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Record W3024525448 · doi:10.1149/ma2020-012259mtgabs

Indigo As Single Additive in Liquid Electrolyte for Lithium Metal Batteries

2020· article· en· W3024525448 on OpenAlexaff
Charlotte Mallet, Sylviane Rochon, Karim Zaghib

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsElectrolyteAnodeElectrochemistryIonic liquidLithium (medication)CathodeMaterials sciencePropylene carbonateInorganic chemistryChemical engineeringElectrodeChemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Rechargeable lithium ion batteries (LIBs) have been successfully developed and widely used to power today’s portable electronic devices. The long term success in electric vehicles and energy storage system relies on rising the energy density, low temperature efficiency, safety and increased cycle life. In parallel, lithium metal batteries (LMBs), described as a system with Li0 as anode and metal oxide as cathode (NMC, LFP, LMO) are arising as aim of research for a plethora of groups. Lithium metal anode is considered as ideal anode due to high theoretical capacity (3860 mAhg-1), lower negative electrochemical potential and lower density (0.534 g.cm-3).1 Researchers used complex liquid electrolyte (ionic liquid, etc.) systems in order to avoid lithium dendrite and degradation, conventional liquid electrolyte (LiPF6, carbonate solvants) degraded faster than expected, and consequently, generated cells fading. Therefore conventional liquid electrolyte in such cell was not used. Use organic additive in electrolyte and/or electrodes is considered as one of the most economical and effective’s approaches for solved many problems as cited previously. The additive can interact with electrolyte or anode to prevent degradation or enhanced cell performances. This presentation will outline commercial organic compounds: Indigo and his derivatives use as single additive in NMC/Li batteries with conventional liquid electrolyte, which considerably increased the cycle life of Li metal batteries. Dyes such Indigoïd contain electron density donor (-NH-) and acceptor (-C=O) groups linked by conjugated bonds, which participate to their versatile electrochemical properties. Used as additive in cathode materials or electrolyte, indigo modified the electrode structure and changed the composition of the Li-ions battery electrolyte and solid electrolyte interface (SEI) on lithium metal. 1.Zhang, H., Eshetu, G.G.,Judez, X., Li, C., Armand, M., Angew. Chem. Int. Ed. 2018, 57, 15002-15027 Figure 1

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 score0.005

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)0.0010.001

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 head0.015
GPT teacher head0.221
Teacher spread0.206 · 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
Published2020
Admission routes1
Has abstractyes

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