Aurelia Butler

The need for more powerful electronic devices in today’s society is curtailed by our ability to produce highly conductive semiconductors that can withstand the harsh, high temperature fabrication processes of high-powered devices.
Gallium nitride (GaN)-on-diamond shows promise as a next-generation semiconductor material due to the wide band gap of both materials, allowing for high conductivity, and diamond’s high thermal conductivity, positioning it as a superior heat-spreading substrate. There have been attempts at creating a GaN-on-diamond structure by combining the two components with some form of transition or adhesion layer, but in both cases the additional layer significantly interfered with diamond’s thermal conductivity — defeating a key advantage of the GaN-diamond combination.
“There is thus a need for a technology that can directly integrate diamond and GaN,” states Jianbo Liang, Associate Professor of the Graduate School of Engineering, Osaka City University (OCU), and first author of the study, “However, due to large differences in their crystal structures and lattice constants, direct diamond growth on GaN and vice versa is impossible.”
Fusing the two elements together without any intermediate layers, known as Wafer direct bonding, is one way of getting around this mismatch. However, to create a sufficiently high bonding strength many direct bonding methods, the structure needs to be heated to extremely high degrees (typically 500 degrees Celsius) in something called a post-annealing process. This generally causes cracks in a bonded sample of dissimilar materials due to a thermal expansion mismatch — this time defeating any chance of the GaN-diamond structure surviving the extremely high temperatures that high-power devices go through during fabrication.
“In previous work, we used surface activated bonding (SAB) to successfully fabricate various interfaces with diamond at room temperature, all exhibiting a high thermal stability and an excellent practicality,” says research lead Professor Naoteru Shigekawa.
As reported this week in the journal ADVANCED MATERIALS, Liang, Shigekawa and their colleagues from Tohoku University, Saga University, and Adamant Namiki Precision Jewel. Co., Ltd, use the SAB method to successfully bond GaN and diamond, and demonstrate that the bonding is stable even when heated to 1,000 degrees Celsius.
SAB creates highly strong bonds between different materials at room temperature by atomically cleaning and activating the bonding surfaces to react when brought into contact with each other.
As the chemical properties of GaN is completely different from materials the research team has used in the past, after they used SAB to create the GaN-on-diamond material, they used a variety of techniques to test the stability the bonding site — or heterointerface. To characterize the residual stress in the GaN of the heterointerface they used micro-Raman spectroscopy, transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy shed light on the nanostructure and the atomic behavior of the heterointerface, electron energy-loss spectroscopy (EELS) showed the chemical bonding states of the carbon atoms at the heterointerface, and the thermal stability of the heterointerface was tested at 700 degrees Celsius in N2 gas ambient pressure, “which is required for GaN-based power device fabrication processes,” states Liang.
Results showed that at the heterointerface an intermediate layer of approximately 5.3 nm formed that was a mixture of amorphous carbon and diamond in which Ga and N atoms were distributed. As the team increased annealing temperatures, they noticed a decrease in the layer thickness, “due to a direct conversion of amorphous carbon into diamond,” as Shigekawa puts it. After annealing at 1,000 degrees Celsius, the layer decreased to 1.5nm, “suggesting the intermediate layer can be completely removed by optimizing the annealing process,” continues the professor. Although numbers for compressive strength of the heterointerface improved as annealing temperatures increased, they did not match those of GaN-on-diamond structures formed by crystal growth.
However, “as no peeling was observed at the heterointerface after annealing at 1000 degrees Celsius,” states Liang, “these results indicate that the GaN/diamond heterointerface can withstand harsh fabrications processes, with temperature rise in gallium nitride transistors being suppressed by a factor of four.” More

Last season, Kansas City Chiefs quarterback Patrick Mahomes boasted a 66.3 pass-completion percentage.
But Mahomes’ impressive stat pales compared with the accuracy of MAHOMES, or Metal Activity Heuristic of Metalloprotein and Enzymatic Sites, a machine-learning model developed at the University of Kansas — and named in the quarterback’s honor — that could lead to more effective, eco-friendly and cheaper drug therapies and other industrial products.
Instead of targeting wide receivers, MAHOMES differentiates between enzymatic and non-enzymatic metals in proteins with a precision rate of 92.2%. A team at KU recently published results on this machine-learning approach to differentiating enzymes in Nature Communications.
“Enzymes are super interesting proteins that do all the chemistry — an enzyme does a chemical reaction on something to transform it from one thing to another thing,” said corresponding author Joanna Slusky, associate professor of molecular biosciences and computational biology at KU. “Everything that you bring into your body, your body breaks it down and makes it into new things, and that process of breaking down and making into new things — all of that is due to enzymes.”
Slusky and graduate student collaborators in her lab, Ryan Feehan (the Chiefs fan who named MAHOMES) and Meghan Franklin of KU’s Center for Computational Biology, sought to use computers to distinguished between metalloproteins, which don’t perform chemical reactions, and metalloenzymes, which facilitate chemical reactions with amazing power and efficiency.
The problem is metalloproteins and metalloenzymes are in many ways identical. More

Soft robots driven by pressurized fluids could explore new frontiers and interact with delicate objects in ways that traditional rigid robots can’t. But building entirely soft robots remains a challenge because many of the components required to power these devices are, themselves, rigid. 
Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed electrically-driven soft valves to control hydraulic soft actuators. These valves could be used in assistive and therapeutic devices, bio-inspired soft robots, soft grippers, surgical robots, and more.
The research was published in the Proceedings of the National Academy of Sciences (PNAS).  
“Today’s rigid regulation systems considerably limit the adaptability and mobility of fluid-driven soft robots,” said Robert J. Wood, the Harry Lewis and Marlyn McGrath Professor of Engineering and Applied Sciences at SEAS and senior author of the paper. “Here, we have developed soft and lightweight valves to control soft hydraulic actuators that open up possibilities for soft on-board controls for future fluidic soft robots.”
Soft valves aren’t new but so far none have achieved the pressure or flow rates required by many existing hydraulic actuators. To overcome those limitations, the team developed new electrically powered dynamic dielectric elastomer actuators (DEAs). These soft actuators have ultra-high power density, are lightweight, and can run for hundreds of thousands of cycles. The team combined these new dielectric elastomer actuators with a soft channel, resulting in a soft valve for fluidic control. 
“These soft valves have a fast response time and are able to control fluidic pressure and flow rates that match the needs of hydraulic actuators,” said Siyi Xu, a graduate student at SEAS and first author of the paper. “These valves give us fast, powerful control of macro-and small-scale hydraulic actuators with internal volume ranging from hundreds of microliters to tens of milliliters.” 
Using the DEA soft valves, the researchers demonstrated control of hydraulic actuators of different volumes and achieved independent control of multiple actuators powered by a single pressure source. 
“This compact and light-weight DEA valve is capable of unprecedented electrical control of hydraulic actuators, showing the potential for future on-board motion control of soft fluid-driven robots,” said Xu. 
The research was co-authored by Yufeng Chen, Nak-Seung Patrick Hyun, and Kaitlyn Becker. It was supported by the National Science Foundation and the National Robotic Initiative under award CMMI-1830291. 
Story Source:
Materials provided by Harvard John A. Paulson School of Engineering and Applied Sciences. Original written by Leah Burrows. Note: Content may be edited for style and length. More

Some electronics can bend, twist and stretch in wearable displays, biomedical applications and soft robots. While these devices’ circuits have become increasingly pliable, the batteries and supercapacitors that power them are still rigid. Now, researchers in ACS’ Nano Letters report a flexible supercapacitor with electrodes made of wrinkled titanium carbide — a type of MXene nanomaterial — that maintained its ability to store and release electronic charges after repetitive stretching.
One major challenge stretchable electronics must overcome is the stiff and inflexible nature of their energy storage components, batteries and supercapacitors. Supercapacitors that use electrodes made from transitional metal carbides, carbonitrides or nitrides, called MXenes, have desirable electrical properties for portable flexible devices, such as rapid charging and discharging. And the way that 2D MXenes can form multi-layered nanosheets provides a large surface area for energy storage when they’re used in electrodes. However, previous researchers have had to incorporate polymers and other nanomaterials to keep these types of electrodes from breaking when bent, which decreases their electrical storage capacity. So, Desheng Kong and colleagues wanted to see if deforming a pristine titanium carbide MXene film into accordion-like ridges would maintain the electrode’s electrical properties while adding flexibility and stretchability to a supercapacitor.
The researchers disintegrated titanium aluminum carbide powder into flakes with hydrofluoric acid and captured the layers of pure titanium carbide nanosheets as a roughly textured film on a filter. Then they placed the film on a piece of pre-stretched acrylic elastomer that was 800% its relaxed size. When the researchers released the polymer, it shrank to its original state, and the adhered nanosheets crumpled into accordion-like wrinkles.
In initial experiments, the team found the best electrode was made from a 3 µm-thick film that could be repetitively stretched and relaxed without being damaged and without modifying its ability to store an electrical charge. The team used this material to fabricate a supercapacitor by sandwiching a polyvinyl(alcohol)-sulfuric acid gel electrolyte between a pair of the stretchable titanium carbide electrodes. The device had a high energy capacity comparable to MXene-based supercapacitors developed by other researchers, but it also had extreme stretchability up to 800% without the nanosheets cracking. It maintained approximately 90% of its energy storage capacity after being stretched 1,000 times, or after being bent or twisted. The researchers say their supercapacitor’s excellent energy storage and electrical stability is attractive for stretchable energy storage devices and wearable electronic systems.
The authors acknowledge funding from the Key Research and Development Program of Jiangsu Provincial Department of Science and Technology of China, China Postdoctoral Science Foundation and High-Level Entrepreneurial and Innovative Talents Program of Jiangsu Province.
Story Source:
Materials provided by American Chemical Society. Note: Content may be edited for style and length. More

When stretched or deformed, shape memory polymers return to their original shapes after heat or light is applied. These materials show great promise for soft robotics, smart biomedical devices and deployable space structures, but until now they haven’t been able to store enough energy. Now, researchers reporting in ACS Central Science have developed a shape memory polymer that stores almost six times more energy than previous versions.
Shape memory polymers alternate between an original, undeformed state and a secondary, deformed state. The deformed state is created by stretching the polymer and is held in place by molecular changes, such as dynamic bonding networks or strain-induced crystallization, that are reversed with heat or light. The polymer then returns to its original state through the release of stored entropic energy. But it’s been challenging for scientists to make these polymers perform energy-intensive tasks. Zhenan Bao and colleagues wanted to develop a new type of shape memory polymer that stretches into a stable, highly elongated state, allowing it to release large amounts of energy when returning to its original state.
The researchers incorporated 4-,4′-methylene bisphenylurea units into a poly(propylene glycol) polymer backbone. In the polymer’s original state, polymer chains were tangled and disordered. Stretching caused the chains to align and form hydrogen bonds between urea groups, creating supermolecular structures that stabilized the highly elongated state. Heating caused the bonds to break and the polymer to contract to its initial, disordered state.
In tests, the polymer could be stretched up to five times its original length and store up to 17.9 J/g energy — almost six times more energy than previous shape memory polymers. The team demonstrated that the stretched material could use this energy to lift objects 5,000 times its own weight upon heating. They also made an artificial muscle by attaching the pre-stretched polymer to the upper and lower arm of a wooden mannequin. When heated, the material contracted, causing the mannequin to bend its arm at the elbow. In addition to its record-high energy density, the shape memory polymer is also inexpensive (raw materials cost about $11 per lb) and easy to make, the researchers say.
Story Source:
Materials provided by American Chemical Society. Note: Content may be edited for style and length. More

Elastomers, or elastic polymers, materials with high elasticity, are widely used for applications in industries, such as automotive, manufacturing, and oil and gas. The degree of elasticity in these materials, denoted by a parameter known as “Young’s modulus,” depends on the extent of cross-linking between the constituent polymer layers such that higher cross-linking leads to higher rigidity, and, in turn, implies a large Young’s modulus.
Different applications require elastomers of different stiffness. For instance, the desirable Young’s modulus for tires is different from that for pipes and hoses. So far, for conventional elastomers, once the cross-linking of polymer chains takes place, their properties cannot be changed, requiring industries to manufacture different elastomers for different applications. But what if we could prepare a single elastomer with versatile properties for a range of applications?
In a new study published in Polymer, Dr. Mikihiro Hayashi from Nagoya Institute of Technology, Japan, and his colleagues have now done just that. The team has successfully synthesized an elastomer film whose elongation can be controlled by post-preparation photo reaction to suit the desired application, thus, saving time, cost and human resources.
To develop this elastomer, the scientists equipped a polyester (polymer having ester group) with thermoreactive and photoreactive groups, which react to heat and light, respectively. They then followed a two-step process in which the thermoreactive groups first underwent thermal cross-linking and then the photoreactive group formed cross-links in presence of UV light. The scientists observed that the material obtained after thermal cross-linking was soft and flexible, but when further treated with UV light, the material increased in stiffness depending on the time of exposure. In fact, when exposed for 30 minutes, the material’s Young’s Modulus increased by two orders of magnitude!
This unprecedented finding excited the scientists. Dr. Hayashi states, “By developing this elastomer using the dual thermal and photo cross-linking, we proved that post-preparation tuning of tensile strength in materials is possible. We were intrigued to further explore the benefits of this material.”
Accordingly, they designed elastomer films with inhomogeneous patterning of Young’s modulus through selective UV illumination. The scientists accomplished this using horizontal and vertical photomasking slits, creating patterns of soft and rigid sections. On testing the horizontal patterned films under stress, the rigid sections hardly showed any deformation, whereas the soft sections showed 5 times elongation. Surprisingly, however, the vertically patterned films showed excellent toughness and delayed the propagation of cracks. While a crack on a fully rigid film propagates instantly, a crack on the inhomogeneous film stopped on reaching the soft section. The more the number of patterns, the slower was the growth of the crack.
“Our findings can provide useful insights for developing new methodologies for controlling the fracture behavior of elastomers,” comments Dr. Hayashi, speaking of the practical ramifications of their study. “In addition, our technique could help save excess chemical consumption, and solve problems associated with depletion of petroleum resources,” he adds.
These versatile films are sure to find applications in a diverse range of fields and pave the way to sustainable societies!
Story Source:
Materials provided by Nagoya Institute of Technology. Note: Content may be edited for style and length. More

For millions of people with epilepsy and movement disorders such as Parkinson’s disease, electrical stimulation of the brain already is widening treatment possibilities. In the future, electrical stimulation may help people with psychiatric illness and direct brain injuries, such as stroke.
However, studying how brain networks interact with each other is complicated. Brain networks can be explored by delivering brief pulses of electrical current in one area of a patient’s brain while measuring voltage responses in other areas. In principle, one should be able to infer the structure of brain networks from these data. However, with real-world data, the problem is difficult because the recorded signals are complex, and a limited amount of measurements can be made.
To make the problem manageable, Mayo Clinic researchers developed a set of paradigms, or viewpoints, that simplify comparisons between effects of electrical stimulation on the brain. Because a mathematical technique to characterize how assemblies of inputs converge in human brain regions did not exist in the scientific literature, the Mayo team collaborated with an international expert in artificial intelligence (AI) algorithms to develop a new type of algorithm called “basis profile curve identification.”
In a study published in PLOS Computational Biology, a patient with a brain tumor underwent placement of an electrocorticographic electrode array to locate seizures and map brain function before a tumor was removed. Every electrode interaction resulted in hundreds to thousands of time points to be studied using the new algorithm.
“Our findings show that this new type of algorithm may help us understand which brain regions directly interact with one another, which in turn may help guide placement of electrodes for stimulating devices to treat network brain diseases,” says Kai Miller, M.D., Ph.D., a Mayo Clinic neurosurgeon and first author of the study. “As new technology emerges, this type of algorithm may help us to better treat patients with epilepsy, movement disorders like Parkinson’s disease, and psychiatric illnesses like obsessive compulsive disorder and depression.”
“Neurologic data to date is perhaps the most challenging and exciting data to model for AI researchers,” says Klaus-Robert Mueller, Ph.D., study co-author and member of the Google Research Brain Team. Dr. Mueller is co-director of the Berlin Institute for the Foundations of Learning and Data and director of the Machine Learning Group — both at Technical University of Berlin.
In the study, the authors provide a downloadable code package so others may explore the technique. “Sharing the developed code is a core part of our efforts to help reproducibility of research,” says Dora Hermes, Ph.D., a Mayo Clinic biomedical engineer and senior author.
This research was supported by National Institutes of Health’s National Center for Advancing Translational Science Clinical and Translational Science Award, National Institute of Mental Health Collaborative Research in Computational Neuroscience, and the Federal Ministry of Education and Research.
Story Source:
Materials provided by Mayo Clinic. Original written by Susan Barber Lindquist. Note: Content may be edited for style and length. More