Results 181 to 190 of about 333,902 (325)

Indirect NRDF for Partially Observable Gauss-Markov Processes with MSE Distortion: Complete Characterizations and Optimal Solutions [PDF]

, 2020
Fotios Stavrou, Mikael Skoglund
openalex   +1 more source

Passivation‐Induced Species Dynamics and Microstructural Evolution in Solid‐State Lithium–Sulfur Cathodes

Advanced Science, EarlyView.
The formation of insulating Li2S during discharge in solid‐state lithium–sulfur batteries passivate reaction sites and limits sulfur utilization. In this work, a microstructure‐resolved modeling framework coupling transport and reaction kinetics is developed to predict charge–discharge behavior and reveal particle‐scale species evolution and incomplete
Arpan K. Sharma, Bairav S. Vishnugopi, Elif Pınar Alsaç, Matthew T. McDowell, Partha P. Mukherjee   +4 more
wiley   +1 more source

Stacking ensemble machine learning for predicting photodetector performance under varying illumination intensities. [PDF]

Sci Rep
Öter A, Berktaş Z, Ersöz B, Sağıroğlu Ş, Orhan E.   +4 more
europepmc   +1 more source

BioMat@MSE 2010 [PDF]

Advanced Engineering Materials, 2010
openaire   +1 more source

Machine Learning Driven Window Blinds Inspired Porous Carbon‐Based Flake for Ultra‐Broadband Electromagnetic Wave Absorption

Advanced Science, EarlyView.
Inspired by the regulation mechanism of window blinds, this study designs an electromagnetic wave‐absorbing metamaterial. By introducing the magneto‐electric coupling concept and integrating it with an artificial intelligence‐based data‐driven collaborative optimization strategy, the material optimizes impedance matching performance and enhances loss ...
Zhe Wang, Wanchong Li, Lu Feng, Zaiqing Yang, Shicheng Jin, Dongxu Zhao, Xiaoyong Wu, Yan Wang, Yu Mao, Jinsong Zhang   +9 more
wiley   +1 more source

Determining Material Removal and Electrode Wear in Electric Discharge Machining with a Generalist Machine Learning Framework. [PDF]

Materials (Basel)
Cortés-Mendoza JM, Żyra A, Tchernykh A, González-Vélez H.   +3 more
europepmc   +1 more source

High‐Conductivity Electrolytes Screened Using Fragment‐ and Composition‐Aware Deep Learning

Advanced Science, EarlyView.
We present a new deep learning framework that hierarchically links molecular and functional unit attributions to predict electrolyte conductivity. By integrating molecular composition, ratios, and physicochemical descriptors, it achieves accurate, interpretable predictions and large‐scale virtual screening, offering chemically meaningful insights for ...
Xiangwen Wang, Muyang Chen, Gengyi Bao, Yan Lai, Jinghe Cao, Xinhua Liu, Rui Tan   +6 more
wiley   +1 more source

Reply to Eltawil et al. Comment on "Iacobescu et al. Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities. <i>J. Cardiovasc. Dev. Dis.</i> 2024, <i>11</i>, 396". [PDF]

J Cardiovasc Dev Dis
Iacobescu P, Marina V, Anghel C, Anghele AD.   +3 more
europepmc   +1 more source

MS/MSE [PDF]

, 2019
openaire   +1 more source

Synergistic Effects of Solid Electrolyte Mild Sintering and Lithium Surface Passivation for Enhanced Lithium Metal Cycling in All‐Solid‐State Batteries

Advanced Science, EarlyView.
This study proposes a synergistic strategy combining mild sintering of solid electrolyte pellets with lithium metal surface passivation, effectively suppressing dendrite growth, and enhancing interfacial stability. This approach doubles the critical current density of symmetric cells (1.1 to 2.2 mA cm−2) and extends full cells cycle life at 1 mA cm−2 ...
Jinsong Zhang, Robin N. Wullich, Thomas J. Schmidt, Mario El Kazzi   +3 more
wiley   +1 more source