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Record W3183426466 · doi:10.1149/ma2021-0111mtgabs

Rational Design of Interlayer Binding Towards Highly Reversible Anion Intercalation Cathode for Dual Ion Batteries

2021· article· en· W3183426466 on OpenAlexaff
Maiwen Zhang, Aiping Yu

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIntercalation (chemistry)CathodeExfoliation jointStackingMaterials scienceIonRational designGraphiteChemical engineeringEnergy storageGrapheneInorganic chemistryNanotechnologyChemistryComposite materialOrganic chemistryThermodynamicsPhysical chemistryEngineering

Abstract

fetched live from OpenAlex

Dual-ion batteries (DIBs) with high working voltages and energy densities have gained more and more attention these years. However, general graphitic intercalation compounds (GICs) still require substantial improvements in both cathode practical capacity and structural stability. In this work, rational edge carboxylic anhydride functionality is introduced between carbon layers. One of the advantages is to expand the interlayer stacking, significantly improving active site accessibility and enhancing anion intercalation kinetics. Moreover, it can also serve as strong binding forces within interlayers, which can effectively prevent interlayer exfoliation during repeated anion intercalation/deintercalation. Benefited from this rational design, the carboxylic anhydride functionalized graphitic cathode is able to improve discharge capacity by over 37% with no obvious voltage deduction. Meanwhile the capacity retention of over 80% after 1000 cycles is achieved. Overall, this work reveals an effective way to improve both energy density and longevity of the graphite cathodes for dual ion batteries.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.271
Teacher spread0.234 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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