MétaCan
Menu
Back to cohort
Record W2044718532 · doi:10.1149/06424.0011ecst

Physicochemical and Electrochemical Properties of the Organic Solvent Electrolyte with Lithium Bis(fluorosulfonyl)Imide (LiFSI) As Lithium-Ion Conducting Salt for Lithium-Ion Batteries

2015· article· en· W2044718532 on OpenAlexaff
Toshihiro Takekawa, Kazuhiro Kamiguchi, Hideto Imai, Masaharu Hatano

Bibliographic record

VenueECS Transactions · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsFord Motor Company (Canada)
Fundersnot available
KeywordsElectrolyteElectrochemistryLithium (medication)IonInorganic chemistryIonic bondingDiffusionSolventConductivitySalt (chemistry)ChemistryIonic conductivityImideDissociation (chemistry)Physical chemistryElectrodeThermodynamicsPolymer chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, we investigated the physicochemical and electrochemical properties of LiFSI solution comparing with those of LiPF 6 in EC/DEC (3/7, v/v), and discuss the difference in ionic conductivity between these electrolyte solutions based on self-diffusion coefficients measured by PFG-NMR. Self-diffusion coefficients of solvent molecules and ions in 1M LiFSI solution are about 1.5 times larger than those of 1M LiPF 6 solution. On the other hand, the degree of dissociation of LiFSI estimated by the Nernst-Einstein Equation from measured ionic conductivity and self-diffusion coefficients of Li + and FSI - is lower than that of LiPF 6 . Therefore it is strongly suggested that almost the same ionic conductivity of 1M LiFSI solution as that of 1M LiPF 6 in EC/DEC (3/7, v/v) is the result of cancelation of these two factors in the opposite direction.

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 score0.002

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 communication0.0000.001
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 head0.021
GPT teacher head0.216
Teacher spread0.196 · 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".

Quick stats

Citations16
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueECS TransactionsSame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207