Aurelia Butler

As researchers make major advances in medical care, they are also discovering that the efficacy of these treatments can be enhanced by individualized approaches. Therefore, clinicians increasingly need methods that can both continuously monitor physiological signals and then personalize responsive delivery of therapeutics.
Need for safe, flexible bioelectronic devices
Implanted bioelectronic devices are playing a critical role in these treatments, but there are a number of challenges that have stalled their widespread adoption. These devices require specialized components for signal acquisition, processing, data transmission, and powering. Up to now, achieving these capabilities in an implanted device has entailed using numerous rigid and non-biocompatible components that can lead to tissue disruption and patient discomfort. Ideally, these devices need to be biocompatible, flexible, and stable in the long term in the body. They also must be fast and sensitive enough to record rapid, low-amplitude biosignals, while still being able to transmit data for external analysis.
Columbia researchers invent first stand-alone, flexible, fully organic bioelectronic device
Columbia Engineering researchers announced today that they have developed the first stand-alone, conformable, fully organic bioelectronic device that can not only acquire and transmit neurophysiologic brain signals, but can also provide power for device operation. This device, about 100 times smaller than a human hair, is based on an organic transistor architecture that incorporates a vertical channel and a miniaturized water conduit demonstrating long-term stability, high electrical performance, and low-voltage operation to prevent biological tissue damage. The findings are outlined in a new study, published today in Nature Materials.
Both researchers and clinicians knew there was a need for transistors that concurrently pose all of these features: low voltage of operation, biocompatibility, performance stability, conformability for in vivo operation; and high electrical performance, including fast temporal response, high transconductance, and crosstalk-free operation. Silicon-based transistors are the most established technologies, but they are not a perfect solution because they are hard, rigid, and unable to establish a very efficient ion interface with the body. ]
The team addressed these issues by introducing a scalable, self-contained, sub-micron IGT (internal-ion-gated organic electrochemical transistor) architecture, the vIGT. They incorporated a vertical channel arrangement that augments the intrinsic speed of the IGT architecture by optimizing channel geometry and permitting a high density arrangement of transistors next to each other — , 155,000of them per centimeter square.

Scalable vGITs are the fastest electrochemical transistors
The vIGTs are composed of biocompatible, commercially available materials that do not require encapsulation in biological environments and are not impaired by exposure to water or ions. The composite material of the channel can be reproducibly manufactured in large quantities and is solution-processible, making it more accessible to a broad range of fabrication processes. They are flexible and compatible with integration into a wide variety of conformable plastic substrates and have long-term stability, low inter-transistor crosstalk, and high-density integration capacity, allowing fabrication of efficient integrated circuits.
“Organic electronics are not known for their high performance and reliability,” said the study’s leader Dion Khodagholy, associate professor of electrical engineering. “But with our new vGIT architecture, we were able to incorporate a vertical channel that has its own supply of ions. This self-sufficiency of ions made the transistor to be particularly fast — in fact, they are currently the fastest electrochemical transistors.”
To push the speed of operation even further, the team used advanced nanofabrication techniques to miniaturize and densify these transistors at submicro-meter scales. Fabrication took place in the cleanroom of the Columbia Nano Initiative.
Collaborating with CUIMC clinicians
To develop the architecture, the researchers first needed to understand the challenges involved with diagnosis and treatment of patients with neurological disorders like epilepsy, as well as the methodologies currently used. They worked with colleagues at the Department of Neurology at Columbia University Irving Medical Center, in particular, with Jennifer Gelinas, assistant professor of neurology, electrical and biomedical engineering and director of the Epilepsy and Cognition Lab.

The combination of high-speed, flexibility. and low-voltage operation enables the transistors to not only be used for neural signal recording but also for data transmission as well as powering the device, leading to a fully conformable implant. The researchers used this feature to demonstrate fully soft and confirmable implants capable of recording and transmitting high resolution neural activity from both outside, on the surface of the brain, as well as inside, deep within the brain.
“This work will potentially open a wide range of translational opportunities and make medical implants accessible to a large patient demographic who are traditionally not qualified for implantable devices due to the complexity and high risks of such procedures,” said Gelinas.
“It’s amazing to think that our research and devices could help physicians with better diagnostics and could have a positive impact on patients’ quality of life,” added the study’s lead author Claudia Cea, who recently completed her PhD and will be a postdoctoral fellow at MIT this fall.
Next steps
The researchers plan next to join forces with neurosurgeons at CUIMC to validate the capabilities of vIGT-based implants in operating rooms. The team expects to develop soft and safe implants that can detect and identify various pathological brain waves caused by neurological disorders. More

Unused solar, wind, and hydroelectric power in the U.S. could support the exponential growth of transactions involving non-fungible tokens (NFTs), Cornell Engineering researchers have found.
Fengqi You, the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering in Cornell Engineering, is corresponding author of “Climate Concerns and the Future of Non-Fungible Tokens: Leveraging Environmental Benefits of the Ethereum Merge,” which published July 10 in Proceedings of the National Academy of Sciences. You’s co-author is Apoorv Lal, graduate student in chemical and biomolecular engineering and a member of the You Research Group.
Processing of NFT transactions, which has increased fourfold over the past five years, was once highly energy-intensive but has been made more sustainable with a recent switch to a more energy-efficient algorithm. But those savings, the researchers said, will be largely offset by the anticipated boom in yearly NFT activity.
Excess renewable energy, due to lack of storage capability, forces grid operators to curtail production. You’s idea would put that unused energy-production potential to good use.
“It’s the same idea as a car sitting in someone’s garage,” You said. “If it’s not being driven, they could lend it to someone for carsharing. In our case, wind, solar and hydro power sources that aren’t being utilized could be used to do something good.”
“Of course, this would be up to the industry and policymakers,” he said, “but technology-wise, we show it’s very feasible because these power sources are there already.”
Their key finding: The increased NFT processing activity could be powered, in part, from un- or underutilized existing power sources. Fifty megawatts of potential hydropower from existing U.S. dams that are not currently used to generate power, or a 15% utilization of wind and solar energy that can’t currently be used or stored from sources in Texas, could be used to power an exponential increase in NFT transactions.

Blockchain technologies, including NFT transactions, offer a high level of security in a variety of applications, but the energy required to process each transaction is problematic in a warming world.
“In the beginning, people only cared about the usefulness of these applications,” Lal said. “But then they started to realize the energy and climate impacts, because the crux of all these applications is the utilization of massive amounts of energy.”
Without any efforts to make NFT transaction processing more sustainable, the authors wrote, their annual emissions will reach an equivalent of 0.37 megatons of carbon dioxide — close to the CO2 emissions from 1 million single-trip flights for a passenger from New York to London.
In September of 2022, the Ethereum blockchain responded to the call for more sustainable trading by switching from an energy-intensive proof of work (PoW) algorithm to a proof of stake (PoS) consensus mechanism, which requires less computing power. Energy consumption decreased drastically following the switch, known as the Ethereum Merge.
Still, the authors wrote, an exponential rise in recorded NFT transactions would translate to more validators operating on the network. Toward the end of this decade, energy consumed by an exponential increase in NFT transactions could be equivalent to that of 100,000 U.S. households.

So even with significantly less energy consumption for individual NFT transactions, the cumulative effect of increased numbers of validators operating on fossil fuel-dominant grids will lead to a further rise in the associated carbon debt.
“By the end of this decade,” You said, “the carbon produced by NFT transactions may be roughly equivalent to that produced in one year by a 600-megawatt coal-fired power plant.”
The authors evaluated two hydroelectric energy carriers — green hydrogen and green ammonia (more energy-dense than hydrogen) — for their viability, noting that their cost savings are influenced by multiple factors, including transportation distances and the utilization levels of available renewable energy sources.
Retrofitting these existing power sources could be challenging, the authors said, but would still be good for energy carriers and the planet.
“NFT processing is very power-hungry,” You said, “so this turns out to be a good way to take advantage of these curtailments.”
You is a senior faculty fellow of the Cornell Atkinson Center for Sustainability and co-director of Cornell University AI for Science Institute.
This research was supported by a grant from the National Science Foundation. More

One of the main ways cells “talk” to each other to coordinate essential biological activities such as muscle contraction, hormone release, neuronal firing, digestion and immune activation is through calcium signaling.
Rice University scientists have used light-activated molecular machines to trigger intercellular calcium wave signals, revealing a powerful new strategy for controlling cellular activity, according to a new study published in Nature Nanotechnology. This technology could lead to improved treatments for people with heart problems, digestive issues and more.
“Most of the drugs developed up to this point use chemical binding forces to drive a specific signaling cascade in the body,” said Jacob Beckham, a chemistry graduate student and lead author on the study. “This is the first demonstration that, instead of chemical force, you can use mechanical force — induced, in this case, by single-molecule nanomachines — to do the same thing, which opens up a whole new chapter in drug design.”
Scientists used small-molecule-based actuators that rotate when stimulated by visible light to induce a calcium-signaling response in smooth muscle cells.
We lack conscious control over many of the critical muscles in our body: The heart is an involuntary muscle, and there is smooth muscle tissue lining our veins and arteries, controlling blood pressure and circulation; smooth muscle lines our lungs and intestines and is involved in digestion and breathing. The ability to intervene in these processes with a molecular-level mechanical stimulus could be game-changing.
“Beckham has shown that we can control, for example, cells’ signaling in a heart muscle, which is really interesting,” said James Tour, Rice’s T. T. and W. F. Chao Professor of Chemistry and a professor of materials science and nanoengineering.

“If you stimulate just one cell in the heart, it will propagate the signal to the neighboring cells, which means you could have targeted, adjustable molecular control over heart function and possibly alleviate arrhythmias,” Tour said.
Activated by quarter-second-long light pulses, the molecular machines allowed scientists to control calcium signaling in a cardiac myocyte cell culture, causing the inactive cells to fire.
“The molecules essentially served as nano-defibrillators, getting these heart muscle cells to start beating,” Beckham said.
The ability to control cell-to-cell communication in muscle tissue could be useful for the treatment of a wide range of diseases characterized by calcium-signaling dysfunction.
“A lot of people who are paralyzed have huge digestive problems,” Tour said. “It would be a big deal if you could alleviate these issues by causing those relevant muscles to fire without any kind of chemical intervention.”
The molecule-sized devices activated the same calcium-based cellular signaling mechanism in a live organism, causing whole-body contraction in a fresh-water polyp, or Hydra vulgaris.

“This is the first example of taking a molecular machine and using it to control an entire functioning organism,” Tour said.
Cellular response varied based on the type and intensity of the mechanical stimulation: Fast, unidirectionally rotating molecular machines elicited intercellular calcium wave signals, while slower speeds and multidirectional rotation did not.
Moreover, adjusting the intensity of the light allowed scientists to control the strength of the cellular response.
“This is mechanical action at the molecular scale,” Tour said. “These molecules spin at 3 million rotations per second, and because we can adjust the duration and intensity of the light stimulus, we have precise spatiotemporal control over this very prevalent cellular mechanism.”
The Tour lab has shown in previous research that light-activated molecular machines can be deployed against antibiotic-resistant infectious bacteria, cancer cells and pathogenic fungi.
“This work expands the capabilities of these molecular machines in a different direction,” Beckham said. “What I love about our lab is that we are fearless when it comes to being creative and pursuing projects in ambitious new directions.”
“We’re currently working towards developing machines activated by light with a better depth of penetration to really actualize the potential of this research. We are also looking to get a better understanding of molecular-scale actuation of biological processes.”
The research was supported by the Discovery Institute, the Robert A. Welch Foundation (C-2017-20190330), the National Science Foundation Graduate Research Fellowship Program, the DEVCOM Army Research Laboratory (Cooperative Agreement W911NF-18-2-0234) and the European Union’s Horizon 2020 (Marie Sklodowska-Curie grant agreement 843116). More

A team from Ames National Laboratory conducted an in-depth investigation of the magnetism of TbMn6Sn6, a Kagome layered topological magnet. They were surprised to find that the magnetic spin reorientation in TbMn6Sn6 occurs by generating increasing numbers of magnetically isotropic ions as the temperature increases.
Rob McQueeney, a scientist at Ames Lab and project lead, explained that TbMn6Sn6has two different magnetic ions in the material, terbium and manganese. The direction of the manganese moments controls the topological state, “But it’s the terbium moment that determines the direction that the manganese points,” he said. “The idea is, you have these two magnetic species and it is the combination of their interactions which controls the direction of the moment.”
In this layered material, there is a magnetic phase transition that occurs as the temperature increases. During this phase transition, the magnetic moments switch from pointing perpendicular to the Kagome layer, or uniaxial, to pointing within the layer, or planar. This transition is called a spin reorientation.
McQueeney explained that in Kagome metals, the spin direction controls the properties of topological or Dirac electrons. Dirac electrons occur where the magnetic bands touch at one point. However, magnetic order causes gapping at the points where the bands are touching. This gapping stabilizes the topological Chern insulator state. “So you can go from a Dirac semimetal to a Chern insulator just by turning the direction of the moment,” he said.
As part of their TbMn6Sn6 investigation, the team performed inelastic neutron scattering experiments at the Spallation Neutron Source to understand how the magnetic interactions in the material drive the spin reorientation transition. McQueeney said that the terbium wants to be uniaxial at low temperatures, while the manganese is planar, so they are at odds.
According to McQueeney, the behavior at very low or very high temperatures is as expected. At low temperatures, the terbium is uniaxial (with electronic orbitals shaped like an ellipsoid). At high temperatures, the terbium is magnetically isotropic (with a spherical orbital shape), which allows the planar Mn to determine the overall moment direction. The team assumed that each terbium orbital would gradually deform from ellipsoidal to spherical. Instead, they found both types of terbium exist at intermediate temperatures, however the population of spherical terbium increases as the temperature increases.
“So, what we did was we determined how the magnetic excitations evolve from this uniaxial state into this easy plane state as a function of temperature. And the long-standing assumption of how it happens is correct,” said McQueeney. “But the nuance is that you can’t treat every terbium as being exactly the same on some timescale. Every terbium site can exist in two quantum states, uniaxial or isotropic, and if I look at a site, it’s either in one state or the other at some instant time. The probability that it’s uniaxial or isotropic depends on temperature.” We call this an orbital binary quantum alloy. More

In a peer-reviewed opinion paper publishing July 10 in the journal Patterns, researchers show that computer programs commonly used to determine if a text was written by artificial intelligence tend to falsely label articles written by non-native language speakers as AI-generated. The researchers caution against the use of such AI text detectors for their unreliability, which could have negative impacts on individuals including students and those applying for jobs.
“Our current recommendation is that we should be extremely careful about and maybe try to avoid using these detectors as much as possible,” says senior author James Zou, of Stanford University. “It can have significant consequences if these detectors are used to review things like job applications, college entrance essays or high school assignments.”
AI tools like OpenAI’s ChatGPT chatbot can compose essays, solve science and math problems, and produce computer code. Educators across the U.S. are increasingly concerned about the use of AI in students’ work and many of them have started using GPT detectors to screen students’ assignments. These detectors are platforms that claim to be able to identify if the text is generated by AI, but their reliability and effectiveness remain untested.
Zou and his team put seven popular GPT detectors to the test. They ran 91 English essays written by non-native English speakers for a widely recognized English proficiency test, called Test of English as a Foreign Language, or TOEFL, through the detectors. These platforms incorrectly labeled more than half of the essays as AI-generated, with one detector flagging nearly 98% of these essays as written by AI. In comparison, the detectors were able to correctly classify more than 90% of essays written by eighth-grade students from the U.S. as human-generated.
Zou explains that the algorithms of these detectors work by evaluating text perplexity, which is how surprising the word choice is in an essay. “If you use common English words, the detectors will give a low perplexity score, meaning my essay is likely to be flagged as AI-generated. If you use complex and fancier words, then it’s more likely to be classified as human written by the algorithms,” he says. This is because large language models like ChatGPT are trained to generate text with low perplexity to better simulate how an average human talks, Zou adds.
As a result, simpler word choices adopted by non-native English writers would make them more vulnerable to being tagged as using AI.
The team then put the human-written TOEFL essays into ChatGPT and prompted it to edit the text using more sophisticated language, including substituting simple words with complex vocabulary. The GPT detectors tagged these AI-edited essays as human-written.
“We should be very cautious about using any of these detectors in classroom settings, because there’s still a lot of biases, and they’re easy to fool with just the minimum amount of prompt design,” Zou says. Using GPT detectors could also have implications beyond the education sector. For example, search engines like Google devalue AI-generated content, which may inadvertently silence non-native English writers.
While AI tools can have positive impacts on student learning, GPT detectors should be further enhanced and evaluated before putting into use. Zou says that training these algorithms with more diverse types of writing could be one way to improve these detectors. More

Brain tissue is one of the most intricate specimens that scientists have arguably ever dealt with. Packed with currently immeasurable amount of information, the human brain is the most sophisticated computational device with its network of around 86 billion neurons. Understanding such complexity is a difficult task, and hence making progress requires technologies to unravel the tiny, complex interactions taking place in the brain at microscopic scales. Imaging is therefore an enabling tool in neuroscience.
The new imaging and virtual reconstruction technology developed by Johann Danzl’s group at ISTA is a big leap in imaging brain activity and is aptly named LIONESS — Live Information Optimized Nanoscopy Enabling Saturated Segmentation. LIONESS is a pipeline to image, reconstruct, and analyze live brain tissue with a comprehensiveness and spatial resolution not possible until now.
“With LIONESS, for the first time, it is possible to get a comprehensive, dense reconstruction of living brain tissue. By imaging the tissue multiple times, LIONESS allows us to observe and measure the dynamic cellular biology in the brain take its course,” says first author Philipp Velicky. “The output is a reconstructed image of the cellular arrangements in three dimensions, with time making up the fourth dimension, as the sample can be imaged over minutes, hours, or days,” he adds.
With LIONESS neuroscientists can image living brain tissue and achieve high-resolution 3D imagery without damaging the living sample.
Collaboration and AI the Key
The strength of LIONESS lies in refined optics and in the two levels of deep learning — a method of Artificial Intelligence — that make up its core: the first enhances the image quality and the second identifies the different cellular structures in the dense neuronal environment.

The pipeline is a result of a collaboration between the Danzl group, Bickel group, Jonas group, Novarino group, and ISTA’s Scientific Service Units, as well as other international collaborators. “Our approach was to assemble a dynamic group of scientists with unique combined expertise across disciplinary boundaries, who work together to close a technology gap in the analysis of brain tissue,” Johann Danzl of ISTA says.
Surpassing hurdles
Previously it was possible to get reconstructions of brain tissue by using Electron Microscopy. This method images the sample based on its interactions with electrons. Despite its ability to capture images at a few nanometers — a millionth of a millimeter — resolution, Electron Microscopy requires a sample to be fixed in one biological state, which needs to be physically sectioned to obtain 3D information. Hence, no dynamic information can be obtained.
Another previously known technique of Light Microscopy allows observation of living systems and record intact tissue volumes by slicing them “optically” rather than physically. However, Light Microscopy is severely hampered in its resolving power by the very properties of the light waves it uses to generate an image. Its best-case resolution is a few hundred nanometers, much too coarse-grained to capture important cellular details in brain tissue.
Using Super-resolution Light Microscopy scientists can break this resolution barrier. Recent work in this field, dubbed SUSHI (Super-resolution Shadow Imaging), showed that applying dye molecules to the spaces around cells and applying the Nobel Prize-winning super-resolution technique STED (Stimulated Emission Depletion) microscopy reveals super-resolved ‘shadows’ of all the cellular structures and thus visualizes them in the tissue. Nevertheless, it has been impossible to image entire volumes of brain tissue with resolution enhancement that matches the brain tissue’s complex 3D architecture. This is because increasing resolution also entails a high load of imaging light on the sample, which may damage or ‘fry’ the subtle, living tissue.

Herein lies the prowess of LIONESS, having been developed for, according to the authors, “fast and mild” imaging conditions, thus keeping the sample alive. The technique does so while providing isotropic super-resolution — meaning that it is equally good in all three spatial dimensions — that allows visualization of the tissue’s cellular components in 3D nanoscale resolved detail.
LIONESS collects only as little information from the sample as needed during the imaging step. This is followed by the first deep learning step to fill in additional information on the brain tissue’s structure in a process called Image Restoration. In this innovative way, it achieves a resolution of around 130 nanometers, while being gentle enough for imaging of living brain tissue in real-time. Together, these steps allow for a second step of deep learning, this time to make sense of the extremely complex imaging data and identify the neuronal structures in an automated manner.
Homing In
“The interdisciplinary approach allowed us to break the intertwined limitations in resolving power and light exposure to the living system, to make sense of the complex 3D data, and to couple the tissue’s cellular architecture with molecular and functional measurements,” says Danzl.
For virtual reconstruction, Danzl and Velicky teamed up with visual computing experts: the Bickel group at ISTA and the group led by Hanspeter Pfister at Harvard University, who contributed their expertise in automated segmentation — the process of automatically recognizing the cellular structures in the tissue — and visualization, with further support by ISTA’s image analysis staff scientist Christoph Sommer. For sophisticated labeling strategies, neuroscientists and chemists from Edinburgh, Berlin, and ISTA contributed. Consequently, it was possible to bridge functional measurements, i.e. to read out the cellular structures together with biological signaling activity in the same living neuronal circuit. This was done by imaging Calcium ion fluxes into cells and measuring the cellular electrical activity in collaboration with the Jonas group at ISTA. The Novarino group contributed human cerebral organoids, often nicknamed mini-brains that mimic human brain development. The authors underline that all of this was facilitated through expert support by ISTA’s top-notch scientific service units.
Brain structure and activity are highly dynamic; its structures evolve as the brain performs and learns new tasks. This aspect of the brain is often referred to as “plasticity.” Hence, observing the changes in the brain’s tissue architecture is essential to unlocking the secrets behind its plasticity. The new tool developed at ISTA shows potential for understanding the functional architecture of brain tissue and potentially other organs by revealing the subcellular structures and capturing how these might change over time. More

Smart textiles offer many potential wearable technology applications, from therapeutics to sensing to communication. For such intelligent textiles to function effectively, they need to be strong, stretchable, and electrically conductive. However, fabricating fibres that possess these three properties is challenging and requires complex conditions and systems.
Drawing inspiration from how spiders spin silk to make webs, a team of researchers led by Assistant Professor Swee-Ching Tan from the Department of Materials Science and Engineering under the National University of Singapore’s College of Design and Engineering, together with their international collaborators, have developed an innovative method of producing soft fibres that possess these three key properties, and at the same time can be easily reused to produce new fibres. The fabrication process can be carried out at room temperature and pressure, and uses less solvent as well as less energy, making it an attractive option for producing functional soft fibres for various smart applications.
“Technologies for fabricating soft fibres should be simple, efficient and sustainable to meet the high demand for smart textile electronics. Soft fibres created using our spider-inspired method of spinning has been demonstrated to be versatile for various smart technology applications — for example, these functional fibres can be incorporated into a strain-sensing glove for gaming purposes, and a smart face mask to monitor breathing status for conditions such as obstructive sleep apnea. These are just some of the many possibilities,” said Asst Prof Tan.
Their innovation was demonstrated and outlined in their paper that was published in scientific journal Nature Electronics on 27 April 2023.
Spinning a web of soft fibres
Conventional artificial spinning methods to fabricate synthetic fibres require high pressure, high energy input, large volumes of chemicals, and specialised equipment. Moreover, the resulting fibres typically have limited functions.

In contrast, the spider silk spinning process is highly efficient and can form strong and versatile fibres under room temperature and pressure. To address the current technological challenges, the NUS team decided to emulate this natural spinning process to create one-dimensional (1D) functional soft fibres that are strong, stretchable, and electrically conductive. They identified two unique steps in spider silk formation that they could mimic.
Spider silk formation involves the change of a highly concentrated protein solution, known as a silk dope, into a strand of fibre. The researchers first identified that the protein concentration and interactions in the silk dope increase from dope synthesis to spinning. The second step identified was that the arrangement of proteins within the dope changes when triggered by external factors to help separate the liquid portion from the silk dope, leaving the solid part — the spider silk fibres. This second step is known as liquid-solid phase separation.
The team recreated the two steps and developed a new spinning process known as the phase separation-enabled ambient (PSEA) spinning approach.
The soft fibres were spun from a viscous gel solution composed of polyacrylonitrile (PAN) and silver ions — referred to as PANSion — dissolved in dimethylformamide (DMF), a common solvent. This gel solution is known as the spinning dope, which forms into a strand of soft fibre through the spinning process when the gel is pulled and spun under ambient conditions.
Once the PANSion gel is pulled and exposed to air, water molecules in the air act as a trigger to cause the liquid portion of the gel to separate in the form of droplets from the solid portion of the gel, this phenomenon is known as the nonsolvent vapour-induced phase separation effect. When separated from the solid fibre, the droplets of the liquid portion are removed by holding the fibre vertically or at an angle for gravity to do its work.

“Fabrication of 1D soft fibres with seamless integration of all-round functionalities is much more difficult to achieve and requires complicated fabrication or multiple post-treatment processes. This innovative method fulfils an unmet need to create a simple yet efficient spinning approach to produce functional 1D soft fibres that simultaneously possess unified mechanical and electrical functionalities,” said Asst Prof Tan.
Three properties, one method
The biomimetic spinning process combined with the unique formulation of the gel solution allowed the researchers to fabricate soft fibres that are imbued with three key properties — strong, stretchable, and electrically conductive.
Researchers tested the mechanical properties, strength, and elasticity, of the PANSion gel through a series of stress tests and demonstrated that this remarkable innovation possessed excellent strength and elasticity. These tests also allowed the researchers to deduce that the formation of strong chemical networks between metal-based complexes within the gel is responsible for its mechanical properties.
Further analysis of the PANSion soft fibres at the molecular level confirmed its electrical conductivity and showed that the silver ions present in the PANSion gel contributed to the electrical conductivity of the soft fibres.
The team then concluded that PANSion soft fibres fulfils all the properties that would allow it to be versatile and potentially be used in a wide range of smart technology applications.
Potential applications and next steps
The team demonstrated the capabilities of the PANSion soft fibres in a number of applications, such as communication and temperature sensing. PANSion fibres were sewn to create an interactive glove that exemplified a smart gaming glove. When connected to a computer interface, the glove could successfully detect human hand gestures and enable a user to play simple games.
PANSion fibres could also detect changes in electrical signals that could be used as a form of communication like Morse code. In addition, these fibres could sense temperature changes, a property that can potentially be capitalised to protect robots from environments with extreme temperatures. Researchers also sewed PANSion fibres into a smart face mask for monitoring the breathing activities of the mask wearer.
On top of the wide range of potential applications of PANSion soft fibres, this innovative discovery earns points in sustainability. PANSion fibres could be recycled by dissolving in DMF, allowing it to be converted back into a gel solution for spinning new fibres. A comparison with other current fibre-spinning methods revealed that this new spider-inspired method consumes significantly lower amounts of energy and requires lower volume of chemicals.
Further to this cutting-edge discovery, the research team will continue to work on improving the sustainability of the PANSion soft fibres throughout its production cycle, from the raw materials to recycling the final product. More