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Research examines how ripples affect nanoscopic materials

When materials are created on a nanometer scale — just a handful of atoms thick — even the thermal energy present at room temperature can cause structural ripples. How these ripples affect the mechanical properties of these thin materials can limit their use in electronics and other key systems.

New research validates theoretical models about how elasticity is scale-dependent — in other words, the elastic properties of a material are not constant, but vary with the size of the piece of material.

Assistant Professor Jian Zhou, PhD ’18, collaborated with researchers from Argonne National Laboratory, Harvard University, Princeton University and Penn State University for a recently published paper in the Proceedings of the National Academy of Sciences.

Using a semiconductor manufacturing process, the team created alumina structures 28 nanometers thick (more than 1,000 times thinner than the diameter of a human hair) on the silicon wafer with thermal-like static ripples, then tested them with lasers to measure their behavior. To remove possible stress to the material that could affect the results, cantilevers held the wafers during testing.

Understanding how thin materials behave is key to electronics and other technology.

New transmitter could make wireless devices more energy-efficient

Researchers from MIT and elsewhere have designed a novel transmitter chip that significantly improves the energy efficiency of wireless communications, which could boost the range and battery life of a connected device.

Their approach employs a unique modulation scheme to encode digital data into a wireless signal, which reduces the amount of error in the transmission and leads to more reliable communications.

JWST observations shed more light on the nature of a distant galaxy

Using the James Webb Space Telescope (JWST), an international team of astronomers has observed a distant faint galaxy designated JADES-GS-z14-1. Results of the observational campaign, published July 30 on the arXiv preprint server, provide more insights into the nature and properties of this galaxy.

Launched into space in 2021, JWST is designed to find and investigate the most distant galaxies, providing insights into the . It enables the detection of galaxies within the first 500 million years after the Big Bang.

One of such early galaxies is JADES-GS-z14-1—the faintest spectroscopically confirmed galaxy, at a redshift of about 14.0. The galaxy has an absolute ultraviolet magnitude of-19.0 and is relatively compact as its half-light radius is estimated to not exceed 520 light years. Previous observations of JADES-GS-z14-1 have found that it has a mass of some 100 million , and a (SFR) at a level of about two solar masses per year.

Searching for Artificial Memory Systems in ancient humans with spatial statistics

Université de Bordeaux-led research reports that spatial statistics can discriminate potential Paleolithic Artificial Memory Systems from butchery and art, aligning prehistoric marked objects with memory devices in Africa and Europe.

Humans are highly symbolic creatures, uniquely combining symbolic reference, complex language, physical representations, active intentional teaching, and large-scale cultural learning.

Artificial Memory Systems (AMS) encompass devices that record, store, transmit, and retrieve coded information beyond the brain, via external representations. AMS can be anything from the notches on a gunslinger’s pistol, tracking past success, to the symbols on and data encoded within the Voyager spacecraft’s golden record, detailing a snapshot of Earthling knowledge and culture.

Study finds Alaska early warning system offers crucial seconds before strong shaking

For a wide variety of earthquake scenarios in Alaska, an earthquake early warning (EEW) system could provide at least 10 seconds of warning time for hazardous shaking, according to a new report.

Increasing the density and improving the spacing of seismic stations around the state could add 5 to 15 seconds to these estimated warning times, write Alexander Fozkos and Michael West at the University of Alaska Fairbanks. Alaska experiences tens of thousands of earthquakes each year, and has been the site of some of the world’s largest and most destructive seismic events.

The study’s findings, published in the Bulletin of the Seismological Society of America, could help lay the groundwork for the expansion of the U.S. ShakeAlert earthquake early warning system, which now covers California, Oregon and Washington state.

Heavy fermions entangled: Discovery of Planckian time limit opens doors to novel quantum technologies

A joint research team from Japan has observed “heavy fermions,” electrons with dramatically enhanced mass, exhibiting quantum entanglement governed by the Planckian time—the fundamental unit of time in quantum mechanics. This discovery opens up exciting possibilities for harnessing this phenomenon in solid-state materials to develop a new type of quantum computer. The findings are published in npj Quantum Materials.

Heavy fermions arise when conduction electrons in a solid interact strongly with localized magnetic electrons, effectively increasing their mass. This phenomenon leads to unusual properties like unconventional superconductivity and is a central theme in condensed matter physics. Cerium-rhodium-tin (CeRhSn), the material studied in this research, belongs to a class of heavy fermion systems with a quasi-kagome lattice structure, known for its geometrical frustration effects.

Researchers investigated the electronic state of CeRhSn, known for exhibiting non-Fermi liquid behavior at relatively high temperatures. Precise measurements of CeRhSn’s reflectance spectra revealed non-Fermi liquid behavior persisting up to near room temperature, with heavy electron lifetimes approaching the Planckian limit. The observed spectral behavior, describable by a single function, strongly indicates that heavy electrons in CeRhSn are quantum entangled.

Scientists find ‘speed limit’ for innovation networks to prevent system collapse

Research shows that while connections between innovations speed discovery, they also sharply increase the risk of total system collapse—with the sweet spot for sustainable innovation proving surprisingly narrow.

Innovation is a central currency of global power. Whether in the race for leadership in , the development of clean energy technologies, or the search for medical breakthroughs, major players like China, the United States, and the European Union are investing billions in research and development to secure the next technological leap—and with it, economic and strategic advantage.

Yet, as a new study from the Complexity Science Hub (CSH), published in Physical Review Research, indicates, long-term innovation is only sustainable under specific structural conditions. First, the study finds that innovation can only endure over time if it is balanced with “exnovation”—the loss or forgetting of older possibilities.

Anti-neuroinflammatory natural products from isopod-related fungus now accessible via chemical synthesis

“Herpotrichone” is a natural substance that has been evaluated highly for its excellent ability to suppress inflammation in the brain and protect nerve cells, displaying significant potential to be developed as a therapeutic agent for neurodegenerative brain diseases such as Alzheimer’s disease and Parkinson’s disease. This substance could only be obtained in minute quantities from fungi that are symbiotic with isopods. However, KAIST researchers have succeeded in chemically synthesizing this rare natural product, thereby presenting the possibility for the development of next-generation drugs for neurodegenerative diseases.

A research team led by Professor Sunkyu Han of the Department of Chemistry successfully synthesized the natural anti-neuroinflammatory substances ‘herpotrichones A, B, and C’ for the first time. The paper is published in the Journal of the American Chemical Society.

Herpotrichone natural products are substances obtainable only in minute quantities from Herpotrichia sp. SF09, a symbiotic pill bug fungus, and possesses a unique 6÷6÷6÷6÷3 pentacyclic framework consisting of five fused rings (four six-membered and one three-membered ring).

Machine learning model helps scientists understand deadly cone snail toxins

Marine cone snails are host to a family of dangerous neurotoxins. Very little is known about how those toxins interact with the human body, making this an area of interest for medical drug research and an area of concern in national security spaces. For the first time, a team at Los Alamos National Laboratory has successfully trained a machine learning model that predicts how alpha conotoxins bind to specific human receptor subtypes, which could help researchers develop lifesaving anti-toxins.

“Because of the diversity and complexity of natural conotoxins, it is estimated that only 2% of them have been sequenced,” said Gnana Gnanakaran, theoretical biologist at Los Alamos. “No antidotes exist for conotoxins, but by using machine learning to predict conotoxin binding, we now have the ability to develop tools to understand and respond to these threats.”

The deadly secretions issued by any one of the more than 800 cone snail species represent a conglomeration of more than 1 million natural conotoxins. The research team concentrated their machine learning work on alpha conotoxins, a particularly prevalent and deadly conotoxin family.

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