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Record W3110272656 · doi:10.1149/1945-7111/abcf17

Concentration Dependent Solution Structure and Transport Mechanism in High Voltage LiTFSI–Adiponitrile Electrolytes

2020· article· en· W3110272656 on OpenAlexafffund
Christopher J. Franko, Chae-Ho Yim, Fabian Årén, Gustav Åvall, Pamela S. Whitfield, Patrik Johansson, Yaser Abu‐Lebdeh, Gillian R. Goward

Bibliographic record

VenueJournal of The Electrochemical Society · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsNational Research Council CanadaMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceChemistry

Abstract

fetched live from OpenAlex

The physiochemical properties of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) in adiponitrile (ADN) electrolytes were explored as a function of concentration. The phase diagram and ionic conductivity plots show a distinct relationship between the eutectic composition of the electrolyte and the concentration of maximum ionic conductivity in the 25 °C isotherm. We propose a structure-based explanation for the variation of electrolyte ionic conductivity with LiTFSI concentration, where the eutectic concentration is a transitionary region at which the structure changes from solvated contact ion pairs to extended units of [Li z (ADN) x TFSI y ] z−y aggregates. It is found through diffusion coefficient measurements using pulsed-field gradient (PFG) NMR that both <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>D</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>L</mml:mi> <mml:mi>i</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> <mml:mo stretchy="true">/</mml:mo> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>D</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>T</mml:mi> <mml:mi>F</mml:mi> <mml:mi>S</mml:mi> <mml:mi>I</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>D</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>L</mml:mi> <mml:mi>i</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> <mml:mo stretchy="true">/</mml:mo> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>D</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>A</mml:mi> <mml:mi>D</mml:mi> <mml:mi>N</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> increase with concentration until 2.9 M, where after Li + becomes the fastest diffusing species, suggesting that ion hopping becomes the dominant transport mechanism for Li + . Variable diffusion-time (Δ) PFG NMR is used to track this evolution of the ion transport mechanism. A differentiation in Li + transport between the micro and bulk levels that increases with concentration was observed. It is proposed that ion hopping within [Li z (ADN) x TFSI y ] z−y aggregates dominates the micro-scale, while the bulk-scale is governed by vehicular transport. Lastly, we demonstrate that LiTFSI in ADN is a suitable electrolyte system for use in Li-O 2 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)0.000
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 score0.043
Threshold uncertainty score0.290

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 head0.004
GPT teacher head0.176
Teacher spread0.172 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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