Nitrous acid (HONO) is a key precursor to atmospheric hydroxyl radicals that drive formation of secondary pollutants such as ozone, secondary organic aerosols (SOA), and peroxyacetyl nitrates (PAN). A new study uses WRF-Chem modeling and field observations across China to assess how HONO sources influence pollution. Results show that aerosol-related sources, especially nitrate photolysis, have a disproportionately strong effect on SOA and PAN formation despite smaller surface HONO contributions, highlighting critical role in haze chemistry.

A research paper by scientists from Beijing University of Posts and Telecommunications designed a multimodal controller termed the tactile, kinesthetic, and electromyography (EMG) bionic gripping controller (TKE-BGC).

The new research paper, published on May. 13 in the journal Cyborg and Bionic Systems, developed a bioinspired control framework that dramatically improves prosthetic hand stability during such dynamic tasks.

A research paper by scientists from Capital Medical University proposed a diffusion tensor imaging (DTI)-based DenseNet to identify SVCI from subcortical ischemic vascular disease (SIVD) and to profile multidomain cognitive risks.

The new research paper, published on May. 13 in the journal Cyborg and Bionic Systems, developed a deep‑learning framework that uses routine diffusion tensor imaging to both identify SVCI and profile an individual’s domain‑specific cognitive weaknesses.

Researchers found that toxic metal particles in rural North China air are smaller and potentially more harmful than those in urban areas. The study showed that village pollution was strongly linked to biomass and coal burning, while urban pollution was mainly associated with dust-related mineral particles. The findings highlight the overlooked health risks faced by rural populations exposed to PM_2.5-bound heavy metals.

As the global transition to renewable energy intensifies, the search for high-performance batteries and efficient electrocatalysts has become a critical race against time. Traditionally, discovering these materials required years of "trial-and-error" laboratory experiments. Now, a comprehensive review published in the journal ENGINEERING Energy by researchers from Tongji University reveals how Artificial Intelligence (AI) is fundamentally shifting this paradigm.