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Record W2041569087 · doi:10.1149/2.052209jes

Studies of the Effect of Triphenyl Phosphate on the Negative Electrode of Li-Ion Cells

2012· article· en· W2041569087 on OpenAlexafffund
Xin Xia, Ping Ping, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsGraphiteElectrolyteLithium (medication)ElectrochemistryCokeChemistryPetroleum cokeElectrodeCapacity lossInorganic chemistryPhosphateChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The effects of the flame retardant, triphenyl phosphate (TPP), on the electrochemical properties of negative electrode materials for Li-ion batteries were studied. Petroleum coke and graphite were chosen for study. Coke/Li half cells with different concentrations of TPP were studied with a home-made automated storage system. The reactivity of intercalated lithium with TPP-containing electrolyte could be inferred from the potential-time graphs of the coke/Li cells during storage. Graphite/graphite symmetric cells with different concentrations of TPP were constructed and tested. Capacity loss in symmetric graphite/graphite cells is caused by reactions of intercalated lithium with electrolyte. The storage test results showed that coke/Li cells with TPP (10%–40% by volume) have similar rates of Li loss to electrolyte reactions at 30°C compared to control cells and show significantly lower rates at 60°C. Graphite/graphite symmetric cells with TPP (10%–40%) have capacities that decrease with TPP content, but show similar rates of% capacity loss per cycle. The impedance of the symmetric cells increases with TPP content. All the results together suggest that TPP forms a stable SEI but a SEI that impedes the transport of Li.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.267
Teacher spread0.256 · 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

Citations26
Published2012
Admission routes2
Has abstractyes

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