Evidence map›Paper›PMID 40545856›Full record

ArticleSmall methods2025

Demonstrating Faster Multi-Label Grey-Level Analysis for Crack Detection in Ex Situ and Operando Micro-CT Images of NMC Electrode.

Matthew P Jones, Huw C W Parks, Alice V Llewellyn, Hamish T Reid, Chun Tan, Aaron Wade, Thomas M M Heenan, Francesco Iacoviello, Shashidhara Marathe, Paul R Shearing and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Small methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

Matthew P JonesElectrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.ORCID https://orcid.org/0000-0001-6628-1464
Huw C W ParksElectrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.ORCID https://orcid.org/0000-0002-5629-1404
Alice V LlewellynElectrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.
Hamish T ReidElectrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.
Chun TanElectrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.
Aaron WadeElectrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.
Thomas M M HeenanElectrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.
Francesco IacovielloElectrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.
Shashidhara MaratheDiamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxon, OX11 0DE, UK.
Paul R ShearingElectrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.
Rhodri JervisElectrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.ORCID https://orcid.org/0000-0003-2784-7802

Funding

Chair in Emerging Technologies CiET1718/59Engineering and Physical Sciences Research Council EP/M014045/1Faraday Institution EP/S003053/1Faraday Institution FIRG060Faraday Institution FIRG066Innovate UK 10044823UKRI 2570149
6 · The paper itself

Abstract

During battery operation, cracking occurs in Nickel Manganese Cobalt (NMC) oxide secondary particles. Cracked particles appear darker in micro-computed tomography (micro-CT) images due to the partial volume effect, where voxels containing both void and solid yield intermediate grey-levels. This work presents an automated method for tracking grey-level changes caused by this effect in large, statistically meaningful micro-CT datasets containing over 10 000 individual particles. It extends earlier work using the GREAT algorithm to analyze NMC particles in tomography images. The new GREAT2 algorithm increases processing speed, from around 1,400 particles per day with GREAT to over 10 000 particles in under a minute. Furthermore, this work introduces methods for automated tracking of grey-level intensity changes in individual particles through different states of charge in an operando experiment. This capability enables temporal analysis of particle degradation mechanisms. Additional data processing methods are presented that extract useful insights. Through this work we show that the large sample sizes, enabled by this method and GREAT2, allow for statistically robust analysis of particle populations. These advances significantly accelerate the tomographic study of cracking in battery electrodes. The GREAT2 algorithm and associated workflows have been made available as the GRAPES Python toolkit.

Indexed as

batteriesdegradationimage analysismicro‐CTNMC

Identifiers

PMID40545856
PMCPMC12391646

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Registered trials

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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.