a femtosecond laser to make an active photoconductive material.”
Using tellurite glass produced by colleagues at Tokyo Tech, the EPFL team brought their expertise in femtosecond laser technology to modify the glass and analyze the effect of the laser. After exposing a simple line pattern on the surface of a tellurite glass 1 cm in diameter, Torun found that it could generate a current when exposing it to UV light and the visible spectrum, and this, reliably for months.
“It’s fantastic, we’re locally turning glass into a semiconductor using light,” says Yves Bellouard. “We’re essentially transforming materials into something else, perhaps approaching the dream of the alchemist!.” More

Compared to robots, human bodies are flexible, capable of fine movements, and can convert energy efficiently into movement. Drawing inspiration from human gait, researchers from Japan crafted a two-legged biohybrid robot by combining muscle tissues and artificial materials. Publishing on January 26 in the journal Matter, this method allows the robot to walk and pivot.
“Research on biohybrid robots, which are a fusion of biology and mechanics, is recently attracting attention as a new field of robotics featuring biological function,” says corresponding author Shoji Takeuchi of the University of Tokyo, Japan. “Using muscle as actuators allows us to build a compact robot and achieve efficient, silent movements with a soft touch.”
The research team’s two-legged robot, an innovative bipedal design, builds on the legacy of biohybrid robots that take advantage of muscles. Muscle tissues have driven biohybrid robots to crawl and swim straight forward and make turns — but not sharp ones. Yet, being able to pivot and make sharp turns is an essential feature for robots to avoid obstacles.
To build a nimbler robot with fine and delicate movements, the researchers designed a biohybrid robot that mimics human gait and operates in water. The robot has a foam buoy top and weighted legs to help it stand straight underwater. The skeleton of the robot is mainly made from silicone rubber that can bend and flex to conform to muscle movements. The researchers then attached strips of lab-grown skeletal muscle tissues to the silicone rubber and each leg.
When the researchers zapped the muscle tissue with electricity, the muscle contracted, lifting the leg up. The heel of the leg then landed forward when the electricity dissipated. By alternating the electric stimulation between the left and right leg every 5 seconds, the biohybrid robot successfully “walked” at the speed of 5.4 mm/min (0.002 mph). To turn, researchers repeatedly zapped the right leg every 5 seconds while the left leg served as an anchor. The robot made a 90-degree left turn in 62 seconds. The findings showed that the muscle-driven bipedal robot can walk, stop, and make fine-tuned turning motions.
“Currently, we are manually moving a pair of electrodes to apply an electric field individually to the legs, which takes time,” says Takeuchi. “In the future, by integrating the electrodes into the robot, we expect to increase the speed more efficiently.”
The team also plans to give joints and thicker muscle tissues to the bipedal robot to enable more sophisticated and powerful movements. But before upgrading the robot with more biological components, Takeuchi says the team will have to integrate a nutrient supply system to sustain the living tissues and device structures that allow the robot to operate in the air.
“A cheer broke out during our regular lab meeting when we saw the robot successfully walk on the video,” says Takeuchi. “Though they might seem like small steps, they are, in fact, giant leaps forward for the biohybrid robots.”
This work was supported by JST-Mirai Program, JST Fusion Oriented Research for disruptive Science and Technology, and the Japan Society for the Promotion of Science. More

Our understanding of the world relies greatly on our knowledge of its constituent materials and their interactions. Recent advances in materials science technologies have ratcheted up our ability to identify chemical substances and expanded possible applications.
One such technology is infrared spectroscopy, used for molecular identification in various fields, such as in medicine, environmental monitoring, and industrial production. However, even the best existing tool — the Fourier transform infrared spectrometer or FTIR — utilizes a heating element as its light source. Resulting detector noise in the infrared region limits the devices’ sensitivity, while physical properties hinder miniaturization.
Now, a research team led by Kyoto University has addressed this problem by incorporating a quantum light source. Their innovative ultra-broadband, quantum-entangled source generates a relatively wider range of infrared photons with wavelengths between 2 μm and 5 μm.
“This achievement sets the stage for dramatically downsizing the system and upgrading infrared spectrometer sensitivity,” says Shigeki Takeuchi of the Department of Electronic Science and Engineering.
Another elephant in the room with FTIRs is the burden of transporting mammoth-sized, power-hungry equipment to various locations for testing materials on-site. Takeuchi eyes a future where his team’s compact, high-performance, battery-operated scanners will lead to easy-to-use applications in various fields such as environmental monitoring, medicine, and security.
“We can obtain spectra for various target samples, including hard solids, plastics, and organic solutions. Shimadzu Corporation — our partner that developed the quantum light device — has concurred that the broadband measurement spectra were very convincing for distinguishing substances for a wide range of samples,” adds Takeuchi.
Although quantum entangled light is not new, bandwidth has thus far been limited to a narrow range of 1 μm or less in the infrared region. This new technique, meanwhile, uses the unique properties of quantum mechanics — such as superposition and entanglement — to overcome the limitations of conventional techniques.
The team’s independently developed chirped quasi-phase-matching device generates quantum-entangled light by harnessing chirping — gradually changing an element’s polarization reversal period — to generate quantum photon pairs over a wide bandwidth.
“Improving the sensitivity of quantum infrared spectroscopy and developing quantum imaging in the infrared region are part of our quest to develop real-world quantum technologies,” remarks Takeuchi. More

People who were more skeptical of human-caused climate change or the Black Lives Matter movement who took part in conversation with a popular AI chatbot were disappointed with the experience but left the conversation more supportive of the scientific consensus on climate change or BLM. This is according to researchers studying how these chatbots handle interactions from people with different cultural backgrounds.
Savvy humans can adjust to their conversation partners’ political leanings and cultural expectations to make sure they’re understood, but more and more often, humans find themselves in conversation with computer programs, called large language models, meant to mimic the way people communicate.
Researchers at the University of Wisconsin-Madison studying AI wanted to understand how one complex large language model, GPT-3, would perform across a culturally diverse group of users in complex discussions. The model is a precursor to one that powers the high-profile ChatGPT. The researchers recruited more than 3,000 people in late 2021 and early 2022 to have real-time conversations with GPT-3 about climate change and BLM.
“The fundamental goal of an interaction like this between two people (or agents) is to increase understanding of each other’s perspective,” says Kaiping Chen, a professor of life sciences communication who studies how people discuss science and deliberate on related political issues — often through digital technology. “A good large language model would probably make users feel the same kind of understanding.”
Chen and Yixuan “Sharon” Li, a UW-Madison professor of computer science who studies the safety and reliability of AI systems, along with their students Anqi Shao and Jirayu Burapacheep (now a graduate student at Stanford University), published their results this month in the journal Scientific Reports.
Study participants were instructed to strike up a conversation with GPT-3 through a chat setup Burapacheep designed. The participants were told to chat with GPT-3 about climate change or BLM, but were otherwise left to approach the experience as they wished. The average conversation went back and forth about eight turns.
Most of the participants came away from their chat with similar levels of user satisfaction.

“We asked them a bunch of questions — Do you like it? Would you recommend it? — about the user experience,” Chen says. “Across gender, race, ethnicity, there’s not much difference in their evaluations. Where we saw big differences was across opinions on contentious issues and different levels of education.”
The roughly 25% of participants who reported the lowest levels of agreement with scientific consensus on climate change or least agreement with BLM were, compared to the other 75% of chatters, far more dissatisfied with their GPT-3 interactions. They gave the bot scores half a point or more lower on a 5-point scale.
Despite the lower scores, the chat shifted their thinking on the hot topics. The hundreds of people who were least supportive of the facts of climate change and its human-driven causes moved a combined 6% closer to the supportive end of the scale.
“They showed in their post-chat surveys that they have larger positive attitude changes after their conversation with GPT-3,” says Chen. “I won’t say they began to entirely acknowledge human-caused climate change or suddenly they support Black Lives Matter, but when we repeated our survey questions about those topics after their very short conversations, there was a significant change: more positive attitudes toward the majority opinions on climate change or BLM.”
GPT-3 offered different response styles between the two topics, including more justification for human-caused climate change.
“That was interesting. People who expressed some disagreement with climate change, GPT-3 was likely to tell them they were wrong and offer evidence to support that,” Chen says. “GPT-3’s response to people who said they didn’t quite support BLM was more like, ‘I do not think it would be a good idea to talk about this. As much as I do like to help you, this is a matter we truly disagree on.'”
That’s not a bad thing, Chen says. Equity and understanding comes in different shapes to bridge different gaps. Ultimately, that’s her hope for the chatbot research. Next steps include explorations of finer-grained differences between chatbot users, but high-functioning dialogue between divided people is Chen’s goal.
“We don’t always want to make the users happy. We wanted them to learn something, even though it might not change their attitudes,” Chen says. “What we can learn from a chatbot interaction about the importance of understanding perspectives, values, cultures, this is important to understanding how we can open dialogue between people — the kind of dialogues that are important to society.” More

Chemists of the University of Amsterdam (UvA) have developed an autonomous chemical synthesis robot with an integrated AI-driven machine learning unit. Dubbed ‘RoboChem’, the benchtop device can outperform a human chemist in terms of speed and accuracy while also displaying a high level of ingenuity. As the first of its kind, it could significantly accelerate chemical discovery of molecules for pharmaceutical and many other applications. RoboChem’s first results were published on 25 January in the journal Science.
RoboChem was developed by the group of Prof. Timothy Noël at the UvA’s Van ‘t Hoff Institute for Molecular Sciences. Their paper shows that RoboChem is a precise and reliable chemist that can perform a variety of reactions while producing minimal amounts of waste. Working autonomously around the clock, the system delivers results quickly and tirelessly. Noël: ‘In a week, we can optimise the synthesis of about ten to twenty molecules. This would take a PhD student several months.’ The robot not only yields the best reaction conditions, but also provides the settings for scale-up. ‘This means we can produce quantities that are directly relevant for suppliers to the pharmaceutical industry, for example.’
RoboChem’s ‘brain’
The expertise of the Noël group is in flow chemistry, a novel way of performing chemistry where a system of small, flexible tubes replaces beakers, flasks and other traditional chemistry tools. In RoboChem, a robotic needle carefully collects starting materials and mixes these together in small volumes of just over half a millilitre. These then flow through the tubing system towards the reactor. There, the light from powerful LEDs triggers the molecular conversion by activating a photocatalyst included in the reaction mixture. The flow then continues towards an automated NMR spectrometer that identifies the transformed molecules. These data are fed back in real-time to the computer that controls RoboChem. ‘This is the brain behind RoboChem,’ says Noël. ‘It processes the information using artificial intelligence. We use a machine learning algorithm that autonomously determines which reactions to perform. It always aims for the optimal outcome and constantly refines its understanding of the chemistry.’
Impressive ingenuity
The group put a lot of effort into substantiating RoboChem’s results. All of the molecules now included in the Science paper were isolated and checked manually. Noël says the system has impressed him with its ingenuity: ‘I have been working on photocatalysis for more than a decade now. Still, RoboChem has shown results that I would not have been able to predict. For instance, it has identified reactions that require only very little light. At times I had to scratch my head to fathom what it had done. You then wonder: would we have done it the same way? In retrospect, you see RoboChem’s logic. But I doubt if we would have obtained the same results ourselves. Or not as quickly, at least.’
The researchers also used RoboChem to replicate previous research published in four randomly selected papers. They then determined whether Robochem produced the same — or better — results. ‘In about 80% of the cases, the system produced better yields. For the other 20%, the results were similar,’ Noël says. ‘This leaves me with no doubt that an AI-assisted approach will be beneficial to chemical discovery in the broadest possible sense.’
Breakthroughs in chemistry using AI
According to Noël, the relevance of RoboChem and other ‘computerised’ chemistry also lies in the generation of high-quality data, which will benefit the future use of AI. ‘In traditional chemical discovery only a few molecules are thoroughly researched. Results are then extrapolated to seemingly similar molecules. RoboChem produces a complete and comprehensive dataset where all relevant parameters are obtained for each individual molecule. That provides much more insight.’
Another feature is that the system also records ‘negative’ data. In current scientific practice, most published data only reflects successful experiments. ‘A failed experiment also provides relevant data,’ says Noël. ‘But this can only be found in the researchers’ handwritten lab notes. These are not published and thus unavailable for AI-powered chemistry. RoboChem will change that, too. I have no doubt that if you want to make breakthroughs in chemistry with AI, you will need these kinds of robots.’ More

The branch of mathematics known as topology has become a cornerstone of modern physics thanks to the remarkable — and above all reliable — properties it can impart to a material or system. Unfortunately, identifying topological systems, or even designing new ones, is generally a tedious process that requires exactly matching the physical system to a mathematical model. Researchers at the University of Amsterdam and the École Normale Supérieure of Lyon have demonstrated a model-free method for identifying topology, enabling the discovery of new topological materials using a purely experimental approach.
Topology encompasses the properties of a system that cannot be changed by any ‘smooth deformation’. As you might be able to tell from this rather formal and abstract description, topology began its life as a branch of mathematics. However, over the last few decades physicists have demonstrated that the mathematics underlying topology can have very real consequences. Topological effects can be found in a wide range of physical systems, from individual electrons to large-scale ocean currents.
As a concrete example: in the field of quantum matter, topology rose to fame thanks to so-called topological insulators. These materials do not conduct electricity through their bulk, but electrons move freely along their surfaces or edges. This surface conduction will persist, unhindered by material imperfections, as long as you do not do something drastic like changing the entire atomic structure of the material. Moreover, currents on the surfaces or edges of a topological insulator have a set direction (depending on the electron spin), again enforced by the topological nature of the electronic structure.
Such topological features can have very useful applications, and topology has become one of the frontiers of materials science. Aside from identifying topological materials in nature, parallel research efforts focus on designing synthetic topological materials from the bottom up. Topological edge states of mechanical structures known as ‘metamaterials’ present unmatched opportunities for achieving reliable responses in wave guiding, sensing, computation, and filtering.
Impractical mathematical models
Research in this area is slowed down by the lack of experimental ways to investigate the topological nature of a system. The necessity of matching a mathematical model to a physical system limits research to materials for which we already have a theoretical description, and forms a bottleneck for identifying and designing topological materials. To tackle this issue, Xiaofei Guo and Corentin Coulais of the Machine Materials Laboratory at the University of Amsterdam teamed up with Marcelo Guzmán, David Carpentier and Denis Bartolo of ENS Lyon.
“Until now, most experiments were intended to prove theories or showcase theoretical predictions in journals,” says Guo. “We found a way to measure topologically protected soft or fragile spots in unknown mechanical metamaterials without the need for modelling. Our approach allows for practical exploration and characterisation of material properties without delving into complex theoretical frameworks.”
Poking and prodding

The researchers demonstrated their method with mechanical metamaterials consisting of a network of rotors (rigid rods which can rotate) connected by elastic springs. Topology in these systems can make some regions of such a metamaterial particularly floppy or stiff. Bartolo: “We realised that selectively probing a material locally could give us all the necessary information to unveil soft or fragile spots in the structure, even in regions far removed from our probes. Using this, we developed a highly practical protocol applicable to a diverse range of materials and metamaterials.”
Prodding individual rotors in the metamaterial and tracking the resulting displacements and elongations in the system, the researchers identified different ‘mechanical molecules’: groups of rotors and springs which move as a single unit. In analogy to electrostatic systems, they then determined an effective ‘polarisation’ of each molecule, calculated from the molecules’ movements. This polarisation will suddenly flip direction in the presence of a topological feature, making inherent topology easy to identify.
The researchers applied their method to various mechanical metamaterials, some of which were known from previous studies to be topological, while others were new structures without an associated mathematical model. The results demonstrate that the experimentally determined polarisation is very effective in pointing out topological features.
This model-free approach is not just limited to mechanical systems; the same method could be applied to photonic or acoustic structures. It will make topology accessible to a broader range of physicists and engineers, and will make it easier to construct functional materials that go beyond laboratory demonstrations. More