Results 131 to 140 of about 24,777 (278)
The densification process of Li6PS5Cl powders with varying particles size distributions reveals differences in smaller and larger distributions. Higher strain is revealed for the smaller particle size distribution from X‐ray diffraction. Discrete element method simulations uncover that the reason for the higher strain is not the particle size itself ...
Vasiliki Faka +14 more
wiley +1 more source
Role of Wadsley Defects and Cation Disorder to Enhance MoNb12O33 Diffusion
Wadsley‐Roth niobates are high‐rate capable and durable anode materials for lithium‐ion batteries. Defect‐tailoring of MoNb12O33 is shown to substantially enhance lithium diffusion. Computational models were used to separate the effects of cation disorder and Wadsley defects to identify that both led to the occupation of fast diffusion sites at lower ...
CJ Sturgill +10 more
wiley +1 more source
Stage‐Specific Roles of Deep Eutectic Solvents in Recycling of Spent Lithium‐Ion Batteries
Deep eutectic solvents (DESs) offer tunable acidity, redox, and coordination properties for selective recycling of spent lithium‐ion battery cathodes. Through co‐dissolution, single‐ and two‐metal separations, DESs enable sustainable recovery of critical metals for closed‐loop regeneration of battery‐grade materials, advancing a circular economy for ...
Jingxiu Wang +4 more
wiley +1 more source
Artificial Intelligence for Bone: Theory, Methods, and Applications
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan +3 more
wiley +1 more source
A novel qutrit representation for RGB digital images. [PDF]
Rofail M, Montaser R, Younes A.
europepmc +1 more source
Deep Learning‐Assisted Design of Mechanical Metamaterials
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong +5 more
wiley +1 more source
Ternary Logic Design Based on Novel Tunneling-Drift-Diffusion Field-Effect Transistors. [PDF]
Lu B +7 more
europepmc +1 more source
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
A Universal Implementation Approach for Multivalued Logic Gates Based on Negative Transconductance in Series-Connected Two-Dimensional Transistors. [PDF]
Zhao G +6 more
europepmc +1 more source

