Solid-state thermoelectric (TE) power generation through the direct conversion of a temperature difference into electricity has received worldwide attention. In this work, a segmented GeTe/BST-SKD TE module is optimized and fabricated to achieve outstanding power density and energy conversion efficiency. To enable high module-level TE performance, p-type GeTe with a ZTmax of 2.47 is used, while p-type BST (Bi2−xSbxTe3, with high ZT in the low temperature range) and n-type SKD (skutterudite, Yb0.3Co4Sb12) are optimized to reach ZTmax values of 1.38 and 1.33, respectively. Furthermore, module structure optimization is conducted using multiphysics-field simulations for three shape factors, including the segmentation ratio, height-to-area ratio, and cross-sectional area ratio between p- and n-type legs. When the three factors are 0.35, 0.67 mm−1, and 1.85, respectively, both the maximum power density and energy conversion efficiency reach their optimal values. Finally, Ni foil is applied at the GeTe/BST interface to serve as both a metallization layer and a diffusion barrier, minimizing internal contact resistances and enhancing high-temperature stability. The maximum power density and energy conversion efficiency of the segmented module are increased by 35% and 21 %, respectively, compared with those of the unsegmented one. The segmented module achieves a substantial power output of ∼0.43 W at ΔT = 500 K, corresponding to a power density of 0.35 W/cm2 per module area and 143 W kg−1 per module weight, with an effective material-level power density of 1.51 W/cm2. The segmented module achieves an outstanding energy conversion efficiency of 12.7%, demonstrating the effectiveness of the optimized leg-segmentation strategy for highperformance solid-state energy harvesting.

Researchers have developed sustainable biochar‑supported mono‑ and bimetallic catalysts from date palm trunk waste for CO₂ methanation. The optimal catalyst (BCNi‑3.0, 4.6 wt% Ni) achieved 58 % CO₂ conversion and 91 % CH₄ selectivity at 400 °C and 1 bar. Increasing pressure to 30 bar enhanced performance to 76 % conversion and nearly 99 % CH₄ selectivity, with stable operation over 20 h. Bimetallic NiFe catalysts promoted CO formation via the reverse water‑gas shift (RWGS) reaction, while K promotion further favored CO production. The study demonstrates that biochar from date palm trunk provides a viable, low‑cost support for CO₂ methanation with competitive performance at significantly reduced metal loading.

Researchers have developed a controllable hydrothermal synthesis strategy for spherical cerium oxide (CeO₂) abrasives with tunable particle sizes from 32 to 531 nm while maintaining excellent sphericity and dispersity. Mechanistic studies reveal that CeO₂ formation occurs via nitrateinduced oxidation of Ce³⁺, with subsequent precipitation coupled to acetate decomposition thermodynamically driving the reaction forward. Chemical mechanical polishing (CMP) tests show that 343 nm CeO₂ achieves the highest material removal rate (73.7 nm·min⁻¹) with low surface roughness (Ra = 0.31 nm). The hydrothermally synthesized CeO₂ outperforms commercial abrasives in both short and longterm polishing owing to its uniform morphology.

Researchers have developed a porous composite sponge (ZnCuCCS) by anchoring ZnS, CuS, and Zn₁₋ₓCuₓS nanoparticles onto a cellulose–chitosan matrix via a facile hydrothermal route. The material exhibits a high porosity of 88.37 %, a narrow bandgap of 1.31 eV, and excellent mechanical stability. In simulated solar irradiation, ZnCuCCS achieves a levofloxacin (LEV) removal efficiency of 90.12 %, maintaining a stable degradation rate of 72.9 % after five reuse cycles. The study elucidates the adsorption–photocatalytic degradation mechanism, demonstrating that photogenerated holes (h⁺) and superoxide radicals (O₂⁻) are the dominant active species.

Researchers have developed a bifunctional catalyst system combining ZnZrOₓ (ZZO) with silanol nest‑enriched silicalite‑1 (AS‑1) for CO₂ hydrogenation coupled with toluene alkylation. The moderate acidity of AS‑1 significantly suppresses side reactions, reducing benzene selectivity from 6.04 % (using conventional ZSM‑5) to 0.5 %, while increasing para‑xylene (PX) selectivity from 23.6 % to 33 %. Epitaxial silicalite‑1 shell coating further enhances shape selectivity, boosting PX selectivity to 44.4 %. Optimizing the spatial distribution of ZZO and AS‑1 increases toluene conversion to 16.3 %. The integrated ZZO/AS‑1@S‑1 catalyst achieves a CO₂ conversion of 9.5 %, toluene conversion of 15 %, and PX selectivity of 57.5 % under optimized conditions.

Researchers have developed a multifunctional flame‑retardant strategy for epoxy resin by incorporating polyphosphazene‑decorated hierarchical nickel phyllosilicate (NiPS@PZN) as a reinforcing and wear‑resistant filler, together with 9,10‑dihydro‑9‑oxa‑10‑phosphaphenanthrene‑10‑oxide (DOPO) as a flame‑retardant synergist. At a total loading of 6 % (1:1 mass ratio), the composite achieves a UL‑94 V‑0 rating and a limited oxygen index of 26.6 %, while total heat release, total smoke production, and CO₂ production are reduced by 10.4 %, 23.2 %, and 26.8 %, respectively. The tensile strength increases to 103.5 MPa and the minimum wear rate reaches 1.78 × 10⁻⁵ mm³·N⁻¹·m⁻¹, demonstrating a breakthrough in overcoming the traditional trade‑off between flame retardancy and mechanical/tribological properties.

Existing electrochemical biosensing systems generally rely on external computing units for data analysis. The necessity of analog-to-digital conversion and data transfer leads to high complexity, low working efficiency and concern of privacy. Zhao et al. realize the in-sensor computing in electrochemical biosensing for the first time, which uses a single neuromorphic organic photoelectrochemical transistor to simultaneously perform multi-target biosensing and artificial neural network classification. The strategy permits in-situ training using real-time data, helping solving the issue of device-to device variations. The proof-of-concept application is demonstrated by direct classification of microRNA fingerprints in real biological samples, which provides a new paradigm for simpler, faster and more secure AI-driven electrochemical biosensing.