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Record W3117365866 · doi:10.1149/ma2020-024666mtgabs

Using Scanning Micro X-Ray Fluorescence (µXRF) to Visualize, Understand and Quantify Transition Metal Dissolution in Li-Ion Cells

2020· article· en· W3117365866 on OpenAlexaff
Ahmed Eldesoky, E. R. Logan, Michel B. Johnson, Chris McFarlane, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of New BrunswickDalhousie University
Fundersnot available
KeywordsAnodeMaterials scienceCathodeDissolutionGraphiteAnalytical Chemistry (journal)OxideX-ray fluorescenceTransition metalLithium cobalt oxideFluorescenceChemical engineeringLithium-ion batteryElectrodeChemistryMetallurgyBattery (electricity)Optics

Abstract

fetched live from OpenAlex

State-of-the-art Li-ion batteries (LIBs) typically consist of a graphite anode with a capacity of 372 mAh g -1 and a cathode material consisting of a layered transition metal oxide in the form of LiMO 2 , where M = Ni, Mn, Co or Al (NMC and NCA material), or olivine-type material in the form of LMPO 4 , where M = Fe, such as LiFePO 4 (LFP). Proposed degradation mechanisms for the cathode material in LIBs include transition metal dissolution (TMD), particle cracking and phase transformation, which are believed to contribute to significant capacity fade due to the loss of cathode active material 1 . Here, we report the use of scanning Micro X-Ray Fluorescence (µXRF) to investigate the effects of cell drying conditions and VC additive on the extent of TMD in LFP type cells and its subsequent deposition on the anode surface. For quantitative analysis, we prepared a calibration wedge made up of a pristine graphite anode with a known linear gradient of sputtered Fe. Figure 1a shows the µXRF images of the calibration sample and blank, from which we were able to correlate known TM concentrations to an observed signal intensity in the form of net count per area to facilitate accurate quantification, schematically shown in Fig. 1b . Unlike previous reports of XRF use in TMD analysis 2 , our approach allows us to quantify TM concentration deposited on our anodes without having to alter the surface by DMC washing, or through ball-milling, which preserves the nature of the sample and allows us to extract both quantitative and qualitative information (such as element distribution) from our aged cells using matrix-matched calibrants. In this work, we demonstrate how our scanning µXRF approach enabled us to visualize the distribution of TMs and other elements present on the anode surface and quantify TMD in LFP Li-ion cells. We show that Fe dissolution can be greatly reduced with rigorous cell drying and appropriate choice of additives. Additionally, we report the presence of locally high TM concentration spots on the anode which we believe are due to non-uniformities in cell stack pressure. Finally, we observe that the extent of TMD is normally not significant enough to be responsible for cell capacity fade due to active material loss, and propose how Fe dissolution might be contributing to a more complicated cell failure mechanism which will be important to investigate in future work to understand its impact on cycle life. References: Börner, M. et al. Degradation effects on the surface of commercial LiNi0.5Co0.2Mn0.3O2 electrodes. J. Power Sources 335 , 45–55 (2016). Evertz, M., Lürenbaum, C., Vortmann, B., Winter, M. & Nowak, S. Development of a method for direct elemental analysis of lithium ion battery degradation products by means of total reflection X-ray fluorescence. Spectrochim. Acta - Part B At. Spectrosc. 112 , 34–39 (2015). Figure 1

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How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.373
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.288
Teacher spread0.243 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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