Data processing, analysis and Machine Learning of Electrical Atomic Force Microscopy (AFM) in Data Cube mode: conduction of a nanomaterial for Lithium-ion battery electrodes

Authors: Germanicus, Rosine Coq, De Wolf, Peter, Klapetek, Petr

Source: 2025 IEEE 6th International Conference on Image Processing, Applications and Systems (IPAS) Image Processing, Applications and Systems (IPAS), 2025 IEEE 6th International Conference on. CFP2540Z-ART:1-5 Jan, 2025

Relation: 2025 IEEE 6th International Conference on Image Processing, Applications and Systems (IPAS)

Multi-parameter AFM characterization of INS-1 cells

Authors: Yang, Fan, Wang, Bowei, Wang, Jiajia, Chen, Yujuan, Wang, Zuobin

Source: 2021 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO) Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO), 2021 IEEE International Conference on. :152-155 Aug, 2021

Relation: 2021 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)

The effects of Hertz's model parameters on analysis of atomic force microscopy (AFM) data for cardiac cell

Authors: Soufivand, Anahita Ahmadi, Navidbakhsh, Mehdi

Source: 2012 19th Iranian Conference of Biomedical Engineering (ICBME) Biomedical Engineering (ICBME), 2012 19th Iranian Conference of. :163-166 Dec, 2012

Relation: 2012 19th Iranian Conference of Biomedical Engineering (ICBME)

The Young’s Modulus as a Mechanical Biomarker in AFM Experiments: A Tool for Cancer Diagnosis and Treatment Monitoring

Authors: Kontomaris, Stylianos - Vasileios, Malamou, Anna, Stylianou, Andreas

Source: Sensors (Basel)
Sensors, Vol 25, Iss 11, p 3510 (2025)
Sensors

Subject Terms: AFM data processing, Chemical technology, TP1-1185, Review, Microscopy, Atomic Force, cancer diagnosis, contact mechanics, Neoplasms, Elastic Modulus, Biomarkers, Tumor, Humans, mechanical distributions, combined therapies, Biomarkers

Access URL: https://pubmed.ncbi.nlm.nih.gov/40969054
https://doaj.org/article/170741868f0a4d75831050afe3f55306

Simplifying Data Processing in AFM Nanoindentation Experiments on Thin Samples

Authors: Stylianos Vasileios Kontomaris, Anna Malamou, Andreas Stylianou

Source: Eng, Vol 6, Iss 2, p 32 (2025)

Subject Terms: simplifying fitting process, adherent samples, Buckle’s rule, cells, AFM in medicine, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://www.mdpi.com/2673-4117/6/2/32; https://doaj.org/toc/2673-4117

Access URL: https://doaj.org/article/3310cadf0f4a4e33a8b38e9683f6b37a

Image Segmentation of Bacterial Biofilms to Study Pathogen-Surface Interactions

Authors: Madugula, Sita Sirisha, Dumerer, Blythe, Millan-Solsona, Ruben, Marti, Checa Nualart, Collins, Liam, Vasudevan, Rama K, Scott, Retterer, Zhang, Lance, Cox, Spencer, Morrell-Falvey, Jennifer L

Source: 2024 IEEE International Conference on Big Data (BigData) Big Data (BigData), 2024 IEEE International Conference on. :4939-4940 Dec, 2024

Relation: 2024 IEEE International Conference on Big Data (BigData)

Advancing High-Throughput Cellular Atomic Force Microscopy with Automation and Artificial Intelligence

Authors: Thomas- -Chemin, Ophélie, Janel, Sébastien, Boumehdi, Zeyd, Séverac, Childérick, Trevisiol, Emmanuelle, Dague, Etienne, Duprés, Vincent

Contributors: DAGUE, Etienne, Équipe Ingénierie pour les sciences du vivant (LAAS-ELIA), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 (CIIL), Institut Pasteur de Lille, Pasteur Network (Réseau International des Instituts Pasteur)-Pasteur Network (Réseau International des Instituts Pasteur)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire CHU Lille (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), Geroscience and rejuvenation research center (RESTORE), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-EFS-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Source: ACS Nano. 19:5045-5062

Subject Terms: AFM data processing, [SDV.IB] Life Sciences [q-bio]/Bioengineering, MESH: Microscopy, MESH: Humans, High-throughput AFM, Atomic Force, Mechanical properties, Cellular analysis, Microscopy, Atomic Force, Artificial intelligence (AI), Bio-AFM, Automation, MESH: Automation, Artificial Intelligence, Atomic force microscopy (AFM), Machine learning, MESH: Artificial Intelligence, Humans, Animals, [SDV.IB]Life Sciences [q-bio]/Bioengineering

File Description: application/pdf

Access URL: https://pubmed.ncbi.nlm.nih.gov/39883411
https://ut3-toulouseinp.hal.science/hal-04956507v1
https://ut3-toulouseinp.hal.science/hal-04956507v1/document
https://doi.org/10.1021/acsnano.4c07729

Automatic Analysis of Conjugated Polyelectrolyte–DNA Interactions Based on Sequential Analysis of AFM Imaging

Authors: Chworos, A., Obara, B.

Source: IEEE Transactions on Nanotechnology IEEE Trans. Nanotechnology Nanotechnology, IEEE Transactions on. 8(4):457-462 Jul, 2009

Fast AFM Imaging Based on Neural Network Compressed Sensing

Authors: Sun, Meng, Chen, Na, Li, Shaoying, Liu, Zhenmin, Ye, Shuai, Shang, Yana, Liu, Shupeng, Pang, Fufei, Wang, Tingyun

Source: 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP) Multimedia Signal Processing (MMSP), 2022 IEEE 24th International Workshop on. :1-5 Sep, 2022

Relation: 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)

FRAME (Force Review Automation Environment): MATLAB-based AFM data processor

Authors: Kostyantyn R, Partola, George, Lykotrafitis

Source: Journal of Biomechanics. 49:1221-1224

Subject Terms: 0301 basic medicine, Automation, 0303 health sciences, 03 medical and health sciences, Elastic Modulus, 11. Sustainability, Image Processing, Computer-Assisted, Humans, Microscopy, Atomic Force, Software

Access URL: http://manuscript.elsevier.com/S0021929016301932/pdf/S0021929016301932.pdf
https://pubmed.ncbi.nlm.nih.gov/26972765

OpenFovea: open-source AFM data processing software

Authors: Roduit C, Saha B, Alonso-Sarduy L, Volterra A, Dietler G, Kasas S

Source: Nat Methods

Subject Terms: 0301 basic medicine, Electronic Data Processing, 0303 health sciences, 03 medical and health sciences, Macrophages, Computational Biology, Microscopy, Atomic Force, Software

Access URL: https://pubmed.ncbi.nlm.nih.gov/22847110

Design of Object Segmentation and Feature Extraction Based On Deep Learning for AFM Image Processing and Analysis System.

Authors: Zhang, Minkang¹ (AUTHOR) minkangz2025@gmail.com

Source: Procedia Computer Science. 2025, Vol. 262, p982-991. 10p.

Subject Terms: Object recognition (Computer vision), Atomic force microscopes, Feature extraction, Image analysis, Deep learning, Image segmentation

Understanding Uncertainty and Error in Slip Length Measured by Atomic Force Microscopy (AFM)

Authors: Liu, Zehui, Author, Laaksonen, Aatto, Author, Mu, Liwen, Author, Zhu, Jiahua, Author, Lu, Xiaohua, Author, Ji, Xiaoyan, Author

Contributors: Liu, Zehui, Author, Laaksonen, Aatto, Author, Mu, Liwen, Author, Zhu, Jiahua, Author, Lu, Xiaohua, Author, Ji, Xiaoyan, Author

Source: Langmuir. 42(29):21344-21354

Subject Terms: Atomic force microscopy, Hydrophobicity, Surface roughness, Hydrodynamics, Fluids, Energiteknik, Energy Engineering

File Description: electronic

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-119205
https://ltu.diva-portal.org/smash/get/diva2:2091117/FULLTEXT01.pdf

Accurate Modelling of AFM Force-Indentation Curves with Blunted Indenters at Small Indentation Depths

Authors: Stylianos Vasileios Kontomaris, Anna Malamou, Andreas Stylianou

Source: Micromachines, Vol 15, Iss 10, p 1209 (2024)

Subject Terms: sphero-conical indenter, biological materials, blunt pyramid, Young’s modulus, data processing, mathematical modelling, Mechanical engineering and machinery, TJ1-1570

File Description: electronic resource

Relation: https://www.mdpi.com/2072-666X/15/10/1209; https://doaj.org/toc/2072-666X

Access URL: https://doaj.org/article/c018afebc5a34e458602a7bdf39801fd

A Review on Advanced AFM and SKPFM Data Analytics for Quantitative Nanoscale Corrosion Characterization

Authors: Mohammad Reza Attar, Ali Davoodi

Source: Corrosion and Materials Degradation, Vol 6, Iss 4, p 58 (2025)

Subject Terms: corrosion mechanisms, AFM, SKPFM, advanced data analysis, surface heterogeneity, Chemical technology, TP1-1185

File Description: electronic resource

Relation: https://www.mdpi.com/2624-5558/6/4/58; https://doaj.org/toc/2624-5558

Access URL: https://doaj.org/article/44e182261d4e461f9610f1b190b35aa7

CrystalTracer: Automated Image Analysis of In Situ AFM Data Uncovers the Kinetics of Crystal Growth

Authors: Oluoch, Luke^aff1, Tao, Jinhui^aff2, Clancy, Paulette^aff1

Source: Crystal Growth & Design. 26(16):6556-6573

Availability: https://pubs.acs.org/doi/10.1021/acs.cgd.6c00581

Application of ensemble machine learning methods to multidimensional AFM data sets.

Authors: Dokukin, Maxim¹ (AUTHOR) MEDokukin@mephi.ru, Dokukina, Irina¹ (AUTHOR)

Source: Procedia Computer Science. 2020, Vol. 169, p763-766. 4p.

Subject Terms: Machine learning, Atomic force microscopy, Data reduction, Electronic data processing

Improvement of Signal Processing Procedure of the Analysis of Atomic Force Microscopy (AFM) Data of Mineral Cleavage Planes / 鉱物劈開面の原子間力顕微鏡データ解析における信号処理法の改善

Authors: Satoru KAKITANI, Tadashi OKUNO, 奥野 雅史, 柿谷 悟

Source: 鉱物学雜誌 / Journal of the Mineralogical Society of Japan. 1999, 28(4):167

The Hertzian theory in AFM nanoindentation experiments regarding biological samples: Overcoming limitations in data processing

Authors: Kontomaris, S.V., primary, Malamou, A., additional, Stylianou, A., additional

Source: Micron. 2022/04/01

An assessment of errors in sample preparation and data processing for nanoparticle size analyses by AFM

Authors: Souza, T.G.F., primary, Ciminelli, V.S.T., additional, Mohallem, N.D.S., additional

Source: Materials Characterization. 2015/11/01