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Record W2993243797 · doi:10.1002/adfm.201906890

Electrode Engineering by Atomic Layer Deposition for Sodium‐Ion Batteries: From Traditional to Advanced Batteries

2019· article· en· W2993243797 on OpenAlexafffund
Yu Fan, Lei Du, Gaixia Zhang, Fengmei Su, Weichao Wang

Bibliographic record

VenueAdvanced Functional Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsAtomic layer depositionMaterials scienceElectrodeNanotechnologyInterphaseElectrolyteEnergy storageDeposition (geology)Engineering physicsLayer (electronics)Power (physics)ChemistryEngineering

Abstract

fetched live from OpenAlex

Abstract Sodium‐ion batteries (SIBs) have emerged as one of the most promising and competitive energy storage systems due to abundant sodium resources and its environmentally friendly features. However, further improvements in the engineering of the SIB electrode/electrolyte interphase—which directly determines the Na‐ion transfer behavior, material structure stability, and sodiation/desodiation property—are highly recommended to meet the continuously increasing requirements for secondary power sources. Reasonably speaking, to promote SIBs, the advanced and controllable interphase/electrode engineering approach exhibits promise by rationally designing the bulk electrode and generating a well‐defined interphase. Atomic layer deposition (ALD) technology, with atomic‐scale deposition, superior uniformity, excellent conformality, and a self‐limiting nature, is thus expected to address the current challenges facing SIBs in terms of low energy density, limited cycling life, and structural instability, and to promote innovations such as multifunctional electrodes and nanostructured materials for advanced SIBs. This review summarizes and discusses the most recent advancements in the interphase engineering of SIBs by ALD via modifying traditional electrodes and designing advanced electrodes (such as 3D, organic, and protected sodium metal electrodes). Furthermore, based on the recent critical progress and current scientific understanding, future perspectives for the engineering of next‐generation SIB electrodes by ALD can be provided.

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), Insufficient payload (model declined to judge)
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 score0.093
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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

Citations55
Published2019
Admission routes2
Has abstractyes

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