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Record W4399385970 · doi:10.1002/cnl2.144

Interfacial regulation engineering in anode‐free rechargeable batteries

2024· article· en· W4399385970 on OpenAlexaff
Zhendong Hao, Yan Liang, Wenjie Li, Yuhan Zeng, Yuming Dai, Cong Yuan, Jia Ju, Baosen Zhang

Bibliographic record

VenueCarbon Neutralization · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsKensington Health
FundersSix Talent Peaks Project in Jiangsu ProvinceNatural Science Foundation of Jiangsu Province
KeywordsAnodeFaraday efficiencyCoatingEnergy storageCathodeNanotechnologyElectrolyteMaterials scienceElectrochemistryEnergy densityProcess engineeringComputer scienceEngineering physicsEngineeringElectrical engineeringChemistryPower (physics)ElectrodePhysics

Abstract

fetched live from OpenAlex

Abstract Anode‐free rechargeable batteries (AFRBs), equipped with bare collectors at the anode, are potential electrochemical energy storage technology attributed to their simplified cell configuration, high energy density, and cost reduction. Nevertheless, issues including insufficient Coulombic efficiency as well as the formation of the dendrites restrict their practical implementation. In recent years, various strategies have been proposed to overcome the critical issues of AFRBs. Among which, interfacial properties play key roles for achieving high stable AFRBs. In this review, an overview of AFRBs is discussed in the first part. Then, the main strategies based on interfacial regulation engineering toward high‐performance AFRBs are summarized including designing of current collectors, introducing of surface coating layers, modification of electrolytes, separators engineering, cathode materials regulation, and so forth. In addition, some future perspectives for developing AFRBs are proposed. This review will create new avenues on constructing stable AFRBs for advanced energy storage devices.

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 score0.002

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
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 head0.224
Teacher spread0.212 · 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

Citations22
Published2024
Admission routes1
Has abstractyes

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