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Record W4256647618 · doi:10.1149/ma2019-02/5/338

Tracking Battery Swelling in Uncompressed Li-Ion Cells Via in-Operando X-Ray Radiography and Micro-Tomography

2019· article· en· W4256647618 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrodeSwellingMaterials scienceBattery (electricity)IonAnalytical Chemistry (journal)Composite materialChemistryPhysics

Abstract

fetched live from OpenAlex

Li-ion battery swelling during cycling is known to be a major cause of battery electrode degradation and can contribute significantly to cell failure [1,2]. The main cause of battery swelling is due to the intercalation of Li into the carbon and LiCoO 2 electrodes. This swelling is known to occur anisotropically due to the electric field and Li salt distributions in the electrode [3]. We have used x-ray radiography and µCT to map this swelling, in-operando under charge and discharge conditions, in the layers of an uncompressed Li-ion battery. This study gives insight into the relationship between the battery state of charge and the electrode swelling. The change in swelling due to the electrode geometry is also investigated. Li-ion pouch cells were cycled at 0.5, 1 and 2 C while undergoing x-ray radiography and tomography at the BMIT beamline of the Canadian Light Source. Synchrotron radiography allowed the tracking of the electrode swelling in two dimensions with high temporal (10 s/frame) and spatial resolution (3 µm/pixel). µCT measurements of Li-ion cells were carried out under the same battery cycling conditions and scans were taken at 0, 25, 75 and 100 % state of charge (3 µm/pixel, 225 s/scan). Reconstruction and segmentation of the collected images allowed for the measurement of each electrode volume. From these results it appears that electrode swelling depends on a number of factors, including electrode geometry, charge/discharge rate and battery age. There is also a noticeable degree of irreversibility in the swelling of the electrode, which may be a possible metric for battery degradation. The quantification of the carbon electrode swelling corresponds closely with the theoretical value for lithium intercalation to LiC 6 , although there is a discrepancy between the swelling value obtained for the LiCoO 2 electrode and theory. These results give a valuable insight into the mechanical dynamics of Li-ion batteries under in-operando conditions and will provide validation to, and data for, battery models including stress-strain and degradation models. References Bond, T., Zhou, J., & Cutler, J. (2017). Electrode Stack Geometry Changes during Gas Evolution in Pouch-Cell-Type Lithium Ion Batteries. Journal of The Electrochemical Society, 164(1), A6158–A6162. Chen, C., Wei, Y., Zhao, Z., Zou, Y., & Luo, D. (2019). Investigation of the swelling failure of lithium-ion battery packs at low temperatures using 2D/3D X-ray computed tomography. Electrochimica Acta, 305, 65–71. Zhang, N., & Tang, H. (2012). Dissecting anode swelling in commercial lithium-ion batteries. Journal of Power Sources, 218, 52–55. Figure 1 (a) 2D x-ray radiography of a Li-ion pouch cell and (b) 3D reconstruction of a µCT of a Li-ion pouch cell section. Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.217
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it