Results 131 to 140 of about 20,312 (246)

Opportunities of Semiconducting Oxide Nanostructures as Advanced Luminescent Materials in Photonics

Advanced Materials, EarlyView.
The review discusses the challenges of wide and ultrawide bandgap semiconducting oxides as a suitable material platform for photonics. They offer great versatility in terms of tuning microstructure, native defects, doping, anisotropy, and micro‐ and nano‐structuring. The review focuses on their light emission, light‐confinement in optical cavities, and
Ana Cremades, Pedro Hidalgo, David Maestre, Ruth Martínez‐Casado, Emilio Nogales, Beatriz Rodríguez, G. Cristian Vásquez, Bianchi Méndez   +7 more
wiley   +1 more source

Tree ring segmentation performance in highly disturbed trees using deep learning. [PDF]

PLoS One
Zambrano-Suárez JD, Pérez-Martín J, Manchado AM, Ballesteros Cánovas JA.   +3 more
europepmc   +1 more source

LEAD: Literature Enhanced Ab Initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions

Advanced Materials, EarlyView.
Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam, William S. Rogers, Chen‐Yu Hu, Ming‐Yuan Song, Xinyu Bao, Shehrin Sayed, Jean Anne C. Incorvia   +6 more
wiley   +1 more source

Tunable dual-band THz metamaterial absorber with regression-learning-enabled numerical redesign. [PDF]

Sci Rep
Mishu SJ, Banad YM, Sharif S.
europepmc   +1 more source

Topology and Material Optimization in Ultra‐Soft Magneto‐Active Structures: Making Advantage of Residual Anisotropies

Advanced Materials, EarlyView.
Residual magnetization induces pronounced mechanical anisotropy in ultra‐soft magnetorheological elastomers, shaping deformation and actuation even without external magnetic fields. This study introduces a computational‐experimental framework integrating magneto‐mechanical coupling into topology optimization for designing soft magnetic actuators with ...
Carlos Perez‐Garcia, Rogelio Ortigosa, Jesús Martínez‐Frutos, Daniel Garcia‐Gonzalez   +3 more
wiley   +1 more source

Cardiovascular digital twins using a Windkessel physics informed neural network. [PDF]

NPJ Digit Med
Osman D, Sel K, Spatz E, Jafari R.
europepmc   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Dual-band graphene-assisted metamaterial absorber with machine learning integration for high-sensitivity THz biosensing. [PDF]

Sci Rep
Gupta S, Gosi VC, Pareek P, Upender P.
europepmc   +1 more source

Azobenzene's Cross‐Scale Optics and Photonics: Molecular Photoswitching, Mesoscopic Material Motions, and Adaptive Devices

Advanced Materials, EarlyView.
Azobenzene photoswitches translate molecular‐scale E/Z photoisomerization into macroscopic material responses and device‐level photonic functions. This Review highlights how azobenzene research has evolved from molecular photochemistry to photoalignment, mass migration, photomechanics, and heat release, ultimately enabling holography, reconfigurable ...
Heeju Son, Soyul Kwak, Heerin Noh, Minsang Kim, Doyeon Noh, Subhayan Chakraborty, Jaewon Lee, YongDeok Cho, Kyeongsoo Kim, Taewoong Eom, Moonyoung Kim, Jangwon Kim, Hoyoung Lee, Soyoung Lee, Jaehun Kim, Grace G. D. Han, Shin‐Tson Wu, Stefano Oscurato, Arri Priimagi, Marina Saphiannikova, Seungwoo Lee   +20 more
wiley   +1 more source

Comparative Evaluation of Deep Learning Graph Neural Networks and Classical Machine Learning Models for Predicting Heats of Combustion as a Key Hazard Indicator under GHS/CLP Standards. [PDF]

ACS Omega
Nnyigide OS, Byeon H, Okpete UE.
europepmc   +1 more source