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Record W4206941148 · doi:10.1002/slct.202104430

Four Phosphonium‐based Ionic Liquids. Synthesis, Characterization and Electrochemical Performance as Electrolytes for Silicon Anodes

2022· article· en· W4206941148 on OpenAlexaff
Nédher Sánchez-Ramírez, Ivonne E. Monje, Vitor L. Martins, Daniel Bélanger, Pedro H. C. Camargo, Roberto M. Torresi

Bibliographic record

VenueChemistrySelect · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsUniversité du Québec à Montréal
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsPhosphoniumIonic liquidElectrolyteThermal stabilityElectrochemistryMaterials scienceAnodeImideSolventInorganic chemistryIonic conductivityAlkylEthylene carbonateChemical engineeringChemistryPolymer chemistryOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract Herein, we describe the synthesis, characterization and electrochemical performance of four phosphonium‐based ionic liquids (ILs) as electrolytes, Physicochemical properties such as viscosity, density, ionic conductivity, and thermal stability of ILs and conventional organic solvent ethylene carbonate (EC)/diethyl carbonate (DEC) were experimentally determined at different temperatures. All ILs showed thermal stability greater than 300 °C, surpassing the stability of the conventional organic solvent, whose flash points were 145 and 23 °C for EC and DEC, respectively. Nevertheless, at room temperature, all ILs are much more viscous than EC/DEC. The composite Si ‐[P 2224 ][FSI] (triethyl‐n‐butylphosphonium bis(fluoromethylsulfonyl)imide) and Si‐EC/DEC anodes exhibit initial specific capacities at 0.15 A/g of 2409 and 2631 mAh/g, respectively. This demonstrates that despite the inferior transport properties of ILs, short alkyl‐substituted phosphonium ILs like [P 2224 ][FSI] are potentially competitive for the new generation of electrolytes for LIBs. NMR, DSC, TGA, and galvanostatic discharged/charged were used as characterization techniques.

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 score0.086
Threshold uncertainty score0.954

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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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