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Depth-sensing nanoindentation techniques for studying mechanical properties of coals

Lookup NU author(s): Emeritus Professor Steve Bull

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).


Abstract

Copyright © 2026. Published by Elsevier B.V. Although coal is often treated as a homogeneous material in macroscopic mechanical testing, it is intrinsically heterogeneous across multiple length scales, and this heterogeneity at the micro- and nanoscale presents significant challenges for measurement techniques. This study reviews the development of indentation techniques and analyzes the principles and application of microindentation and depth-sensing nanoindentation (DSNI) for evaluating the mechanical properties of coal. It emphasizes the integration of DSNI with microscopy and thin-film preparation to improve the accuracy of mechanical measurements in heterogeneous coal systems. An effective method for estimating the elastic modulus of thin films is proposed, with an evaluation of the applicability of the Galanov-Dub and Oliver-Pharr methods. The phenomenon of coal dust formation during indentation is also examined, suggesting that there should be more refined testing standards tailored to coal materials.


Publication metadata

Author(s): Gao Z, Borodich FM, Epshtein SA, Kossovich EL, Bull SJ, Galanov BA, Jin X

Publication type: Article

Publication status: Published

Journal: Results in Engineering

Year: 2026

Volume: 30

Print publication date: 01/06/2026

Online publication date: 23/03/2026

Acceptance date: 22/03/2026

Date deposited: 14/04/2026

ISSN (electronic): 2590-1230

Publisher: Elsevier BV

URL: https://doi.org/10.1016/j.rineng.2026.110231

DOI: 10.1016/j.rineng.2026.110231

Data Access Statement: Data will be made available on request.


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Funding

Funder referenceFunder name
Chongqing City Science and Technology Program (Grant Nos. CSTB2025NSCQ-GPX0784 and CSTB2025NSCQ-GPX0778)
National Natural Science Foundation of China (Grant Nos. 52575201 and HWG2022001)
Russian Science Foundation (grant #18-77-10052)

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