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Record W4226494835 · doi:10.1149/1945-7111/ac6453

High Temperature Testing of NMC/Graphite Cells for Rapid Cell Performance Screening and Studies of Electrolyte Degradation

2022· article· en· W4226494835 on OpenAlexaff
Tina Taskovic, Ahmed Eldesoky, Wentao Song, Michael Bauer, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteGraphiteDegradation (telecommunications)ElectrodeDissolutionAnalytical Chemistry (journal)ChemistryMass spectrometryMaterials scienceChromatographyMetallurgyOrganic chemistryElectrical engineering

Abstract

fetched live from OpenAlex

LiNi0.5Mn0.3Co0.2O2/graphite cells with two different electrolytes underwent charge-discharge cycling at 70 °C. The 70 °C condition reduced the time it took for cells to lose significant capacity. Studies of the changes to the electrolyte after cycling by gas chromatography/mass spectrometry (GC/MS) and by Nuclear Magnetic Resonance spectroscopy (NMR) suggest that the same processes which cause cell failure and electrolyte degradation at 40 °C and 55 °C occur at 70 °C, only at an accelerated rate. Transition metal dissolution from the positive electrode was tracked using X-ray fluorescence studies of the negative electrode after testing. Based on the confidence obtained that the same degradation processes were occurring; advanced graphites were screened in NMC811/graphite cells at 70 °C. Differences in cell lifetime were apparent in weeks at 70 °C while the same differences took much longer to observe at 40 °C. It is our opinion that elevated temperature testing of Li-ion cells at 70 °C is a viable rapid screening technique for advanced electrolytes and advanced electrode materials.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.221
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations32
Published2022
Admission routes1
Has abstractyes

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