Machine learning assisted discovery of high-efficiency self-healing epoxy coating for corrosion protection

Gate-tunable kinetic inductance parametric amplifier

Unraveling the hydrogen sulfide aging mechanism on electrical-thermal–mechanical property degradation of sintered nanocopper interconnects used in power electronics packaging

Release of phosphorus through pretreatment of waste activated sludge differs essentially from that of carbon and nitrogen resources: Comparative analysis across four wastewater treatment facilities

Nanostructure and damage characterisation of bitumen under a low cycle strain-controlled fatigue load based on molecular simulations and rheological measurements

Introducing a force-matched united atom force field to explore larger spatiotemporal domains in molecular dynamics simulations of bitumen

Advancements and insights in thermal and water management of proton exchange membrane fuel cells: Challenges and prospects

High dielectric filler for all-solid-state lithium metal battery

Effect of microporous layer structural parameters on heat and mass transfer in proton exchange membrane fuel cells

The Effect of Psychological Scarcity on Health Decisions of Rural Residents in China: Preliminary Results

SMI2PDB: A self-contained Python tool to generate atomistic systems of organic molecules using their SMILES notations

Creation of memory–memory entanglement in a metropolitan quantum network

Rectification in Ionic Field Effect Transistors Based on Single Crystal Silicon Nanopore

Benchmarking of hardware-efficient real-time neural decoding in brain-computer interfaces

An Accurate Propagator for Heterogeneous Media in Full Wavefield Migration

FedViT: Federated continual learning of vision transformer at edge

Plastic particles affect N₂O release via altering core microbial metabolisms in constructed wetlands

PDB2DAT: Automating LAMMPS data file generation from PDB molecular systems using Python, Rdkit, and Pysimm

General Optimal Trajectory Planning: Enabling Autonomous Vehicles with the Principle of Least Action

Physics-Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation