Results 221 to 230 of about 20,207 (328)
LiMn2O4${\rm LiMn}_2{\rm O}_4$ is of high interest for sustainable energy storage and conversion. Its mixed‐valence Mn3+${\rm Mn}^{3+}$/Mn4+${\text{Mn}}^{4+}$ structure drives site‐specific behavior at aqueous interfaces, featuring proton‐generating Mn4+${\text{Mn}}^{4+}$ centers and electron‐rich Mn3+${\rm Mn}^{3+}$ sites that render the surrounding ...
K. Nikolas Lausch +3 more
wiley +2 more sources
Single-Metal-Atom Chains at Mirror Twin Boundaries in Transition Metal Dichalcogenides: Electronic, Magnetic, and Catalytic Properties. [PDF]
Santra P +3 more
europepmc +1 more source
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +1 more source
Compositional and Structural Diversity in Cesium Superhydrides at High Pressure
Through high pressure‐high temperature diamond anvil cell experiments combined with density functional theory calculations, cesium is demonstrated to have remarkably complex and diverse hydride chemistry culminating in the formation of CsH25${\rm CsH}_{25}$, the most hydrogen‐rich metal hydride reported to date.
Mikhail A. Kuzovnikov +7 more
wiley +2 more sources
Combinatorial asymmetric acoustic metamaterials with real-time programmability. [PDF]
Keogh MR, Bilal OR.
europepmc +1 more source
Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation
Predicting a crystal’s structure from composition alone is a long‐standing challenge in materials discovery. The CSP180 benchmark of 180 inorganic crystals evaluates thirteen crystal structure prediction algorithms requiring no density functional theory (DFT) against DFT‐based baselines across twelve metrics.
Lai Wei +9 more
wiley +1 more source
Revealing Hidden Raman Signatures Through Attention‐Based Spectral Unmixing
Weak Raman signatures are recovered from background‐dominated spectra using a transformer‐based, reference‐free spectral unmixing AI framework. Self‐attention reconstructs substrate contribution directly from mixed data, enabling reliable extraction of previously inaccessible vibrational features.
Dmitriy A. Poteryayev +9 more
wiley +1 more source
Impacts of Increasing Low-Level Shear on Supercells during the Early Evening Transition*
Brice E Coffer, M. Parker
semanticscholar +1 more source
Selective sensing characteristics of Fe doped and (Fe, N, O) co-doped molybdenum disulfide toward CO, CO<sub>2</sub>, and NH<sub>3</sub> gases: a first-principles investigation. [PDF]
Ho TH +7 more
europepmc +1 more source

