Results 101 to 110 of about 6,912 (256)
Chiral‐Nematic Lattice Instability in Charge Density Wave of 1T‐TiSe2
Advanced Science, EarlyView.The charge density wave of 1T‐TiSe2 appears chiral to surface probes yet loses all symmetry in spectroscopic measurements. This study reveals how cooperative condensation of multiple phonon modes stabilizes a chiral monoclinic ground state, while a shallow nematic energy landscape allows the lattice to break the remaining symmetry at negligible cost ...
Abdurrafi Ahsan Alghifari +3 more
wiley +1 more source
DynCFS: a program for modelling dynamic Coulomb failure stress changes in layered elastic media
Geophysical Journal InternationalSUMMARY Coulomb failure stress change (ΔCFS) quantifies the earthquake-induced difference of shear stress and frictional resistance on a receiver fault, with the latter being proportional to the effective normal stress change. ΔCFS has become a widely used measure for studying earthquake triggering, dynamic rupture processes and ...
Jiangcheng Zhou +2 more
openaire +2 more sources
Advanced Science, EarlyView.A hygroscopic hydrogel layer functionalized with fullerene C60 tackles heat and dust challenges in bifacial photovoltaics (PV) panels. It resists soiling and photo‐aging, maintains high transparency, absorbs water vapor from the air during the night, and delivers strong evaporative cooling power during daytime, significantly lowering PV panel ...
Xiaoqi Zhou +6 more
wiley +1 more source
地球与行星物理论评The static and dynamic stress changes induced by earthquakes can further trigger seismic events on surrounding or even remote faults. This phenomenon, known as static and dynamic triggering of earthquakes, has significant implications for earthquake ...
Pengchao He +5 more
doaj +1 more source
Sulfide‐Based Electrolytes for All‐Solid‐State Sodium Batteries
Advanced Energy Materials, EarlyView.This review covers the structural features and synthesis strategies of sulfide‐based solid electrolytes, as well as critical challenges related to conductivity, interfacial and moisture stability, and scaling‐up for practical application in Sodium‐based All Solid‐State Batteries.
Han Yang +6 more
wiley +1 more source
Free Charge Generation in Organic Photoconversion Devices–the Impact of Electrostatic Potentials
Advanced Energy Materials, EarlyView.Free charge generation in organic photovoltaic and photodetector devices typically requires an external driving force to separate bound excitons formed upon photoexcitation. This review details how electrostatic potentials, arising from molecular charge distributions in organic semiconductors, can impact interfacial energetic landscapes and promote ...
Emily J. Yang +3 more
wiley +1 more source
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
Advanced Intelligent Discovery, EarlyView.This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
The impact of Coulomb stress changes of the 2018 Mw 7.5 Palu earthquake, Indonesia
ANAS Transactions, Earth Sciences, 2023 B. Ahadov, Sh. Jin
openaire +1 more source
Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction
Advanced Intelligent Discovery, EarlyView.Multimodal fusion integrates composition, crystal‐structure, and radial‐distribution descriptors to predict battery discharge capacity. Rashomon analysis across near‐optimal models reveals that explanatory variation is structured rather than arbitrary, separating stable mechanistic signals from model‐contingent attributions and providing a more ...
Jue Gong +4 more
wiley +1 more source
Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
Advanced Intelligent Systems, EarlyView.This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
wiley +1 more source