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Record W4286715779 · doi:10.1002/smll.202203045

Ionic Conductive and Highly‐Stable Interface for Alkali Metal Anodes

2022· article· en· W4286715779 on OpenAlexafffund
Enzhong Jin, Karnpiwat Tantratian, Changtai Zhao, Anastasia M Codirenzi, Lyudmila V. Goncharova, Changhong Wang, Feipeng Yang, Yijia Wang, Parham Pirayesh, Jinghua Guo, Lei Chen, Xueliang Sun, Yang Zhao

Bibliographic record

VenueSmall · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaAdvanced Light SourceOffice of ScienceWestern UniversityU.S. Department of Energy
KeywordsAlkali metalElectrical conductorMaterials scienceAnodeIonic bondingInterface (matter)MetalInorganic chemistryChemical engineeringNanotechnologyIonChemistryMetallurgyComposite materialElectrodePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Alkali metals are regarded as the most promising candidates for advanced anode for the next-generation batteries due to their high specific capacity, low electrochemical potential, and lightweight. However, critical problems of the alkali metal anodes, especially dendrite formation and interface stabilization, remain challenging to overcome. The solid electrolyte interphase (SEI) is a key factor affecting Li and Na deposition behavior and electrochemical performances. Herein, a facile and universal approach is successfully developed to fabricate ionic conductive interfaces for Li and Na metal anodes by modified atomic layer deposition (ALD). In this process, the Li metal (or Na metal) plays the role of Li (or Na) source without any additional Li (or Na) precursor during ALD. Moreover, the key questions about the influence of ALD deposition temperature on the compositions and structure of the coatings are addressed. The optimized ionic conductive coatings have significantly improved the electrochemical performances. In addition, the electrochemical phase-field model is performed to prove that the ionic conductive coating is very effective in promoting uniform electrodeposition. This approach is universal and can be potentially applied to other different metal anodes. At the same time, it can be extended to other types of coatings or other deposition techniques.

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 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 score0.198
Threshold uncertainty score0.389

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 science0.0000.000
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 head0.026
GPT teacher head0.249
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.

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

Citations29
Published2022
Admission routes2
Has abstractyes

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