Results 211 to 220 of about 6,554,333 (291)
HfxZr1−xO2${\rm Hf}_x{\rm Zr}_{1-x}{\rm O}_2$ offers CMOS‐compatible nanoscale ferroelectricity yet suffers from a high Ec${\rm E}_c$ demanding large operating voltages. A unified phase‐field framework spanning AFE/FE/DE phases shows how FE grains soften neighboring AFE grains over λ$\lambda$ ≈$\approx$ 22–37 nm.
P. Pankaj +4 more
wiley +1 more source
Tracking Battery Microstructural Degradation Through Directional Thermal Transport Signatures
In lithium‐ion batteries, heat conduction varies between the through‐plane and in‐plane directions because of the cell's layered structure. Therefore, cell degradation influences thermal conductivity in each direction uniquely due to detailed microstructural and compositional changes.
Mohammad Shoghi Tekmedash +6 more
wiley +1 more source
The cognitive anchor system: a structural model of self, task selection, and latent intention in human cognition. [PDF]
Li J.
europepmc +1 more source
Load Distributing Metamaterials Via Discrete Optimization
Mechanical metamaterials are computationally optimized to homogenize transmitted forces by minimizing the spread of reaction forces. The resulting architectures transform localized loading into broader, more uniform force distributions and experimentally demonstrate robust load spreading under quasi‐static and impact loading.
Andrea Detry +6 more
wiley +1 more source
Operationalising 'what matters'-will we ever find a way to patient-centred care? [PDF]
Schwarze M, Telma K, Taylor LJ.
europepmc +1 more source
A gas‐fed, zero‐gap, PEM CO2 electrolyzer is realized by incorporating PDDA+ ions onto the carbonaceous Co/N‐C electrocatalyst, with gaseous H2 and CO2 fed into the anode and cathode, respectively. Operating without an aqueous electrolyte, the system sustains a peak FECO of 65.1% at 100 mA cm−2. ABSTRACT Electrochemical carbon dioxide reduction (ECO2R)
Yuen Leong Chow +6 more
wiley +1 more source
Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang +3 more
wiley +1 more source

