Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Quantifying nanoscale forces using machine learning in dynamic atomic force microscopy
Abhilash Chandrashekar, Pierpaolo Belardinelli, Miguel A. Bessa, et al.
Nanoscale Advances (2022) Vol. 4, Iss. 9, pp. 2134-2143
Open Access | Times Cited: 24

Showing 24 citing articles:

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics
Hong Zhou, Liangge Xu, Zhihao Ren, et al.
Nanoscale Advances (2022) Vol. 5, Iss. 3, pp. 538-570
Open Access | Times Cited: 73
Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
Hanxun Jin, Enrui Zhang, Horacio D. Espinosa
Applied Mechanics Reviews (2023) Vol. 75, Iss. 6
Open Access | Times Cited: 72
Enzymes and enzymatic mechanisms in enzymatic degradation of lignocellulosic biomass: A mini-review
Hongliang Guo, Ying Zhao, Jo‐Shu Chang, et al.
Bioresource Technology (2022) Vol. 367, pp. 128252-128252
Closed Access | Times Cited: 66
Advances of machine learning in materials science: Ideas and techniques
Sue Sin Chong, Yi Sheng Ng, Hui‐Qiong Wang, et al.
Frontiers of Physics (2023) Vol. 19, Iss. 1
Open Access | Times Cited: 26
Nonlinear modeling of nanoscale interaction forces between atomic force microscope and carbon nanotubes
Moharam Habibnejad Korayem, Rouzbeh Nouhi Hefzabad
International Journal of Non-Linear Mechanics (2024) Vol. 161, pp. 104690-104690
Closed Access | Times Cited: 5
Machine learning approaches for improving atomic force microscopy instrumentation and data analytics
Nabila Masud, Jaydeep Rade, Md. Hasibul Hasan Hasib, et al.
Frontiers in Physics (2024) Vol. 12
Open Access | Times Cited: 5
A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks
Roberto Perera, Vinamra Agrawal
Mechanics of Materials (2023) Vol. 181, pp. 104639-104639
Open Access | Times Cited: 10
Output feedback near-optimal control of atomic force microscope
Joshua L. Sutton, Mohammad Al Saaideh, Mohammad Al Janaideh, et al.
International Journal of Control (2025), pp. 1-20
Closed Access
Nonlinear Harmonics: A Gateway to Enhanced Image Contrast and Material Discrimination
Pardis Biglarbeigi, Gourav Bhattacharya, Dewar Finlay, et al.
Advanced Science (2025)
Open Access
Exposing hidden periodic orbits in scanning force microscopy
Lukas Böttcher, Hannes Wallner, Niklas Kruse, et al.
Communications Physics (2025) Vol. 8, Iss. 1
Open Access
Machine Learning Algorithms, Tools, and Databases for Applications in Materials Science
Nilima R. Das, Swayam Aryam Behera, P. Kali Krishna, et al.
Challenges and advances in computational chemistry and physics (2025), pp. 249-272
Closed Access
Analysis of biofilm assembly by large area automated AFM
Rubén Millán‐Solsona, Spenser R. Brown, L. Zhang, et al.
npj Biofilms and Microbiomes (2025) Vol. 11, Iss. 1
Open Access
Sensing red blood cell nano-mechanics: Toward a novel blood biomarker for Alzheimer’s disease
Matteo Nardini, Gabriele Ciasca, Alessandra Lauria, et al.
Frontiers in Aging Neuroscience (2022) Vol. 14
Open Access | Times Cited: 16
Characteristics and Functionality of Cantilevers and Scanners in Atomic Force Microscopy
Andrius Dzedzickis, Justė Rožėnė, Vytautas Bučinskas, et al.
Materials (2023) Vol. 16, Iss. 19, pp. 6379-6379
Open Access | Times Cited: 9
Insights and guidelines to interpret forces and deformations at the nanoscale by using a tapping mode AFM simulator: dForce 2.0
Victor G. Gisbert, Ricardo Garcı́a
Soft Matter (2023) Vol. 19, Iss. 31, pp. 5857-5868
Open Access | Times Cited: 7
Crystallization Control of Anionic Thiacalixarenes on Silicon Surface Coated with Cationic Poly(ethyleneimine)
Anna A. Botnar, O Novikov, Oleg A. Korepanov, et al.
Langmuir (2024) Vol. 40, Iss. 46, pp. 24634-24643
Closed Access | Times Cited: 2
Machine learning to probe modal interaction in dynamic atomic force microscopy
Pierpaolo Belardinelli, Abhilash Chandrashekar, Richard Wiebe, et al.
Mechanical Systems and Signal Processing (2022) Vol. 179, pp. 109312-109312
Open Access | Times Cited: 10
Topological Data Analysis of Nanoscale Roughness of Layer-by-Layer Polyelectrolyte Samples Using Machine Learning
Aleksandr S. Aglikov, Timur A. Aliev, Mikhail V. Zhukov, et al.
ACS Applied Electronic Materials (2023) Vol. 5, Iss. 12, pp. 6955-6963
Closed Access | Times Cited: 5
Quantitative Dynamic AFM Hydration‐Adsorption Design for Hygroscopic and Bio‐Compatible Polymeric Nanofibers
Willy Menacho, Karina N. Catalan, Tomás P. Corrales, et al.
Small Structures (2024) Vol. 5, Iss. 4
Open Access | Times Cited: 1
Non-Smooth Dynamics of Tapping Mode Atomic Force Microscopy
Pierpaolo Belardinelli, Abhilash Chandrashekar, Farbod Alijani, et al.
Journal of Computational and Nonlinear Dynamics (2023) Vol. 18, Iss. 8
Open Access | Times Cited: 3
Nonlinear numerical analysis and averaging method applied atomic force microscopy with viscoelastic term
Maurício A. Ribeiro, Г. А. Курина, Ângelo Marcelo Tusset, et al.
Archive of Applied Mechanics (2022) Vol. 92, Iss. 12, pp. 3817-3827
Closed Access | Times Cited: 5
Novel Approaches for Greener Synthesis of Extremozymes Using Agro/Food Waste
Freny Shah, Bablesh Ranawat, Vishwa Patel, et al.
(2024), pp. 297-318
Closed Access
Chaos prediction in trolling mode atomic force microscopy: analytical approach
Reza Mohaqeqi, Mohammadreza Sajjadi, Hossein Nejat Pishkenari, et al.
Microsystem Technologies (2022) Vol. 29, Iss. 1, pp. 127-140
Closed Access | Times Cited: 1
Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
Hanxun Jin, Enrui Zhang, Horacio D. Espinosa
arXiv (Cornell University) (2023)
Open Access
Page 1

Scroll to top