Evidence map›Paper›PMID 40519751›Full record

ReviewMRS bulletin2025

Revealing new depths of information with indentation mapping of microstructures.

Edoardo Rossi, Christophe Tromas, Zhiying Liu, Yu Zou, Jeffrey M Wheeler

Abstract readReview
In one paragraph

Review in MRS bulletin, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Edoardo RossiDepartment of Civil, Computer Science and Aeronautical Technologies Engineering, Università degli Studi Roma Tre, Rome, Italy.ORCID 0000-0001-8194-5329
Christophe TromasInstitut Pprime, Université de Poitiers/UPR 3346 CNRS/ISAE-ENSMA, Poitiers, France.
Zhiying LiuDepartment of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong.
Yu ZouDepartment of Materials Science and Engineering, University of Toronto, Toronto, ON Canada.
Jeffrey M WheelerLaboratory for Nanometallurgy, Department of Materials, ETH Zürich, Zurich, Switzerland.ORCID 0000-0001-7763-2610

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nanoindentation is crucial in materials science for assessing mechanical properties in submicrometer volumes, and high-speed nanoindentation mapping has evolved it from a localized measurement technique into a scanning-probe-like approach for microstructures, delivering large-area, high-resolution mechanical property maps with more than 200,000 indents in hours. Such mapping enables direct imaging of hardness and modulus variations, phase boundaries, and local deformation behaviors in materials where heterogeneity governs mechanical performance. By correlating these mechanical maps with composition, orientation, and phase data from complementary analytical techniques, deep multidimensional data sets reveal the complex interplay between structure, processing, and properties. Such data sets increasingly demand advanced statistical clustering, machine learning, and deep learning for classification, trend extraction, and phase identification. Moving forward, high-speed nanoindentation is anticipated to operate under Graphical abstract: Schematic representation of high-speed nanoindentation mapping revealing microstructural heterogeneities in mechanical response. The indenter tip rapidly probes the surface, generating property maps sensitive to features such as twinning, recrystallization, segregation, precipitates, and sintered phases. These mechanical maps can be directly correlated with crystallographic and phase information from Electron Backscatter Diffraction (EBSD) and elemental composition from Energy-Dispersive X-ray Spectroscopy (EDS). Measurements can be performed operando, i.e., under real-time and service-relevant environmental conditions (e.g., temperature, atmosphere), enabling direct analysis of structure-property-performance relationships at the microstructural scale.

Indexed as

HardnessMachine learningMicrostructureNanoindentationScanning electron microscopy (SEM)Statistics/statistical methods

Identifiers

PMID40519751
PMCPMC12162787

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.