Lifeboat Foundation

A new type of material can learn and improve its ability to deal with unexpected forces thanks to a unique lattice structure with connections of variable stiffness, as described in a new paper by my colleagues and me.

The new material is a type of architected material, which gets its properties mainly from the geometry and specific traits of its design rather than what it is made out of. Take hook-and-loop fabric closures like Velcro, for example. It doesn’t matter whether it is made from cotton, plastic or any other substance. As long as one side is a fabric with stiff hooks and the other side has fluffy loops, the material will have the sticky properties of Velcro.

My colleagues and I based our new material’s architecture on that of an artificial neural network—layers of interconnected nodes that can learn to do tasks by changing how much importance, or weight, they place on each connection. We hypothesized that a mechanical lattice with physical nodes could be trained to take on certain mechanical properties by adjusting each connection’s rigidity.

Electronics engineers worldwide are trying to improve the performance of devices, while also lowering their power consumption. Tunnel field-effect transistors (TFETs), an experimental class of transistors with a unique switching mechanism, could be a particularly promising solution for developing low-power electronics.

Despite their potential, most TFETs based on silicon and III-V heterojunctions exhibit low on-current densities and on/off current ratios in some modes of operation. Fabricating these transistors using 2D materials could help to improve electrostatic control, potentially increasing their on-current densities and on/off ratios.

Researchers at University of Pennsylvania, the Chinese Academy of Sciences, the National Institute of Standards and Technology, and the Air Force Research Laboratory have recently developed new heterojunction tunnel triodes based on van der Waals heterostructures formed from 2D metal selenide and 3D silicon. These triodes, presented in a paper published in Nature Electronics, could outperform other TFETs presented in the past in terms of on-current densities and on/off ratios.

X-rays can be used like a superfast, atomic-resolution camera, and if researchers shoot a pair of X-ray pulses just moments apart, they get atomic-resolution snapshots of a system at two points in time. Comparing these snapshots shows how a material fluctuates within a tiny fraction of a second, which could help scientists design future generations of super-fast computers, communications, and other technologies.

Resolving the information in these X-ray snapshots, however, is difficult and time intensive, so Joshua Turner, a lead scientist at the Department of Energy’s SLAC National Accelerator Center and Stanford University, and ten other researchers turned to artificial intelligence to automate the process. Their machine learning-aided method, published October 17 in Structural Dynamics, accelerates this X-ray probing technique, and extends it to previously inaccessible materials.

“The most exciting thing to me is that we can now access a different range of measurements, which we couldn’t before,” Turner said.

A team of engineers has developed a new type of camera that can detect radiation in terahertz (THz) wavelengths. This new imaging system can see through certain materials in high detail, which could make it useful for security scanners and other sensors.

Terahertz radiation is that which has wavelengths between microwaves and visible light, and these frequencies show promise in a new class of imaging systems. They can penetrate many materials and capture new levels of detail, and importantly the radiation is non-ionizing, meaning it’s safer than X-rays when used on humans.

The problem is that detectors that pick up THz wavelengths can be bulky, slow, expensive, difficult to run under practical conditions, or some combination of these. But in a new study, researchers at MIT, Samsung and the University of Minnesota have developed a system that can detect THz pulses quickly, precisely and at regular room temperature and pressure.

TSMC is setting up a new 1-nm chip production facility that will be located in an industrial park in Longtan District in Taiwan.

Scientists have discovered that an obscure material known as cubic boron arsenide (c-BAs) may perform much better than silicon. In fact, it may be the best semiconductor possible—demonstrating both high carrier mobility and simultaneously high thermal conductivity.

Wearable heaters are highly desirable for low-temperature environments. However, the fundamental challenge in achieving such devices is to design electric-heating membranes with flexible, breathable, and stretchable properties.

Study: Large-Scale Preparation of Micro–Nanofibrous and Fluffy Propylene-Based Elastomer/ [email protected] Nanoplatelet Membranes with Breathable and Flexible Characteristics for Wearable Stretchy Heaters. Image Credit: s_maria/Shutterstock.com.

A study published in ACS Applied Materials and Interfaces aimed to achieve an electric heating membrane with a nanofibrous fluffy texture and excellent electric-heating features. Here, an electric heating membrane was fabricated by coating a melt-blown propylene-based elastomer (PBE) with polyurethane (PU) and graphene nanoplatelet films via an easy, cost-effective, and large-scale method involving a coating-compression cyclic process.

A new tool can determine whether a collection of building blocks will assemble into a mechanically sound structure.

In 1911, physicist Heike Kamerlingh Onnes used liquid helium—whose production method he invented—to cool mercury to a few kelvins, discovering that its electrical resistance dropped to nil. Although mercury was later found to be a “conventional” superconductor, no microscopic theory so far managed to fully explain the metal’s behavior and to predict its critical temperature T_C. Now, 111 years after Kamerlingh Onnes’ discovery, theorists have done just that. Their first-principles calculations accurately predict mercury’s T_C but also pinpoint theoretical caveats that could inform searches for room-temperature superconductors [1].

Mercury is an exception among conventional superconductors, most of which can be successfully described with state-of-the-art density-functional-theory methods. To tackle mercury’s unique challenges, Gianni Profeta of the University of L’Aquila, Italy, and colleagues scrutinized all physical properties relevant for conventional superconductivity, which is mediated by the coupling of electrons to phonons. In particular, the researchers accounted for previously neglected relativistic effects that alter phonon frequencies, they improved the description of electron-correlation effects that modify electronic bands, and they showed that mercury’s d-electrons provide an anomalous screening effect that promotes superconductivity by reducing Coulomb repulsion between superconducting electrons. With these improvements, their calculations delivered a T_C prediction for mercury only 2.5% lower than the experimental value.

The new understanding of the oldest superconductor will find a place in textbooks but may also offer valuable lessons for superconductivity research, says Profeta. A promising material-by-design approach involves “high-throughput” computations that screen millions of theoretical material combinations to suggest those that could be conventional superconductors close to ambient conditions. “If we don’t include subtle effects similar to those relevant for mercury, these computations may overlook many interesting materials or err in their critical temperature predictions by hundreds of kelvins,” he says.