Results 111 to 120 of about 2,733 (260)

Mechanical properties of crystalline-amorphous composites: generalization of Hall-Petch and inverse Hall-Petch behaviors. [PDF]

Natl Sci Rev
Xu Z, Li M, Han Y.
europepmc   +1 more source

Engineering Na‐Rich P2‐Type Layered Oxides Through Li/Ti Dual Doping for Oxygen Redox Activation and Superior Structural Stability

Advanced Energy Materials, EarlyView.
P2‐type sodium layered oxides have potential for high‐voltage operation but suffer from structural instability and capacity fading. This work demonstrates that synergistic Li and Ti co‐doping enhances sodium inventory, suppresses detrimental phase transitions, and activates reversible lattice oxygen redox.
Rishika Jakhar, Shrestha Ghosh, Adesh Rohan Mishra, Shristi Pradhan, Debalina Sarkar, Yuanlong Bill Zheng, Zengqing Zhuo, Tianyi Li, Lu Ma, Minghao Zhang, Shyue Ping Ong, Matthew Li, Leeann Sun, Prabhat Thapliyal, Jing Wang, Abhik Banerjee, Ying Shirley Meng   +16 more
wiley   +1 more source

Degradation Pathways of Silicon‐Based Anodes in Lithium‐Ion Batteries

Advanced Energy Materials, EarlyView.
Silicon‐based anodes undergo degradation through five primary pathways: (1) mechanical and structural deterioration of the active material, (2) loss of electrode integrity and electrical contact, (3) mechanical instability of the solid electrolyte interphase (SEI), characterized by repetitive fracture and deformation, (4) chemical instability of the ...
Yoon Jeong Choi, Ji‐Youn Bae, Ga‐On Park, Seung‐Ho Yu   +3 more
wiley   +1 more source

Modelling Structural Material Damage Using the Cohesive Zone Approach Under Operational Conditions. [PDF]

Materials (Basel)
Kozák V, Vala J, Derevianko A.
europepmc   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

A comprehensive review on rubber wear modeling with focus on tire applications. [PDF]

iScience
Maglione R, Farroni F, Genovese A, Napolitano Dell'Annunziata G, Timpone F, Sakhnevych A.   +5 more
europepmc   +1 more source

Which Method Best Predicts Postoperative Complications: Deep Learning, Machine Learning, or Conventional Logistic Regression?

Annals of Gastroenterological Surgery, EarlyView.
Deep learning has shown promise in predicting postoperative complications, particularly when using image or time‐series data. However, on tabular clinical data such as the NCD, it often underperforms compared to conventional machine learning. Integrating multimodal data may enhance predictive accuracy and interpretability in surgical care.
Ryosuke Fukuyo, Masanori Tokunaga, Hiroyuki Yamamoto, Hideki Ueno, Yusuke Kinugasa   +4 more
wiley   +1 more source

Evolution mechanisms of mining-induced overlying strata structures: a case study in the Qinshui coalfield. [PDF]

Sci Rep
Zou Y, Wang Z, Song M, Chai H, Wang H, Chen F.   +5 more
europepmc   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

A numerical design framework for unloading excavations in deep potash mining. [PDF]

Sci Rep
Zhang Y, Lapatsin S, Zhuravkov M, Yu G, He Y, Piaredryi P.   +5 more
europepmc   +1 more source