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Quantum sensor based on silicon carbide qubits operates at room temperature

Over the past decades, physicists and quantum engineers introduced a wide range of systems that perform desired functions leveraging quantum mechanical effects. These include so-called quantum sensors, devices that rely on qubits (i.e., units of quantum information) to detect weak magnetic or electric fields.

Researchers at the HUN-REN Wigner Research Center for Physics, the Beijing Computational Science Research Center, the University of Science and Technology of China and other institutes recently introduced a new quantum sensing platform that utilizes silicon carbide (SiC)-based spin qubits, which store quantum information in the inherent angular momentum of electrons. This system, introduced in a paper published in Nature Materials, operates at room temperature and measures qubit signals using near-infrared light.

“Our project began with a puzzle,” Adam Gali, senior author of the paper told Phys.org. “Quantum defects that sit just a few nanometers below a surface are supposed to be fantastic sensors—but in practice, they pick up a lot of ‘junk’ signals from the surface itself. This is especially true in SiC. Its standard oxide surface is full of stray charges and spins, and those produce noise that overwhelms the quantum defects we actually want to use for sensing. We wanted to break out of this limitation.”

Tiny reconfigurable robots can help manage carbon dioxide levels in confined spaces

Vehicles and buildings designed to enable survival in extreme environments, such as spacecraft, submarines and sealed shelters, heavily rely on systems for the management of carbon dioxide (CO2). These are technologies that can remove and release CO2, ensuring that the air remains breathable for a long time.

Most existing systems for the capture and release of CO2 consume a lot of energy, as they rely on materials that need to be heated to high temperatures to release the gas again after capturing it. Some engineers have thus been trying to devise more energy-efficient methods to manage CO2 in confined spaces.

Researchers at Guangxi University in China have developed new reconfigurable micro/nano-robots that can reversibly capture CO2 at significantly lower temperatures than currently used carbon management systems.

Finding information in the randomness of living matter

When describing collective properties of macroscopic physical systems, microscopic fluctuations are typically averaged out, leaving a description of the typical behavior of the systems. While this simplification has its advantages, it fails to capture the important role of fluctuations that can often influence the dynamics in dramatic manners, as the extreme examples of catastrophic events such as volcanic eruptions and financial market collapse reveal.

On the other hand, studying the dynamics of individual microscopic degrees of freedom comprehensively becomes too cumbersome even when considering systems of a moderate number of particles. To describe the interface between these opposite ends of the scale, stochastic field theories are commonly used to characterize the dynamics of complex systems and the effect of the microscopic fluctuations.

Due to their overwhelming complexity, predicting outcomes by analyzing these fluctuations in living or active matter systems is not possible using traditional methods of physics. Since these systems persistently consume energy, they exhibit dynamical traits that violate the laws of equilibrium thermodynamics, not unrelated to the arrow of time.

Can quantum computers help researchers learn about the inside of a neutron star?

A new paper published in Nature Communications could put scientists on the path to understanding one of the wildest, hottest, and most densely packed places in the universe: a neutron star.

Christine Muschik, a faculty member at the University of Waterloo Institute for Quantum Computing (IQC) and a research associate faculty member at Perimeter Institute is part of a U.S.–Canadian research group using a quantum computer to build on a theory of quantum chromodynamics that describes how different varieties of quarks and gluons (the most fundamental bits of nature) interact in nuclei.

To really understand the behavior of the quark-gluon plasma in extreme conditions like the beginning of the universe, or the inside of a neutron star, scientists need a map, a so-called “phase diagram” to describe the phase transitions in those conditions that are so extreme—so dense and complex—that classical computer simulations of the models will fail.

Electric control of ions and water enables switchable molecular stickiness on surfaces

What if a surface could instantly switch from sticky to slippery at the push of a button? By using electricity to control how ions and water structure at the solid liquid interface of self-assembled monolayers of aromatic molecules, researchers at National Taiwan University have created a molecular-scale adhesion switch that turns attraction on and off.

Why do some surfaces stick together while others repel each other? At scales far too small to see with the bare eye, this question is controlled by a complex interplay of intermolecular forces that arise when charged particles, called ions, and water organize themselves at the boundary between a solid and a liquid.

Understanding and controlling this behavior is essential for technologies ranging from lubricants and coatings to sensors and electronics.

Quasi-periodic oscillations detected in unusual multi-trigger gamma-ray burst

A new study led by the Yunnan Observatories of the Chinese Academy of Sciences has detected quasi-periodic oscillation (QPO) signals in an unusual gamma-ray burst (GRB) event. The findings are published in The Astrophysical Journal.

GRBs are short-timescale, highly energetic explosive phenomena typically associated with the collapse of massive stars or the mergers of compact objects. On July 2, 2025, the Gamma-ray Burst Monitor (GBM) aboard NASA’s Fermi satellite detected an unusual high-energy burst—designated GRB 250702DBE—that triggered the Fermi/GBM system three times.

Despite being named in accordance with standard GRB conventions, the event exhibited striking anomalies: its duration spanned several hours, far exceeding that of typical GRBs. The same source, also detected in the X-ray band by the Einstein Probe (EP) as EP250702a, has drawn scientific interest due to its long duration and unclear physical origin and radiation mechanisms.

BrainBody-LLM algorithm helps robots mimic human-like planning and movement

Large language models (LLMs), such as the model underpinning the functioning of OpenAI’s platform ChatGPT, are now widely used to tackle a wide range of tasks, ranging from sourcing information to the generation of texts in different languages and even code. Many scientists and engineers also started using these models to conduct research or advance other technologies.

In the context of robotics, LLMs have been found to be promising for the creation of robot policies derived from a user’s instructions. Policies are essentially “rules” that a robot needs to follow to correctly perform desired actions.

Researchers at NYU Tandon School of Engineering recently introduced a new algorithm called BrainBody-LLM, which leverages LLMs to plan and refine the execution of a robot’s actions. The new algorithm, presented in a paper published in Advanced Robotics Research, draws inspiration from how the human brain plans actions and fine-tunes the body’s movements over time.

Researchers pioneer pathway to mechanical intelligence by breaking symmetry in soft composite materials

A research team has developed soft composite systems with highly programmable, asymmetric mechanical responses. By integrating “shear-jamming transitions” into compliant polymeric solids, this innovative work enhances key material functionalities essential for engineering mechano-intelligent systems—a major step toward the development of next-generation smart materials and devices.

The work is published in the journal Nature Materials.

In engineering fields such as soft robotics, synthetic tissues, and flexible electronics, materials that exhibit direction-dependent responses to external stimuli are crucial for realizing intelligent functions.

Intelligent photodetectors ‘sniff and seek’ like retriever dogs to recognize materials directly from light spectra

Researchers at the University of California, Los Angeles (UCLA), in collaboration with UC Berkeley, have developed a new type of intelligent image sensor that can perform machine-learning inference during the act of photodetection itself.

Reported in Science, the breakthrough redefines how spectral imaging, machine vision and AI can be integrated within a single semiconductor device.

Traditionally, spectral cameras capture a dense stack of images, each image corresponding to a different wavelength, and then transfer this large dataset to digital processors for computation and scene analysis. This workflow, while powerful, creates a severe bottleneck: the hardware must move and process massive amounts of data, which limits speed, power efficiency, and the achievable spatial–spectral resolution.

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