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Record W3185034529 · doi:10.1149/ma2021-01150mtgabs

Electron Transfer and Transport Properties of Redox Compounds in Highly Concentrated Electrolytes

2021· article· en· W3185034529 on OpenAlexaff
Simon Généreux, Valérie Gariépy, Dominic Rochefort

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAcetonitrileElectrolyteSolvationElectrochemistrySolventChemistryLithium (medication)Electron transferMoleculeSalt (chemistry)IonInorganic chemistryChemical physicsPhysical chemistryOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Highly concentrated electrolytes (HCE) are currently gaining the interest of the scientific community for battery applications. These electrolytes are obtained when the number of ions of a salt nears or equals that of the solvent molecules, while maintaining a liquid state. In such systems, the solvent molecules are all found as complexes with the cations, usually Li+. It is currently accepted that in HCE, no “free” molecules of solvent are found in the solution since they are entirely located in the sphere of solvation of the ions present in large quantities. This unique structure brings a greater electrochemical stability of the solvent and a higher Li+ transport number. These properties have stimulated research to apply HCE in several electrochemical systems. For instance, HCEs employing acetonitrile as the solvent have been applied in lithium metal batteries while acetonitrile with moderate salt concentration spontaneously decomposes on lithium. Despite the attractiveness of these electrolytes, the origin of the properties and the relationships between the structure and those properties are still poorly understood, and there is currently no study on heterogeneous electron transfer in such electrolyte. We therefore aim at determining if the HCE structure has an impact on electron transfert. The approach developed in our group is the study of HCE based on LiTFSI and acetonitrile, a well-studied system quite representative of the highly concentrated systems. The approach that will be presented is divided in two parts. The first part concerns the study of the physicochemical properties of HCE. This strategy highlights the factors that have a significant impact on the properties of electrolytes. One of these factors is the water content. The results demonstrate that the presence of water in the solutions has a limited impact on the physical properties of viscosity and density of the mixtures as long as the concentration remains at or below 1000 ppm. The water content has also an impact on the electrochemical stability window (ESW). The ESW of HCE, decreases from 5.45V to less than 2.5 V at 1000 ppm of water. The second part concerns the study of the electron transfer rate of redox couples in HCE. We investigated the solvating structure of highly concentrated electrolytes via the electrochemistry of redox molecules. The first redox couple is the Ferrocenium/Ferrocene (Fc+/Fc) to probe the impact of the electrolyte properties on electron transfer reactions and to validate the approaches used. In the highly concentrated system, the Fc+/Fc redox couple follows the same relation (Stokes-Einstein and Nicholson-Shain) as the ideally dilute system. Two others redox couple have been studied, the Ru(bpy)3 3+/2+ and Fe3+/2+, to investigate the effect of different charges and electron transfer mechanisms. The inner sphere electron transfer of the Fe3+/2 redox couple is more affected by the salt concentration than the outer sphere complex (Ru(bpy)3 3+/2+ and Fc). These results highlight the importance of structure and composition in the development of highly concentrated electrolytes.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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