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Record W2132379017 · doi:10.1149/2.1131410jes

Evaluation of Electrolyte Salts and Solvents for Na-Ion Batteries in Symmetric Cells

2014· article· en· W2132379017 on OpenAlexaff
T. D. Hatchard, M. N. Obrovac

Bibliographic record

VenueJournal of The Electrochemical Society · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteFOIL methodFaraday efficiencyBattery (electricity)CyclingElectrodeChemistrySalt (chemistry)Materials scienceIonChemical engineeringInorganic chemistryAnalytical Chemistry (journal)Composite materialChromatographyOrganic chemistryThermodynamicsPhysical chemistry

Abstract

fetched live from OpenAlex

The cycle life of NaCrO 2 in NaPF 6 and NaTFSI in PC and EC/DEC (1/2 v/v) electrolytes were studied in half-cells and symmetric cells. Half-cells were found to be not useful for evaluating the cycling performance due to impedance growth at the Na foil electrode. Symmetric cells containing no Na foil were found to be more useful when used for cycling performance evaluation instead of half-cells. NaCrO 2 in 1M NaTFSI in PC and NaPF 6 in EC/DEC electrolytes performed well in symmetric cells, having coulombic efficiencies of 99.94% and greater. We believe that these are the highest coulombic efficiencies reported for Na-ion battery materials. In contrast, half-cell performance did not reflect the cycling performance of NaCrO 2 and had no correlation to the symmetric cell performance. This illustrates the importance of symmetric cells for the evaluation of Na-ion battery materials. In addition to these findings, it was found that 1M NaTFSI salt solutions caused significant corrosion of the Al current collector, which could be suppressed by additions of NaPF 6 .

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)0.001
metaresearch head score (Gemma)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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

Citations39
Published2014
Admission routes1
Has abstractyes

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