A fast-track method of breeding disease-resistant ash trees has been developed by researchers leading efforts to conserve the species

Wireless signals do not travel well through the human body, especially in high-frequency bands, such as the UWB band, making it difficult for swallowable medical devices to reliably send data outside the body. By accounting for how different frequencies behave in the body, researchers adjusted each part of the signal to match how it is absorbed and distorted by tissue, creating a stronger, clearer signal at the receiver.  

Self-powered flexible sensors exhibit revolutionary potential in next-generation wearable technologies owing to their exceptional sensitivity and self-sustaining energy harvesting capabilities. Nevertheless, their widespread deployment remains constrained by three fundamental challenges: dynamic mechanical mismatch between biological tissues and rigid devices, suboptimal energy conversion efficiency, and interfacial impedance fluctuation under deformation. Drawing inspiration from the unique negative Poisson’s ratio mesh architecture of lacewing wings, we present a bioinspired auxetic metastructure-engineered triboelectric nanogenerator. This innovative design integrates engineered collagen and micropatterned fluorinated ethylene propylene as triboelectric layers, unified by an auxetic framework with re-entrant hexagonal unit cells interconnected via triangular ligaments. The metastructure enables exceptional lateral expansion under longitudinal strain while simultaneously enhancing structural rigidity and deformation adaptability. This dual functionality effectively minimizes tissue–device mechanical mismatch, thereby significantly improving signal fidelity, sensitivity, and mechanical-to-electrical conversion efficiency during multi-axial deformations. The optimized device achieves remarkable performance metrics, delivering 478 V output voltage with 13.8% energy conversion efficiency in linear configuration, while demonstrating threefold enhanced stability (58 V, 7.58% efficiency) under complex bending compared to conventional designs. Integrated with a convolutional neural network-based machine learning enables exceptional classification accuracy (> 99%) across diverse material recognition tasks, validating its robustness as a next-generation platform for adaptive self-powered wearable sensing.

An upcoming international online forum will bring together leading researchers to explore how engineered biochar can advance carbon capture and sustainable resource recovery, addressing urgent global climate and environmental challenges.

What do you look for when you buy wine? Is price the main consideration? Or do you notice quality logos, region of production or alcohol content? Researchers at the University of Tennessee Institute of Agriculture are studying how these attributes affect your decision. Their goal is to identify attributes Tennessee residents and visitors prefer when evaluating wines produced in the state.

 

In 2024, a research team from the UTIA Department of Agricultural and Resource Economics received a grant from the USDA Agricultural Marketing Service to examine how wineries utilize social media marketing to promote their wines and vineyards. In 2026, the research team received an additional $189,000 in funding to extend the original study for two years. The expansion allows the team to collect additional data to examine how wine buyers value the new Tennessee Quality Assurance Program (QAP) logo, the alcohol content of the product, and whether the wine originated from grapes from an American Viticultural Area (AVA) in Tennessee.

In a new study of tropical amphibians, a team led by Penn State biologists found that amphibians in connected natural forests and aquatic habitats were more likely to host beneficial skin microbes that inhibit a deadly fungal pathogen.

New Virginia Tech research brings safer, compostable packaging closer to everyday use.