Results 91 to 100 of about 5,026 (252)

Autonomous AI‐Driven Design for Skin Product Formulations

Advanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang, Yongrui Xiao, Chunlin Chen, Dimitrios Tsaoulidis, Kai Yang, Tao Chen   +5 more
wiley   +1 more source

Accelerated Tests on Si and SiC Power Transistors with Thermal, Fastand Ultra-Fast Neutrons. [PDF]

Sensors (Basel), 2020
Principato F, Altieri S, Abbene L, Pintacuda F.   +3 more
europepmc   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
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

A Study of the Radiation Tolerance of CVD Diamond to 70 MeV Protons, Fast Neutrons and 200 MeV Pions. [PDF]

Sensors (Basel), 2020
Bäni L, Alexopoulos A, Artuso M, Bachmair F, Bartosik MR, Beck HC, Bellini V, Belyaev V, Bentele B, Bes A, Brom JM, Chiodini G, Chren D, Cindro V, Claus G, Collot J, Cumalat J, Curtoni S, Dabrowski AE, D'Alessandro R, Dauvergne D, De Boer W, Dorfer C, Dünser M, Eigen G, Eremin V, Forneris J, Gallin-Martel L, Gallin-Martel ML, Gan KK, Gastal M, Ghimouz A, Goffe M, Goldstein J, Golubev A, Gorišek A, Grigoriev E, Grosse-Knetter J, Grummer A, Hiti B, Hits D, Hoeferkamp M, Hosselet J, Hügging F, Hutson C, Janssen J, Kagan H, Kanxheri K, Kass R, Kis M, Kramberger G, Kuleshov S, Lacoste A, Lagomarsino S, Giudice AL, Paz IL, Lukosi E, Maazouzi C, Mandić I, Marcatili S, Marino A, Mathieu C, Menichelli M, Mikuž M, Morozzi A, Moscatelli F, Moss J, Mountain R, Oh A, Olivero P, Passeri D, Pernegger H, Perrino R, Picollo F, Pomorski M, Potenza R, Quadt A, Rarbi F, Re A, Reichmann M, Roe S, Rossetto O, Becerra DAS, Schmidt CJ, Schnetzer S, Sciortino S, Scorzoni A, Seidel S, Servoli L, Smith DS, Sopko B, Sopko V, Spagnolo S, Spanier S, Stenson K, Stone R, Stugu B, Sutera C, Traeger M, Trischuk W, Truccato M, Tuvè C, Velthuis J, Wagner S, Wallny R, Wang J, Wermes N, Wickramasinghe J, Yamouni M, Zalieckas J, Zavrtanik M, Hara K, Ikegami Y, Jinnouchi O, Kohriki T, Mitsui S, Nagai R, Terada S, Unno Y.   +118 more
europepmc   +1 more source

Deep Learning Approaches for Classifying Crack States With Overload and Predicting Fatigue Parameters in a Titanium Alloy

Advanced Intelligent Systems, EarlyView.
This study proposes a deep learning approach to evaluate the fatigue crack behavior in metals under overload conditions. Using digital image correlation to capture the strain near crack tips, convolutional neural networks classify crack states as normal, overload, or recovery, and accurately predict fatigue parameters.
Seon Du Choi, Suman Timilsina, Youn Woo Shin, Yong Nam Kwon, Kee‐Sun Sohn, Ji Sik Kim   +5 more
wiley   +1 more source

Defect concentration in clusters, created by fast-pile neutrons in n-Si (FZ, Cz) [PDF]

Ядерна фізика та енергетика, 2007
The dependence of concentration of defects on doping level for average cluster in n-Si was calculated. It was shown that in the framework of the Gossick's model the concentration of defects for the average cluster is in inverse proportion to the square ...
A. P. Dolgolenko, G. P. Gaidar, P. G. Litovchenko   +2 more
doaj  

Predicting Crystal Structures and Ionic Conductivities in Li3YCl6−xBrx Halide Solid Electrolytes Using a Fine‐Tuned Machine Learning Interatomic Potential

Advanced Intelligent Systems, EarlyView.
This study refines the Crystal Hamiltonian Graph Network to predict energies, structures, and lithium‐ion dynamics in halide electrolytes. By generating ordered structural models and using an iterative fine‐tuning workflow, we achieve near‐ab initio accuracy for phase stability and ionic transport predictions.
Jonas Böhm, Aurélie Champagne
wiley   +1 more source

Test of Diamond sCVD Detectors at High Flux of Fast Neutrons

Particles
We have tested the performance of spectroscopic single-crystal Chemical Vapor-Deposited (sCVD) diamond detectors with radioactive sources and with a pulsed deuterium-tritium neutron generator.
Leo Weissman, Asher Shor, Sergey Vaintraub   +2 more
doaj   +1 more source

Spatial Evolution of Coke in ZSM‐5 Catalysts During Methanol‐to‐Hydrocarbons Conversion Revealed by In Situ X‐Ray Photoelectron Spectroscopy

Angewandte Chemie International Edition, EarlyView.
Coking is the central source of deactivation of zeolite catalysts. Using in situ XPS along with complementary methods, we identify differences in the formation kinetics and composition of surface coke on industrially relevant ZSM‐5 catalysts during MTH conversion, demonstrating the strong potential of this technique for studying catalyst deactivation ...
Luca Artiglia, Hannes Frey, Loïc Bénariac‐Doumal, Sung Sik Lee, Przemyslaw Rzepka, Jeroen A. van Bokhoven, Vladimir Paunović   +6 more
wiley   +1 more source

Research on Evaporation Characteristics and Safe Storage Duration of Marine Liquid Hydrogen Storage Tanks Under the Coupling Effect of Heat Leakage and Sloshing

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT Liquid hydrogen, a zero‐carbon and high–energy‐density fuel, is a promising option for future oceangoing vessels. During maritime transportation, onboard cryogenic tanks are exposed to ambient heat leakage and ship‐induced roll motion, which can trigger sloshing and fundamentally modify the coupled thermo‐fluid processes governing boil‐off and
Yan Deng, Wenfeng Wu, Zhenyu Zhao, Rongsheng Lin, Liangyi Sun, Youfei Liu   +5 more
wiley   +1 more source