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Category: particle physics

Major battery breakthrough paving way for EV upgrade

Chinese scientists have developed a lithium metal battery that boasts an energy density of more than 700 watt-hours per kilogram and stable performance at extremely low temperatures, marking a significant advancement in the production of high-energy batteries for electric vehicles. The research paper was published on Thursday in the science journal Nature.

Chen Jun, an academician of the Chinese Academy of Sciences and vice-president of Nankai University in Tianjin, was among the researchers who led the breakthrough. Chen said the team has replaced oxygen atoms with fluorine ones. It designed and synthesized novel fluorinated hydrocarbon solvent molecules, creating a new electrolyte system based on lithium-fluorine coordination.

A world first at the microscopic scale: Metamaterials that can shrink and expand on their own

Leiden physicists Daniela Kraft and Julio Melio have created soft structures that can take on different shapes without any external drive in their lab. They present their research on microscale metamaterials in Nature —a breakthrough that opens the door to smart, reconfigurable materials and microscopic robots.

“Metamaterials have completely changed the way we think about materials,” explains Professor of Experimental Physics Daniela Kraft. “In these systems, movements are no longer set by the material itself, but by the structure—the way particles are connected. We set out to create such functional structures at the microscopic scale. And we succeeded.”

A robust new telecom qubit identified in silicon

Quantum technologies are anticipated to transform computing, communication, and sensing by harnessing the unusual behavior of matter at the atomic scale. Translating quantum’s promise into practical devices will require physical systems that have desirable quantum properties and can be easily manufactured. Silicon, the material behind today’s computer chips, is highly attractive as a platform because it plays to the strengths of the trillion-dollar semiconductor industry that has already been built. Identifying quantum building blocks—qubits—in silicon is, therefore, an important frontier research area.

In a new study, researchers in UC Santa Barbara materials professor Chris Van de Walle’s Computational Materials Group identified a robust new qubit in silicon, called the CN center. The work is published in the journal Physical Review B.

Qubits can be based on atomic-scale defects in a crystal. A prototype example is the NV center, which consists of a nitrogen (N) atom sitting next to a vacancy (V, a missing carbon atom) in a diamond crystal. These defects can interact with both electrons and light, allowing them to emit single photons (quanta of light) that can transmit quantum information or be processed in quantum networks.

A protocol to realize near-perfect atom-photon entanglement

Quantum technologies, devices and systems that operate leveraging quantum mechanical effects, could tackle some tasks more reliably and efficiently than any classical technology could. In recent years, some researchers have been trying to realize quantum networks to scale up the size of quantum computers, which essentially consist of several connected smaller quantum processors.

The devices in a quantum network are connected via entanglement, a quantum effect via which distant quantum particles become inextricably linked and share a single correlated state. One way to create entanglement between different atomic quantum computers is to use an atom-cavity interface, a system in which atoms interact with light inside an optical cavity.

Over two decades ago, two physicists at the University of Aarhus introduced a protocol designed to produce high-quality entangled states, reliably connecting devices in a network. Despite its potential, this framework, known as the state-carving (SC) protocol, was found to only succeed in 50% of cases, which has so far prevented its application on a large scale.

When light ‘thinks’ like the brain: The connection between photons and artificial memory

An international study has revealed a surprising connection between quantum physics and the theoretical models underlying artificial intelligence. The study results from a collaboration between the Institute of Nanotechnology of the National Research Council (Cnr-Nanotec), the Italian Institute of Technology (IIT), and Sapienza University of Rome, together with international research institutions. The research paper was published recently in the journal Physical Review Letters.

Italian researchers show that identical photons propagating within optical circuits spontaneously behave like a Hopfield Network, one of the best-known mathematical models used to describe the associative memory mechanisms of the human brain.

“Instead of using traditional electronic chips, we exploited quantum interference —the phenomenon that occurs in photonic chips when particles of light overlap and interact with one another to encode and retrieve information,” explains Marco Leonetti, coordinator and corresponding author of the study, senior researcher at Cnr-Nanotec and affiliated with the Center for Life Nano-and Neuro-Science at the Italian Institute of Technology (IIT) in Rome. “In this system, photons are not merely carriers of data, but themselves become the ‘neurons’ of an associative memory.”

Electrical control of magnetism in 2D materials promises to advance spintronics

Conventional electronics process information leveraging the electrical charge of electrons. Over the past few decades, some electronics engineers have been exploring the potential of a different type of device that instead processes and stores data exploiting the intrinsic magnetic moment (i.e., spin) of electrons.

These devices, known as spintronics, could consume less energy, process data faster and be easier to reduce in size than current electronics. A central objective for engineers who are developing spintronics is to identify promising strategies to control magnetism in devices without wasting power.

One promising approach to control magnetism entails the use of multiferroics, materials that exhibit both ferroelectricity, meaning that positive and negative charges in them are permanently separated, and ferromagnetism, which means that magnetic moments in them are aligned. When one of these properties can be used to control the other, this is known as magnetoelectric coupling.

Rapid Evolution of Complex Multi-mutant Proteins

The researchers developed MULTI-evolve, a framework for efficient protein evolution that applies machine learning models trained on datasets of ~200 variants focused specifically on pairs of function-enhancing mutations.

Published in Science, this work represents the first lab-in-the-loop framework for biological design, where computational prediction and experimental design are tightly integrated from the outset, reflecting our broader investment in AI-guided research.

Our insight was to focus on quality over quantity. First identify ~15–20 function-enhancing mutations (using protein language models or experimental screens), then systematically test all pairwise combinations of those beneficial mutations. This generates ~100–200 measurements, and every one is informative for learning beneficial epistatic interactions.

We validated this computationally using 12 existing protein datasets from published studies. Training neural networks on only the single and double mutants, we found models could accurately predict complex multi-mutants (variants with 3–12 mutations) across all 12 diverse protein families. This result held even when we reduced training data to just 10% of what was available.

Training on double mutants works because they reveal epistasis. A double mutant might perform better than the sum of its parts (synergy), worse than expected (antagonism), or exactly as predicted (additivity). These pairwise interaction patterns teach models the rules for how mutations combine, enabling extrapolation to predict which 5-, 6-, or 7-mutation combinations will work synergistically.

We then applied MULTI-evolve to three new proteins: APEX (up to 256-fold improvement over wild-type, 4.8-fold beyond already-optimized APEX2), dCasRx for trans-splicing (up to 9.8-fold improvement), and an anti-CD122 antibody (2.7-fold binding improvement to 1.0 nM, 6.5-fold expression increase). For dCasRx, we started with a deep mutational scan of 11,000 variants, extracted only the function-enhancing mutations, and tested their pairwise combinations—demonstrating the value of strategic data curation for efficient engineering.

Each required experimentally testing only ~100–200 variants in a single round to train models that accurately predicted complex multi-mutants, compressing what traditionally takes 5–10 iterative cycles over many months into weeks. Science Mission sciencenewshighlights.

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