The practical deployment of lithium–sulfur (Li–S) batteries is hampered by the shuttle effect of lithium polysulfides (LiPSs) and sluggish kinetics of sulfur redox reaction. While prevailing research focuses on maximizing the intrinsic activity of catalysts, the critical role of the host architecture in modulating the local reaction microenvironment remains overlooked. Herein, we report a facile ion-exchange strategy to synthesize double-shell hollow Prussian blue analogue derivative (Co_2.5Fe/NC) as advanced sulfur hosts by precisely controlling the Co/Fe molar ratio at 2.5. Finite element simulations and in situ diagnostics reveal that the double-shell structure orchestrates a self-propelled electrolyte flow within the nanoreactor during operation. This dynamic flow effectively mitigates the spatial concentration heterogeneity of LiPSs, especially near the catalysts, thereby preventing active material passivation and ensuring sustained high catalytic efficiency. Consequently, even with a catalyst of moderate intrinsic activity, the Co_2.5Fe/NC cathode achieves exceptional stability under lean electrolyte conditions (0.016% decay per cycle over 1000 cycles at 2 C). More importantly, an Ah-level pouch cell delivers a high energy density of 454.7 Wh kg⁻¹. This work shifts the paradigm from solely pursuing catalytic activity to designing host structures that intelligently manage the electrochemical microenvironment, offering a new avenue toward practical Li–S batteries.

Rice, wheat, bamboo, and the grasses growing along roadsides all belong to the Poaceae family—a plant family that underpins global food security, terrestrial ecosystems, and biodiversity. A new study published in the 2026 Issue 4 of Biodiversity Science presents the most comprehensive and up-to-date checklist of grasses in China, documenting 2,304 grass species in 283 genera, 37 tribes, 7 supertribes, and 10 subfamilies. Together, these account for approximately one-sixth of the world's grass species, reinforcing China's position as one of the world's most important centers of grass diversity.

 Researchers have developed ACGC+, an enhanced atomic connectivity group contribution method that improves the prediction of nine key phase transition properties for organic compounds, including entropy of fusion, enthalpy of vaporization, boiling point, melting point, and critical properties. By introducing local atomic connectivity factors (ACFs+) that capture both global and local structural influences, the new method outperforms the original ACGC approach, reducing mean absolute errors by 1.44% to 7.91%. External validation and cross-validation confirmed the model's robustness, with R² values ranging from 0.845 (melting point) to 0.998 (critical volume).

Researchers have systematically evaluated 14 polyglycol‑based foam control agents under simulated industrial fermentation conditions, investigating their foam suppression efficiency and microbial compatibility. FCA332 emerged as the superior candidate, combining low viscosity, favorable surface tension, and moderate cloud point with consistent foam control and excellent biocompatibility. A predictive model was developed to estimate foam volume and microbial growth based on FCA physicochemical properties, achieving R² values of 0.97 and 0.99, respectively. This model enables rapid screening of FCAs without extensive biological assays, accelerating product development for fermentation applications.

Type 2 diabetes (T2D) develops through the long-term interplay of genetic predisposition, lifestyle, and environmental exposures. Residential greenness is increasingly studied as a measurable environmental exposure that may be related to diabetes risk. However, prospective evidence from Chinese populations remains limited, and whether genetic susceptibility modifies the greenness–T2D association, particularly in terms of absolute risk reduction, has remained unclear.

Cold snaps can restrict tea growth, damage young leaves, and reduce crop yield and quality, yet the molecular links between hormone signaling and cold defense have remained unclear. 

Nitrilases are critical industrial biocatalysts for converting nitriles into high-value carboxylic acids, yet their application is often hindered by the formation of insoluble inclusion bodies during heterologous expression. A new study introduces a robust engineering strategy utilizing superfolder green fluorescent protein (sfGFP) fusion to screen for solubility-enhancing mutations. By targeting surface hydrophobic residues, researchers successfully developed a triple mutant, PD1Mut3, which exhibits significantly improved soluble yield, thermostability, and catalytic efficiency, offering a generalizable platform for optimizing enzyme production.

A localized study in Tiaty, Kenya, shows how climate variability and land-use change are reshaping pastoral and agro-pastoral livelihoods. Using satellite imagery, climate records, agroecological information, and socioeconomic data, researchers found shifts in vegetation, rainfall seasonality, livestock choices, and settlement patterns. The findings highlight the need for locally tailored adaptation strategies that support resilient livestock systems, sustainable rangeland management, and climate-smart farming.

Apple Valsa canker, caused by the fungus Valsa mali, ranks among the most destructive diseases threatening global apple production. 

A new study published in Big Earth Data proposes an operational framework that integrates multiple machine learning and deep learning models with high-resolution Sentinel-2 data to estimate agricultural drought conditions in irrigated and non-irrigated maize fields in Eastern Austria. Results from 2018–2023 show that deep learning models, particularly the Deep Neural Network (DNN), achieved the best performance, while non-irrigated fields exhibited higher prediction accuracy due to clearer drought signals, demonstrating the value of Sentinel-2–based deep learning for operational drought monitoring.