Evidence map›Paper›PMID 38081869›Full record

ArticleNature communications2023

Nonintrusive thermal-wave sensor for operando quantification of degradation in commercial batteries.

Yuqiang Zeng, Fengyu Shen, Buyi Zhang, Jaeheon Lee, Divya Chalise, Qiye Zheng, Yanbao Fu, Sumanjeet Kaur, Sean D Lubner, Vincent S Battaglia and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. A Si-MoSeAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  2. Review
  3. National science review · 2025
    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

13 authors.

Yuqiang ZengSchool of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, China.ORCID http://orcid.org/0000-0002-4826-1846
Fengyu ShenEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Buyi ZhangEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.ORCID http://orcid.org/0000-0002-7200-5244
Jaeheon LeeEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.ORCID http://orcid.org/0000-0002-9285-0728
Divya ChaliseEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.ORCID http://orcid.org/0000-0001-6197-355X
Qiye ZhengEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Yanbao FuEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Sumanjeet KaurEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.ORCID http://orcid.org/0000-0002-7525-5492
Sean D LubnerEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Vincent S BattagliaEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Bryan D McCloskeyEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.ORCID http://orcid.org/0000-0001-6599-2336
Michael C TuckerEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.ORCID http://orcid.org/0000-0002-8508-499X
Ravi S PrasherEnergy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. rsprasher@lbl.gov.ORCID http://orcid.org/0000-0002-3282-7147

Funding

DOE | Office of Energy Efficiency & Renewable Energy | Vehicle Technologies Office (VTO) DEAC0205CH11231DOE | Office of Science (SC) DEAC0205CH11231
6 · The paper itself

Abstract

Monitoring real-world battery degradation is crucial for the widespread application of batteries in different scenarios. However, acquiring quantitative degradation information in operating commercial cells is challenging due to the complex, embedded, and/or qualitative nature of most existing sensing techniques. This process is essentially limited by the type of signals used for detection. Here, we report the use of effective battery thermal conductivity (k

Identifiers

PMID38081869
PMCPMC10713567

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.