The heightened intrinsic activity resulting from loop confinement in enzymes arises synergistically from enthalpic and entropic contributions, which are contingent upon the extent of surface confinement.

A new long-term field study reveals that adding biochar to tropical soils can subtly reshape underground ecosystems, influencing insects and other tiny organisms that play vital roles in forest health.

A new review highlights how a carbon-rich material made from agricultural waste could help reverse land degradation, boost food production, and strengthen climate resilience in some of the world’s most vulnerable regions.

Researchers have developed a new type of engineered biochar that can deliver oxygen in a controlled and stable way, offering a promising solution for environmental remediation and sustainable water and soil management.

A new global analysis reveals that tiny soil microbes play a decisive role in determining whether biochar can effectively lock carbon into agricultural soils, offering new insights for climate change mitigation.

A research group led by Profs. GE Ziyi and LIU Chang at the Ningbo Institute of Materials Technology and Engineering (NIMTE) of the Chinese Academy of Sciences (CAS) has developed an innovative colloidal chemistry strategy to enhance the performance of all-perovskite tandem solar cells (TSCs).

How does the human brain’s electrical activity grow from childhood, peak in adulthood, and decline in older age? A multinational team has tackled this question by linking the brain’s “wiring diagram” and signal‑conduction speed to two familiar features of an electroencephalogram (EEG): the broadband background activity (ξ, pronounced “xi”) and the more rhythmic alpha waves. Their work, published in National Science Review, introduces a new model called Xi–αNET (“Xi–AlphaNET”) that explains how anatomical connections and nerve‑signal delays give rise to these patterns and how they change over the lifespan.

Chinese researchers identified tumor-specific MHC-II (tsMHC-II) expression as a novel biomarker predicting response to neoadjuvant immunotherapy in gastric cancer. Single-cell profiling revealed higher MHC-II expression in tumor cells from treatment-sensitive patients. Validated in prospective trials, tsMHC-II-positive patients achieved 36.67% pathological complete response rate versus low rates in unselected populations. This simple immunohistochemistry-based biomarker outperforms current PD-L1 assessment and could guide personalized therapy for gastric cancer patients.

Sepsis complicated by acute respiratory failure is a severe condition among patients admitted to the intensive care unit (ICU). Researchers have developed and externally validated a machine learning model to predict the 28-day mortality risk in such ICU patients with sepsis. Using routinely collected clinical variables from the first 24 hours after admission, the model demonstrated stable predictive performance in large critical care databases, with potential value for early risk stratification and individualized treatment decision-making.