Christina Tringides is the recipient of the highly prestigious Pew Biomedical Scholar award.

China’s power grid is set to undergo major transformation by 2030 as wind and solar energy expand rapidly. A new article in Engineering identifies five typical grid development scenarios and key technical challenges in power supply, grid stability, and equipment performance. It also proposes targeted research directions to support secure, low-carbon operation of the future power system, offering clear guidance for technology innovation and grid upgrading.

SAN ANTONIO — June 16, 2026 — Southwest Research Institute (SwRI) has completed a three-year renovation to its High Energy Annex Test (HEAT) facility, which supports highly efficient gas turbine combustor testing. An internally funded project designed new technology that expands HEAT’s capabilities to better serve evolving industry needs, particularly for alternative fuels.

Peptides are important biological compounds that carry key information for many biological processes. However, accurately reading their sequences has long been a challenge. In a new study, researchers developed a mass-spectrometry-based approach that attaches a coumarin-derived tag to the N-terminus of each peptide, enabling accurate and reliable sequencing of even short peptides, directly from spectral data without relying on protein sequence databases. This helps in identifying novel peptides.

A simple surface coating could make it easier to reliably make parts of next-generation computer chips using silicon and a promising new class of materials called transition metal dichalcogenides (TMD). By coating the surface of TMD with oxygen or fluorine before bombarding it with plasma ions, the team determined that the top layer of atoms could be removed from the material while lower layers were less likely to be damaged. The approach could help manufacturers build better computer chips and processors in the future.

Amorphous or disordered materials can “remember” past mechanical experiences. Until now, scientists believed that such memories form mainly under perfectly repetitive deformation, where materials are gently trained through predictable back-and-forth motion over many cycles. New research from the Tata Institute of Fundamental Research, Hyderabad, in collaboration with researchers in ESPCI Paris, France and Heinrich Heine University Düsseldorf, Germany, challenges this conventional picture.

Think about a new pair of shoes or jeans. At first, they may feel stiff and uncomfortable. But after months of everyday use, they begin to fit just right, almost as if they have adapted to the body shape of their user. This change does not happen through perfectly repeated movements — we walk, bend, twist, and move unpredictably. Yet these materials still adjust and “remember.” Inspired by this everyday observation, the researchers asked a simple but fundamental question: do materials really need perfectly repeated deformation to form memory, or can they learn from random experiences as well?

Using large-scale computer simulations, the researchers discovered that disordered materials can indeed form precise mechanical memories even when subjected to random deformation. Instead of repeatedly deforming the material in a perfectly regular way, they “trained” it using irregular back-and-forth deformation within a fixed amplitude and later tested whether the material retained this memory.

Remarkably, the material returned to the same state only when the test deformation matched the original training amplitude, showing that it had retained a precise memory despite the randomness of the driving. The researchers also found an important limit: memory forms only below the material’s yielding point, beyond which the material starts to break and the memory is lost.

The findings, published in the New Journal of Physics, bring scientific understanding of material memory closer to the irregular conditions found in everyday life.

This review provides a critical assessment of the ongoing debate surrounding altermagnetism in ruthenium dioxide (RuO₂), synthesizes a decade of experimental and theoretical efforts, offering a clear roadmap of the studies conducted on RuO₂.

While optical computing systems (OCS) with high bandwidth, low latency, and inherent parallelism are promising accelerators for artificial intelligence, the existing OCS implementations require direct participation of physical hardware, tightly coupling development workflows to device access and limiting offline design. Now, researchers have developed a Digital Twin OCS, a system-level, measurement-driven digital surrogate that enables a hardware-decoupled and fully offline development paradigm for OCS.

Biomass chemical looping is emerging as a promising route for producing renewable energy and chemicals while enabling efficient carbon management. A comprehensive review highlights advances in hydrogen, methanol, syngas, and power generation pathways, alongside the growing role of machine learning in oxygen carrier design and process optimization. The study also evaluates lifecycle and economic performance, identifying biomass chemical looping as a sustainable platform for future low-carbon energy systems.