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Record W4220682715 · doi:10.1021/acsaem.2c00252

Paraffin Based Cathode–Electrolyte Interface for Highly Reversible Aqueous Zinc-Ion Battery

2022· article· en· W4220682715 on OpenAlexaff
Yu Liu, Jian Zhi, Tuan K.A. Hoang, Min Zhou, Mei Han, Yan Wu, Qiuyu Shi, Rong Xing, Pu Chen

Bibliographic record

VenueACS Applied Energy Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsHydro-QuébecUniversity of Waterloo
FundersJiangsu Provincial Department of EducationJiangsu Agricultural Science and Technology Innovation Fund
KeywordsCathodeElectrolyteAqueous solutionBattery (electricity)Chemical engineeringDissolutionIntercalation (chemistry)Materials scienceElectrodeGraphiteZincChemistryInorganic chemistryComposite materialMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

In aqueous rechargeable zinc-ion batteries (ARZIBs), aqueous electrolytes tend to initiate structure changes of metal oxides and conductive agents of the electrode, which leads to rapid capacity degradation. In this work, we report an artificial cathode–electrolyte interface (CEI) composed of paraffin that provides a trade-off between Zn2+ intercalation kinetics and stability of the cathode materials. Such paraffin-based CEI can either suppress Mn2+ dissolution and hence stabilize MnO2, or prevent water contact with conductive graphite to maintain its morphology and carbonaceous structure. As a result, the assembled aqueous Zn//MnO2 and Zn//ZnMn2O4 full battery with paraffin-based CEI delivered a superior capacity retention of 82% and 81% after 1000 cycles, 67% and 48% higher than the battery without CEI, respectively. More importantly, both Zn//MnO2 and Zn//ZnMn2O4 full battery also exhibit exceptional cycling stability even at a very high cathode mass loading of 23.6 and 25.2 mg cm–2, respectively, which offers an ideal capacity retention of 73% and 78% after 5000 cycles. Such a unique CEI design on the cathode surface provides a general strategy to improve the cycle life of ARZIBs.

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 categoriesMeta-epidemiology (narrow)
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 score0.189
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.012
GPT teacher head0.236
Teacher spread0.223 · 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.

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

Citations22
Published2022
Admission routes1
Has abstractyes

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