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Record W3133361724 · doi:10.1016/j.elecom.2021.106979

Determining the effect of dissolved CO2 on solution phase Li+ diffusion in common Li-ion battery electrolytes

2021· article· en· W3133361724 on OpenAlexafffund
Laurence Savignac, Jeremy I. G. Dawkins, Steen B. Schougaard, Janine Mauzeroll

Bibliographic record

VenueElectrochemistry Communications · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolytePulsed field gradientEthylene carbonateDiffusionChemistryBattery (electricity)Analytical Chemistry (journal)IonDimethyl carbonateMass spectrometryInorganic chemistryGaseous diffusionPhase (matter)CarbonateElectrodeThermodynamicsChromatographyPhysical chemistryOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

Electrolyte decomposition in Li-ion batteries (LIB) and its entailing gas evolution significantly impacts cell performance. CO2 is one of the most abundantly evolved gases from common LIB electrolytes and could potentially affect Li+ solution transport during LIB operation. To this end, a comparative analysis of the Li+ diffusion coefficient (DLi+) which governs mass transport is required. Herein a methodology is established to saturate common battery electrolytes with a soluble gas without introducing other contaminants, so as to determine DLi+ in saturated and unsaturated samples. As a proof of concept, the values of DLi+ are determined in 1 M LiPF6 and 1 M LiClO4 in ethylene carbonate (EC):dimethyl carbonate (DMC) with and without dissolved CO2, confirmed by gas chromatography–mass spectrometry (GC–MS). The results obtained by pulse field gradient nuclear magnetic resonance (PFG-NMR) diffusion measurements agree for both electrolytes; saturating the electrolyte with CO2 has no measurable effect on DLi+ and therefore does not hinder Li+ mass transport.

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)0.000
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 score0.007
Threshold uncertainty score0.729

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 science0.0010.000
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 head0.010
GPT teacher head0.284
Teacher spread0.273 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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