Aurelia Butler

Scientists studying two different configurations of bilayer graphene — the two-dimensional (2-D), atom-thin form of carbon — have detected electronic and optical interlayer resonances. In these resonant states, electrons bounce back and forth between the two atomic planes in the 2-D interface at the same frequency. By characterizing these states, they found that twisting one of the graphene layers by 30 degrees relative to the other, instead of stacking the layers directly on top of each other, shifts the resonance to a lower energy. From this result, just published in Physical Review Letters, they deduced that the distance between the two layers increased significantly in the twisted configuration, compared to the stacked one. When this distance changes, so do the interlayer interactions, influencing how electrons move in the bilayer system. An understanding of this electron motion could inform the design of future quantum technologies for more powerful computing and more secure communication.
“Today’s computer chips are based on our knowledge of how electrons move in semiconductors, specifically silicon,” said first and co-corresponding author Zhongwei Dai, a postdoc in the Interface Science and Catalysis Group at the Center for Functional Nanomaterials (CFN) at the U.S. Department of Energy (DOE)’s Brookhaven National Laboratory. “But the physical properties of silicon are reaching a physical limit in terms of how small transistors can be made and how many can fit on a chip. If we can understand how electrons move at the small scale of a few nanometers in the reduced dimensions of 2-D materials, we may be able to unlock another way to utilize electrons for quantum information science.”
At a few nanometers, or billionths of a meter, the size of a material system is comparable to that of the wavelength of electrons. When electrons are confined in a space with dimensions of their wavelength, the material’s electronic and optical properties change. These quantum confinement effects are the result of quantum mechanical wave-like motion rather than classical mechanical motion, in which electrons move through a material and are scattered by random defects.
For this research, the team selected a simple material model — graphene — to investigate quantum confinement effects, applying two different probes: electrons and photons (particles of light). To probe both electronic and optical resonances, they used a special substrate onto which the graphene could be transferred. Co-corresponding author and CFN Interface Science and Catalysis Group scientist Jurek Sadowski had previously designed this substrate for the Quantum Material Press (QPress). The QPress is an automated tool under development in the CFN Materials Synthesis and Characterization Facility for the synthesis, processing, and characterization of layered 2-D materials. Conventionally, scientists exfoliate 2-D material “flakes” from 3-D parent crystals (e.g., graphene from graphite) on a silicon dioxide substrate several hundred nanometers thick. However, this substrate is insulating, and thus electron-based interrogation techniques don’t work. So, Sadowski and CFN scientist Chang-Yong Nam and Stony Brook University graduate student Ashwanth Subramanian deposited a conductive layer of titanium oxide only three nanometers thick on the silicon dioxide substrate.
“This layer is transparent enough for optical characterization and determination of the thickness of exfoliated flakes and stacked monolayers while conductive enough for electron microscopy or synchrotron-based spectroscopy techniques,” explained Sadowski.
In the Charlie Johnson Group at the University of Pennsylvania — Rebecca W. Bushnell Professor of Physics and Astronomy Charlie Johnson, postdoc Qicheng Zhang, and former postdoc Zhaoli Gao (now an assistant professor at the Chinese University of Hong Kong) — grew the graphene on metal foils and transferred it onto the titanium oxide/silicon dioxide substrate. When graphene is grown in this way, all three domains (single layer, stacked, and twisted) are present. More

Researchers have developed a lightweight optical system for 3D inspection of surfaces with micron-scale precision. The new measurement tool could greatly enhance quality control inspection for high-tech products including semiconductor chips, solar panels and consumer electronics such as flat panel televisions.
Because vibrations make it difficult to capture precision 3D measurements on the production line, samples are periodically taken for analysis in a lab. However, any defective products made while waiting for results must be discarded.
To create a system that could operate in the vibration-prone environment of an industrial manufacturing plant, researchers headed by Georg Schitter from Technische Universität Wien in Austria combined a compact 2D fast steering mirror with a high precision 1D confocal chromatic sensor.
“Robot-based inline inspection and measurement systems such as what we developed can enable 100% quality control in industrial production, replacing current sample-based methods,” said Ernst Csencsics, who co-led the research team with Daniel Wertjanz. “This creates a production process that is more efficient because it saves energy and resources.”
As described in The Optical Society (OSA) journal Applied Optics, the new system is designed to be mounted on tracking platform placed on a robotic arm for contactless 3D measurements of arbitrary shapes and surfaces. It weighs just 300 grams and measures 75 x 63 x 55 millimeters cubed, which is about the size of an espresso cup.
“Our system can measure 3D surface topographies with unprecedented combination of flexibility, precision, and speed,” said Wertjanz, who is pursuing a PhD on this research topic. “This creates less waste because manufacturing problems can be identified in real-time, and processes can be quickly adapted and optimized.”
From lab to fab More

Researchers have developed a mathematical model that can predict the optimum exercise regime for building muscle.
The researchers, from the University of Cambridge, used methods of theoretical biophysics to construct the model, which can tell how much a specific amount of exertion will cause a muscle to grow and how long it will take. The model could form the basis of a software product, where users could optimise their exercise regimes by entering a few details of their individual physiology.
The model is based on earlier work by the same team, which found that a component of muscle called titin is responsible for generating the chemical signals which affect muscle growth.
The results, reported in the Biophysical Journal, suggest that there is an optimal weight at which to do resistance training for each person and each muscle growth target. Muscles can only be near their maximal load for a very short time, and it is the load integrated over time which activates the cell signalling pathway that leads to synthesis of new muscle proteins. But below a certain value, the load is insufficient to cause much signalling, and exercise time would have to increase exponentially to compensate. The value of this critical load is likely to depend on the particular physiology of the individual.
We all know that exercise builds muscle. Or do we? “Surprisingly, not very much is known about why or how exercise builds muscles: there’s a lot of anecdotal knowledge and acquired wisdom, but very little in the way of hard or proven data,” said Professor Eugene Terentjev from Cambridge’s Cavendish Laboratory, one of the paper’s authors.
When exercising, the higher the load, the more repetitions or the greater the frequency, then the greater the increase in muscle size. However, even when looking at the whole muscle, why or how much this happens isn’t known. The answers to both questions get even trickier as the focus goes down to a single muscle or its individual fibres. More

The miniaturization of microelectronic sensor technology, microelectronic robots or intravascular implants is progressing rapidly. However, it also poses major challenges for research. One of the biggest is the development of tiny but efficient energy storage devices that enable the operation of autonomously working microsystems — in more and more smaller areas of the human body for example. In addition, these energy storage devices must be bio-compatible if they are to be used in the body at all. Now there is a prototype that combines these essential properties. The breakthrough was achieved by an international research team led by Prof. Dr. Oliver G. Schmidt, Professorship of Materials Systems for Nanoelectronics at Chemnitz University of Technology, initiator of the Center for Materials, Architectures and Integration of Nanomembranes (MAIN) at Chemnitz University of Technology and director at the Leibniz Institute for Solid State and Materials Research (IFW) Dresden. The Leibniz Institute of Polymer Research Dresden (IPF) was also involved in the study as a cooperation partner.
In the current issue of Nature Communication, the researchers report on the smallest microsupercapacitors to date, which already functions in (artificial) blood vessels and can be used as an energy source for a tiny sensor system to measure pH.
This storage system opens up possibilities for intravascular implants and microrobotic systems for next-generation biomedicine that could operate in hard-to-reach small spaces deep inside the human body. For example, real-time detection of blood pH can help predict early tumor growing. “It is extremely encouraging to see how new, extremely flexible, and adaptive microelectronics is making it into the miniaturized world of biological systems,” says research group leader Prof. Dr. Oliver G. Schmidt, who is extremely pleased with this research success.
The fabrication of the samples and the investigation of the biosupercapacitor were largely carried out at the Research Center MAIN at Chemnitz University of Technology.
“The architecture of our nano-bio supercapacitors offers the first potential solution to one of the biggest challenges — tiny integrated energy storage devices that enable the self-sufficient operation of multifunctional microsystems,” says Dr. Vineeth Kumar, researcher in Prof. Schmidt’s team and a research associate at the MAIN research center.
Smaller than a speck of dust — voltage comparable to a AAA battery
Ever smaller energy storage devices in the submillimeter range — so-called “nano-supercapacitors” (nBSC) — for even smaller microelectronic components are not only a major technical challenge, however. This is because, as a rule, these supercapacitors do not use biocompatible materials but, for example, corrosive electrolytes and quickly discharge themselves in the event of defects and contamination. Both aspects make them unsuitable for biomedical applications in the body. So-called “biosupercapacitors (BSCs)” offer a solution. They have two outstanding properties: they are fully biocompatible, which means that they can be used in body fluids such as blood and can be used for further medical studies. More

An undergraduate student from the University of Surrey has discovered a way to suppress hot-carrier effects that have plagued devices that use thin-film transistor architecture — such as smartwatches and solar panels.
Hot-carrier effects occur when unwanted electron energy builds up in certain regions of transistors, resulting in devices performing unreliably.
In her final-year project, Lea Motte studied a new device, the multimodal transistor, an alternative to conventional thin-film transistors, invented and developed by PhD candidate Eva Bestelink and supervisor Dr Radu Sporea at Surrey.
Lea used a defining feature of multimodal transistors, the separation of controls for introducing electrons into the device and allowing them to move across the transistor. Through computer simulations, Lea discovered that choosing the right voltage to apply to the transport control region can prevent unwanted hot-carrier effects. In addition, it ensures that the current through the transistor remains constant in a wide range of operating conditions.
In a paper published in the journal Advanced Electronic Materials, PhD student Eva Bestelink systematically studies Lea’s discovery of the unusual behaviour in multimodal transistors by confirming it with measurements in microcrystalline silicon transistors and performing extensive device simulations to understand the device physics that underpins its unique ability.
This discovery means that future technologies that use multimodal transistors could be more power-efficient, and it could lead to high-performance amplifiers, which are essential for measuring signals from environmental and biological sensors.
Eva Bestelink, lead author of the study from the University of Surrey, said:
“We now have a better understanding of what the multimodal transistor can offer when made with materials that cause numerous challenges to regular devices.
“For circuit designers, this work offers insight into how to operate the device for optimum performance. In the long term, the multimodal transistor offers an alternative for emerging high-performance materials, where traditional solutions are no longer applicable.”
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In a step toward a future of higher performance memory devices, researchers from National Taiwan Normal University and Kyushu University have developed a new device that needs only a single semiconductor known as perovskite to simultaneously store and visually transmit data.
By integrating a light-emitting electrochemical cell with a resistive random-access memory that are both based on perovskite, the team achieved parallel and synchronous reading of data both electrically and optically in a ‘light-emitting memory.’
At the most fundamental level, digital data is stored as a basic unit of information known as a bit, which is often represented as either a one or a zero. Thus, the pursuit of better data storage comes down to finding more efficient ways to store and read these ones and zeros.
While flash memory has become extremely popular, researchers have been searching for alternatives that could further improve speed and simplify fabrication.
One candidate is nonvolatile resistive random-access memory, or RRAM. Instead of storing charge in transistors like in flash memory, resistive memory uses materials that can switch between states of high and low resistance to represent ones and zeros.
“However, the electrical measurements needed to check the resistance and read zeros and ones from RRAM can limit the overall speed,” explains Chun-Chieh Chang, professor at National Taiwan Normal University and one of the corresponding authors of the study published in Nature Communications. More

The quantum computing market is projected to reach $65 billion by 2030, a hot topic for investors and scientists alike because of its potential to solve incomprehensibly complex problems.
Drug discovery is one example. To understand drug interactions, a pharmaceutical company might want to simulate the interaction of two molecules. The challenge is that each molecule is composed of a few hundred atoms, and scientists must model all the ways in which these atoms might array themselves when their respective molecules are introduced. The number of possible configurations is infinite — more than the number of atoms in the entire universe. Only a quantum computer can represent, much less solve, such an expansive, dynamic data problem.
Mainstream use of quantum computing remains decades away, while research teams in universities and private industry across the globe work on different dimensions of the technology.
A research team led by Xu Yi, assistant professor of electrical and computer engineering at the University of Virginia School of Engineering and Applied Science, has carved a niche in the physics and applications of photonic devices, which detect and shape light for a wide range of uses including communications and computing. His research group has created a scalable quantum computing platform, which drastically reduces the number of devices needed to achieve quantum speed, on a photonic chip the size of a penny.
Olivier Pfister, professor of quantum optics and quantum information at UVA, and Hansuek Lee, assistant professor at the Korean Advanced Institute of Science and Technology, contributed to this success.
Nature Communications recently published the team’s experimental results, A Squeezed Quantum Microcomb on a Chip. Two of Yi’s group members, Zijiao Yang, a Ph.D. student in physics, and Mandana Jahanbozorgi, a Ph.D. student of electrical and computer engineering, are the paper’s co-first authors. A grant from the National Science Foundation’s Engineering Quantum Integrated Platforms for Quantum Communication program supports this research. More

Artificial intelligence (AI) will fundamentally change medicine and healthcare: Diagnostic patient data, e.g. from ECG, EEG or X-ray images, can be analyzed with the help of machine learning, so that diseases can be detected at a very early stage based on subtle changes. However, implanting AI within the human body is still a major technical challenge. TU Dresden scientists at the Chair of Optoelectronics have now succeeded for the first time in developing a bio-compatible implantable AI platform that classifies in real time healthy and pathological patterns in biological signals such as heartbeats. It detects pathological changes even without medical supervision. The research results have now been published in the journal Science Advances.
In this work, the research team led by Prof. Karl Leo, Dr. Hans Kleemann and Matteo Cucchi demonstrates an approach for real-time classification of healthy and diseased bio-signals based on a biocompatible AI chip. They used polymer-based fiber networks that structurally resemble the human brain and enable the neuromorphic AI principle of reservoir computing. The random arrangement of polymer fibers forms a so-called “recurrent network,” which allows it to process data, analogous to the human brain. The nonlinearity of these networks enables to amplify even the smallest signal changes, which — in the case of the heartbeat, for example — are often difficult for doctors to evaluate. However, the nonlinear transformation using the polymer network makes this possible without any problems.
In trials, the AI was able to differentiate between healthy heartbeats from three common arrhythmias with an 88% accuracy rate. In the process, the polymer network consumed less energy than a pacemaker. The potential applications for implantable AI systems are manifold: For example, they could be used to monitor cardiac arrhythmias or complications after surgery and report them to both doctors and patients via smartphone, allowing for swift medical assistance.
“The vision of combining modern electronics with biology has come a long way in recent years with the development of so-called organic mixed conductors,” explains Matteo Cucchi, PhD student and first author of the paper. “So far, however, successes have been limited to simple electronic components such as individual synapses or sensors. Solving complex tasks has not been possible so far. In our research, we have now taken a crucial step toward realizing this vision. By harnessing the power of neuromorphic computing, such as reservoir computing used here, we have succeeded in not only solving complex classification tasks in real time but we will also potentially be able to do this within the human body. This approach will make it possible to develop further intelligent systems in the future that can help save human lives.”
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