Windows contribute significantly to energy inefficiency across global infrastructure. Overcoming complex trade-offs in multispectral optical responses, scientists in China have developed a physics-guided neural network to design energy-efficient thermochromic smart windows for directional privacy protection. These inverse-designed windows achieve ultrahigh simultaneous modulation of near-infrared transmittance and longwave infrared emissivity. Applied to electric vehicles and building envelopes, these customized smart windows demonstrate superior, climate-adaptive all-weather thermal management.

Researchers have developed a process to manufacture flu vaccines in baker’s yeast that is fast, inexpensive, and safe. The approach could even make the yearly flu shot obsolete by providing long‑lasting protection against multiple influenza strains. They will present their results at ACS Fall 2026.

Plastic might be the last ingredient you would ever add to a cookie recipe. But a team of researchers is looking to change that. They’ve programmed yeasts to turn plastic and agricultural waste into edible proteins and flavoring molecules, creating a treat from trash. The technology not only offers a new upcycling method, but could also sustain life in disaster zones or manned deep-space missions. The researchers will present their results at ACS Fall 2026.

A new purple cooling fabric developed by Chinese and Australian researchers could help people stay cooler during scorching summer days without having to resort to wearing white.

-  Joint research team led by Professor Kang Taek Lee and Professor EunAe Cho uses a 3D digital twin to reveal localized degradation mechanisms during fast charging

- Study shows how the spatial distribution of binder and pore phase affects fast-charging performance and battery degradation

-  Findings provide a new electrode design strategy for faster-charging, longer-lasting lithium-ion batteries

Plasma bubbles in the ionosphere over equatorial and low-latitude regions are a common nighttime plasma density depletion structure that can cause severe scintillation interference on high-frequency communications, making them an important target of space weather monitoring. Traditional detection methods rely on statistical thresholds derived from temporal fluctuations in ion density; however, plasma bubbles exhibit significant variability in spatiotemporal scales (ranging from kilometers to hundreds of kilometers), and their occurrence frequency is strongly modulated by solar activity and seasonal factors. Consequently, fixed-threshold methods struggle to accommodate this diversity, with miss rates reaching up to 40% during periods of low solar activity. Although deep learning has demonstrated powerful capabilities in automatically extracting and recognizing complex waveform features in fields such as medical image segmentation and signal peak detection, its systematic application to ionospheric plasma bubble detection—and thus addressing the adaptability deficiencies of traditional threshold methods—remains underexplored.