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Category: materials – Page 7

Researchers at the University of Twente, in collaboration with the City University of Hong Kong, have designed a cutting-edge programmable photonic chip in a thin-film lithium niobate platform, an important material in photonics. Published in Nature Communications, this work paves the way for next-generation high-performance radar and communication applications.

An important material is changing the way work, making them smaller, faster, and more efficient: thin-film lithium niobate (TFLN). It offers exceptional properties for how light and electrical signals can interact. This enables the seamless integration of key components—such as electro-optic modulators and signal processors—onto a single chip. As a result, can achieve unprecedented compactness, efficiency, and performance.

Researchers at the University of Twente have designed a TFLN-based integrated photonic chip, working in close collaboration with City University of Hong Kong, where the fabrication takes place. At the same time, these chips are also being fabricated locally in the MESA+ Nanolab.

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Electronic devices rely on materials whose electrical properties change with temperature, making them less stable in extreme conditions. A discovery by McGill University researchers that challenges conventional wisdom in physics suggests that bismuth, a metal, could serve as the foundation for highly stable electronic components.

The researchers observed a mysterious electrical effect in ultra-thin that remains unchanged across a wide temperature range, from near absolute zero (−273°C) to room temperature.

“If we can harness this, it could become important for green electronics,” said Guillaume Gervais, a professor of physics at McGill and co-author of the study.

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A research team led by Prof. Hu Weijin from the Institute of Metal Research (IMR) of the Chinese Academy of Sciences has discovered that single-domain ferroelectric thin films can be efficiently achieved by simply elevating the growth temperature.

Their findings, published in Advanced Functional Materials, offer a straightforward alternative to conventional complex fabrication methods, with significant implications for ferroelectric device performance.

Ferroelectric materials naturally form polydomain structures to minimize electrostatic energy. Nevertheless, single-domain can be achieved through precise control of interfacial atomic layers or strain gradients. The quest for a simple method to obtain a single-domain state and its impact on ferroelectric device performance are of great interest.

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Ever since their discovery almost four decades ago, high-temperature superconductors have fascinated scientists and engineers alike. These materials, primarily cuprates, defy classical understanding because they conduct electricity without resistance at temperatures far higher than traditional superconductors. Yet despite decades of research, we still don’t have a clear, comprehensive microscopic picture of how superconductivity emerges in these complex materials.

During my Ph.D. at Caltech, I was intrigued by the profound puzzle presented by high-temperature superconductors: Can we directly compute their from fundamental quantum mechanics without relying on simplified models or approximations? With this question, I embarked on a challenging but rewarding scientific journey.

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Boquila trifoliolata plants were purchased from a local store placed in Port Townsend Washington and arrived in 15.24 cm pots. Shortly after arrival plants were reported in 25.4 cm pots filled with high nutrient potting soil with a pH of 6.3, 0.30% nitrogen, 0.45% phosphate, 0.05% potassium, and 1.00% calcium. The plants were watered with distilled water (approximately 236 ml) until they reached field capacity every other day to keep the soil moist. A stone humidifier was placed near the plants to maintain a higher humidity. The experiment was conducted in Magna, Ut, USA (40°42ʹN, 112°06ʹW) during the period from September 2019 to October 2020. The plants were placed in front of a large west facing window. The first leaves sample for analysis was collected in December 2020 and the second sample was collected in June 2021.

Each plant was assigned a number and placed on a growing rack. Two artificial vines were placed above the plants on a wooden trellis. During the winter, the plants grew quickly through the leaves showed poor mimicry of the artificial plants leaves. The original plant that we had did not show good evidence of mimicry until the spring and summer. We decided to continue the experiment and see if there were better results in the warmer months.

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Researchers at ETH Zurich have developed a new technique to better understand how electrons interact within materials. By using a moiré material — created by twisting ultra-thin atomic layers — they generated an artificial crystal lattice in a nearby semiconductor, allowing for more precise studies of electron behavior.

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To study the interactions between electrons in a material, physicists have come up with a number of tricks over the years. These interactions are interesting, among other things, because they lead to technologically important phenomena such as superconductivity.

In most materials, however, are very weak and, therefore, hard to detect. One of the tricks that researchers have used for a while now consists in reducing the motional energy of the electrons by artificially creating a with a large lattice constant—that is, with a large distance between the lattice sites in the crystal. In this way, the interaction energy, which is still small, becomes relatively more important, so that interaction effects become visible.

However, the so-called moiré materials used for this suffer from the disadvantage that inside them it is not only the motion of electrons that is modified with respect to ordinary crystal lattices, but also other physical processes that are needed for studying the material.

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Composite adhesives like epoxy resins are excellent tools for joining and filling materials including wood, metal, and concrete. But there’s one problem: once a composite sets, it’s there forever. Now there’s a better way. Researchers have developed a simple polymer that serves as a strong and stable filler that can later be dissolved. It works like a tangled ball of yarn that, when pulled, unravels into separate fibers.

A new study led by researchers at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) outlines a way to engineer pseudo-bonds in materials. Instead of forming chemical bonds, which is what makes epoxies and other composites so tough, the chains of molecules entangle in a way that is fully reversible. The research is published in the journal Advanced Materials.

“This is a brand new way of solidifying materials. We opened a new path to composites that doesn’t go with the traditional ways,” said Ting Xu, a faculty senior scientist at Berkeley Lab and one of the lead authors for the study.

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Researchers at the Fritz Haber Institute have developed the Automatic Process Explorer (APE), an approach that enhances our understanding of atomic and molecular processes. By dynamically refining simulations, APE has uncovered unexpected complexities in the oxidation of palladium (Pd) surfaces, offering new insights into catalyst behavior. The study is published in the journal Physical Review Letters.

Kinetic Monte Carlo (kMC) simulations are essential for studying the long-term evolution of atomic and molecular processes. They are widely used in fields like surface catalysis, where reactions on material surfaces are crucial for developing efficient catalysts that accelerate reactions in and pollution control. Traditional kMC simulations rely on predefined inputs, which can limit their ability to capture complex atomic movements. This is where the Automatic Process Explorer (APE) comes in.

Developed by the Theory Department at the Fritz Haber Institute, APE overcomes biases in traditional kMC simulations by dynamically updating the list of processes based on the system’s current state. This approach encourages exploration of new structures, promoting diversity and efficiency in structural exploration. APE separates process exploration from kMC simulations, using fuzzy machine-learning classification to identify distinct atomic environments. This allows for a broader exploration of potential atomic movements.

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