Results 101 to 110 of about 3,935 (259)

Atomistic Insights Into Lithium Alloying and Crystallization at Metal Interlayers in Zero‐Excess Lithium Batteries

open access: yesAdvanced Energy Materials, EarlyView.
Molecular dynamics simulations with machine learning potentials, combined with experiments, reveal how interlayer metals govern Li alloying and crystallization in zero‐excess lithium batteries. Mg and Zn promote solid‐solution alloy‐mediated pathways that influence Li diffusion and structural uniformity, while Bi forms ordered intermetallics with more ...
Neubi F. Xavier Jr.   +10 more
wiley   +1 more source

Self‐Heating and Thermal Gradient in Solid‐State Batteries: Friend or Foe?

open access: yesAdvanced Energy Materials, EarlyView.
This work investigates the role of self‐heating and thermal gradients derived from the kinetics/transport interactions within the cathode microstructure, electrode architecture, and operating regimes, thus encouraging a mechanism‐centric design of anode‐free solid‐state batteries for next‐generation high‐power applications.
Abhinand Ayyaswamy   +2 more
wiley   +1 more source

Ultra‐Low‐Strain Calcium and Magnesium Ion Storage Enabled by Tunnel‐Structured MoO3 Positive Electrode

open access: yesAdvanced Energy Materials, EarlyView.
A hexagonal tunnel‐structured MoO3 is nanoparticulated via hydrothermal synthesis followed by ball‐milling. As a positive electrode in Ca and Mg batteries, it delivers superior capacity and structural reversibility, enabling divalent cation intercalation with minimal lattice distortion and no phase transitions.
Reona Iimura   +10 more
wiley   +1 more source

A Quantitative Lithium Inventory Framework for Anode‐Free Lithium Metal Batteries

open access: yesAdvanced Energy Materials, EarlyView.
A component‐resolved lithium inventory framework quantitatively tracks Li redistribution across the cell in anode‐free NMC622||Cu pouch cells throughout cycling. Three sequential degradation stages are identified: formation‐driven cathode Li depletion, midlife inactive Li0 accumulation, and late‐stage runaway SEI thickening. The cathode, as the sole Li
Wurigumula Bao   +9 more
wiley   +1 more source

3D‐Printable Nanoporous Thermosets via Disulfide‐Based Polymerization‐Induced Microphase Separation

open access: yesAngewandte Chemie, EarlyView.
α‐Lipoic acid‐derived degradable macro‐chain transfer agents (macroCTAs) with tunable hydrophilicity drive polymerization‐induced microphase separation (PIMS) in photocurable resins. Selective etching via disulfide cleavage then generates interconnected nanoporous thermosets, and compatibility with liquid‐crystal display (LCD) 3D printing enables the ...
Xueheng Dai   +8 more
wiley   +2 more sources

Silicon‐Based Anodes for Sulfide Solid‐State Batteries: Failure Mechanisms and Multiscale Design Strategies

open access: yesAdvanced Energy Materials, EarlyView.
Silicon anodes in sulfide SSBs face coupled electrochemo‐mechanical failure by interface instability. This review examined recent advances and proposed mitigation strategies via material‐, electrode/interface‐, and cell‐level‐ engineering. We further evaluate scalable synthesis of sulfide SEs.
Murugesan Karuppaiah   +4 more
wiley   +1 more source

Nickel‐Free Synthesis of Poly(pyrene‐4,5,9,10‐tetraone) for Sodium‐based Batteries: Insights into Electrode Architecture and Reversible Na‐ion Insertion

open access: yesAdvanced Energy Materials, EarlyView.
A nickel‐free and practical synthesis strategy to poly(pyrene tetraone) and its integration with a percolated CNTs/Ketjen Black network enables stable cycling and efficient energy storage in a sodium‐based batteries. This work demonstrates how controlling polymer structure and electrode architecture improves ion transport and mitigates dissolution in ...
Md. Adil   +9 more
wiley   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

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