Researchers have established a cross‑scale framework combining in situ visualization and multiphase flow simulations to elucidate droplet transport on fibrous membranes with asymmetric wettability. The study reveals that fiber diameter and curvature govern droplet nucleation, adhesion, and migration at the single‑fiber scale; inter‑fiber spacing and pore size determine liquid‑bridge formation and transport pathways at the network scale; and wettability gradients coupled with layer stacking drive the transition from surface spreading to through‑thickness penetration at the multilayer scale. A coarse‑to‑fine Janus membrane achieved rapid penetration, a high water vapor transmission rate, and excellent fog‑harvesting performance.

Researchers have systematically investigated how support properties govern the in‑situ carburization of MoO₃ to active Mo₂C for the reverse water‑gas shift reaction. Among six oxide supports, Mo/SiO₂ exhibited the weakest metal oxide‑support interaction, the highest surface Mo₂C concentration, and the best mass‑specific activity. The ionic potential of the support serves as a quantitative descriptor for metal oxide‑support interaction strength: acidic supports preserve MoO₃ crystallites that readily carburize, while basic supports form stable solid solutions that suppress Mo₂C formation. This work provides clear support selection criteria for rational design of high‑performance Mo‑based RWGS catalysts.

Researchers have developed a coordinate‑aware Mamba2 framework (CA‑Mamba2) for predicting the remaining useful life of lithium‑ion batteries. By integrating a coordinate feature attention network and a swiGLU‑gated residual network with the state‑space Mamba2 backbone, the model effectively captures both long‑term degradation trends and local fluctuation patterns. On the NASA, Tongji University, and Xi’an Jiaotong University datasets under single‑variable, multivariate, and cross‑dataset generalization settings, the model achieved mean absolute error values below 0.0098, 0.0016, and 0.0083, respectively, with superior computational efficiency and robustness across different prediction starting points.

Researchers in China propose a physics-data co-driven deep neural network. By embedding coupled mode theory into a data-driven deep neural network, this framework achieves rapid and precise prediction of physical parameters in complex coupled resonant systems, deciphering their underlying physical properties in a unified way. As a proof-of-principle experiment, the precise measurement of nanometer-scale displacements is demonstrated. This study not only provides a new method for analyzing the underlying physical mechanisms of coupled resonant systems but also opens up a new avenue for high-precision sensing applications.

Developing self-powered optical sensors with high sensitivity and wavelength-selective photocurrent amplification is crucial for advancing modern optoelectronics. Towards this goal, Scientists in Thailand developed a perovskite photodetector with an energy-selective broadband photocurrent amplification by using a chocolate-chip-cookie-like hybrid architecture. Embedding CsPbBr₃ micro-droplets (chips) within a triple-cation perovskite light-absorber matrix (cookie) redistributes the light propagation and creates energy-funneling pathways for efficient charge transfer from chips to cookie matrix under ultraviolet and green illumination, while scalable coating techniques ensure industrial marketability.

A research team has developed hierarchical ZSM-5 zeolite catalysts whose pore structure and acidity can be adjusted through a simple one-pot synthesis, enabling longer-lasting conversion of plastic waste into gasoline-range liquids and valuable aromatic chemicals.