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Stretching without Buckling

Liquid-crystal elastomers (LCEs) are shape-shifting materials that stretch or squeeze when stimulated by an external input such as heat, light, or a voltage. Designing these materials to produce desired shapes is a challenging math problem, but Daniel Castro and Hillel Aharoni from the Weizmann Institute of Science, Israel, have now provided an analytical solution for flat materials that shape-shift within a single plane—like font-changing letters on a page [1]. Such “planar” designs could help in producing rods that change their cross section (from, say, round to square) without buckling.

LCEs consist of networks of polymer fibers containing liquid-crystal molecules. When exposed to a stimulus, the molecules align in a way that causes the material to shrink or extend in a predefined direction—called the director. Researchers can design an LCE by choosing the director orientation at each point in the material. However, calculating the “director field” for an arbitrary shape change is difficult, so approximate methods are typically used.

Castro and Aharoni focused on a specific design problem: how to create an LCE that stretches only in two dimensions. These planar LCEs often suffer from residual stress that causes the material to wrinkle or buckle out of the plane. The researchers showed that finding a buckle-free design is similar to a well-known mathematical problem that has been studied in other contexts, such as minimizing the mass of load-carrying structures. Taking inspiration from these previous studies, Castro and Aharoni provided a method for exactly deriving the director field for any desired planar LCE. “Our results could be readily implemented by a wide range of experimentalists, as well as by engineers and designers,” Aharoni says.

Light Steering Technologies claims $1.25 million Air Force contract

SAN FRANCISCO –New Hampshire startup Light Steering Technologies won a $1.25 million U.S. Air Force contract for angular pointing technology with small satellite applications.

Through the contract with AFWERX, the Air Force organization focused on innovation, LST aims to advance the Technology Readiness Level, or technological maturity, of its Multi-Axis Scanner. LST’s Multi-Axis Scanner is a patented magnetic joint for gimbal-like capability.

“What’s compelling about the technology is we are minimizing the moving mass,” Aaron Castillo, LST senior vice president of business development and program management, told SpaceNews. “This is achieved by actuating a mirror instead of the entire satellite bus or using a traditional gimbal mechanism.”

Auto-GPT May Be The Strong AI Tool That Surpasses ChatGPT

Like many people, you may have had your mind blown recently by the possibility of ChatGPT and other large language models like the new Bing or Google’s Bard.

For anyone who somehow hasn’t come across them — which is probably unlikely as ChatGPT is reportedly the fastest-growing app of all time — here’s a quick recap:

Large language models or LLMs are software algorithms trained on huge text datasets, enabling them to understand and respond to human language in a very lifelike way.


Auto-GPT is a breakthrough technology that creates its own prompts and enables large language models to perform complex multi-step procedures. While it has potential benefits, it also raises ethical concerns about accuracy, bias, and potential sentience in AI agents.

A particular ‘sandwich’ of graphene and boron nitride may lead to next-gen microelectronics

Moiré patterns occur everywhere. They are created by layering two similar but not identical geometric designs. A common example is the pattern that sometimes emerges when viewing a chain-link fence through a second chain-link fence.

For more than 10 years, scientists have been experimenting with the moiré pattern that emerges when a sheet of graphene is placed between two sheets of . The resulting moiré pattern has shown tantalizing effects that could vastly improve that are used to power everything from computers to cars.

A new study led by University at Buffalo researchers, and published in Nature Communications, demonstrated that graphene can live up to its promise in this context.

Researchers in Japan develop a new ultra-high-density sulfonic acid polymer electrolyte membrane for fuel cells

In a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO), researchers at Nagoya University in Japan have developed poly(styrenesulfonic acid)-based PEMs with a high density of sulfonic acid groups.

One of the key components of environmentally friendly polymer electrolyte fuel cells is a (PEM). It generates through a reaction between hydrogen and oxygen gases. Examples of practical fuel cells include fuel cell vehicles (FCVs) and combined heat and power (CHP) systems.

The best-known PEM is a membrane based on a perfluorosulfonic acid polymer, such as Nafion, which was developed by DuPont in the 1960s. It has a good proton conductivity of 0.1 S/cm at 70–90 °C under humidified conditions. Under these conditions, protons can be released from sulfonic acid groups.

This hypersonic hydrogen jet could fly from London to New York in 90 mins

Flying across the world from Europe to Australia currently takes around 20 hours in a regular passenger jet.

But Swiss startup Destinus is looking to slash that time to just four hours — by taking jet travel to hypersonic speeds.

Founded by Russian-born physicist and serial entrepreneur Mikhail Kokorich, Destinus is developing a prototype hydrogen-powered aircraft capable of travelling at Mach 5 and above. That’s five times the speed of sound: over 6,000 kph.

Is deep learning a necessary ingredient for artificial intelligence?

The earliest artificial neural network, the Perceptron, was introduced approximately 65 years ago and consisted of just one layer. However, to address solutions for more complex classification tasks, more advanced neural network architectures consisting of numerous feedforward (consecutive) layers were later introduced. This is the essential component of the current implementation of deep learning algorithms. It improves the performance of analytical and physical tasks without human intervention, and lies behind everyday automation products such as the emerging technologies for self-driving cars and autonomous chat bots.

The key question driving new research published today in Scientific Reports is whether efficient learning of non-trivial classification tasks can be achieved using brain-inspired shallow feedforward networks, while potentially requiring less .

“A positive answer questions the need for deep learning architectures, and might direct the development of unique hardware for the efficient and fast implementation of shallow learning,” said Prof. Ido Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research. “Additionally, it would demonstrate how brain-inspired shallow learning has advanced computational capability with reduced complexity and energy consumption.”

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