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Record W2288524902 · doi:10.1149/2.0061603jes

The Electrochemical Behavior of Polyimide Binders in Li and Na Cells

2015· article· en· W2288524902 on OpenAlexaff
B. N. Wilkes, Zachary Lee Brown, L. J. Krause, Matthew Triemert, M. N. Obrovac

Bibliographic record

VenueJournal of The Electrochemical Society · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPolyimideCarbonizationElectrochemistryMaterials scienceLithium (medication)Battery (electricity)Carbon fibersChemical engineeringComposite numberPolymerElectrodePolymer chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

The electrochemistry of aromatic and aliphatic polyimide binders was characterized in composite coatings for Li-ion and Na-ion battery negative electrodes. Aromatic polyimide was found to have a large first lithiation capacity of 1943 mAh/g and a reversible capacity of 874 mAh/g in lithium cells. The large first lithiation capacity is suggestive of its full reduction to carbon. Subsequent cycles of aromatic-PI are also similar to that of hydrogen containing carbons. Aromatic-PI is also active in Na cells, but with less capacity and less hysteresis during cycling, which is also consistent with the behavior of hydrogen containing carbon in Na cells. Therefore, we suspect that after the first lithiation or sodiation, all of the aromatic-PI becomes carbonized. These conductive reaction products lead to excellent cycling in alloy cells. In contrast, aliphatic-PI is inert and leads to poor cycling when used in alloy cells. These results may have large implications for the use of conductive polymer binders, which may just be carbonizing during their first lithiation.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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

Citations48
Published2015
Admission routes1
Has abstractyes

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