On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition

Sokolov, I.

doi:10.1039/d3cp05673b

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Cited by 4 publications

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Mechanical spectroscopy of materials using atomic force microscopy (AFM-MS)

Petrov,

Canena,

Kulachenkov

et al. 2024

Materials Today

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Mechanical spectroscopy of materials using atomic force microscopy (AFM-MS)

Petrov,

Canena,

Kulachenkov

et al. 2024

Materials Today

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Mechanistic Insights and Technical Challenges in Sulfur-Based Batteries: A Comprehensive In Situ/Operando Monitoring Toolbox

Yu,

Pinto-Huguet,

Zhang

et al. 2024

ACS Energy Lett.

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Batteries based on sulfur cathodes offer a promising energy storage solution due to their potential for high performance, cost-effectiveness, and sustainability. However, commercial viability is challenged by issues such as polysulfide migration, volume changes, uneven phase nucleation, limited ion transport, and sluggish sulfur redox kinetics. Addressing these challenges requires insights into the structural, morphological, and chemical evolution of phases, the associated volume changes and internal stresses, and ion and polysulfide diffusion within the battery. Such insights can only be obtained through real-time reaction monitoring within the battery's operational environment, supported by molecular dynamics simulations and advanced artificial intelligence-driven data analysis. This review provides an overview of in situ/operando techniques for real-time tracking of these processes in sulfur-based batteries and explores the integration of simulations with experimental data to provide a holistic understanding of the critical challenges, enabling advancements in their development and commercial adoption.

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Scientific exploration with expert knowledge (SEEK) in autonomous scanning probe microscopy with active learning

Pratiush,

Funakubo,

Vasudevan

et al. 2025

Digital Discovery

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On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition

Abstract: Atomic force microscopy (AFM or SPM) imaging is one of the best matches with machine learning (ML) analysis among microscopy techniques. The digital format of AFM images allows for direct...

Cited by 4 publications

References 48 publications

Mechanical spectroscopy of materials using atomic force microscopy (AFM-MS)

Mechanical spectroscopy of materials using atomic force microscopy (AFM-MS)

Mechanistic Insights and Technical Challenges in Sulfur-Based Batteries: A Comprehensive In Situ/Operando Monitoring Toolbox

Scientific exploration with expert knowledge (SEEK) in autonomous scanning probe microscopy with active learning

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