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Record W2088266989 · doi:10.1149/2.047302jes

Accurate and Precise Temperature-Controlled Boxes for the Safe Testing of Advanced Automotive Li-Ion Cells with High Precision Coulometry

2012· article· en· W2088266989 on OpenAlexaff
J. R. Dahn, S. Trussler, S. Dugas, D. J. Coyle, J. J. Dahn, J. C. Burns

Bibliographic record

VenueJournal of The Electrochemical Society · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAutomotive industryCoulometryBattery (electricity)Computer scienceLithium (medication)ElectrolyteAutomotive engineeringMaterials scienceElectronicsNuclear engineeringIonProcess engineeringNanotechnologyElectrodeElectrical engineeringChemistryEngineeringPhysicsAerospace engineeringElectrochemistry

Abstract

fetched live from OpenAlex

Lithium-ion cells for electronics and automotive applications have an excellent safety record. However, safety-related events can sometimes occur during routine testing of prototype designs, especially in the case of designs using new electrode materials, new separators and/or new electrolytes. High precision measurements of coulombic efficiency have recently been shown to have great value in predictions of the impact of electrolyte additives on cell lifetime. In order to apply those methods to prototype automotive cells, special compact temperature-controlled boxes were required that could maintain the temperature to ± 0.05°C precision and ensure the safety of the laboratory. These versatile temperature-controlled boxes were designed to accommodate cells as small as coin cells and as large as 40 Ah automotive pouch cells. The ability of the design to prevent cell-to-cell fire propagation and to channel smoke and flame away from the rest of the laboratory was experimentally verified. It is hoped that the information presented here will be of value to those designing precision testing facilities for large Li-ion cells.

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 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)0.001
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 score0.006
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.009
GPT teacher head0.249
Teacher spread0.240 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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