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Record W3159309313 · doi:10.1002/aenm.202100077

Solid Electrolyte Interphase Engineering for Aqueous Aluminum Metal Batteries: A Critical Evaluation

2021· article· en· W3159309313 on OpenAlexafffund
Tony Dong, Kok Long Ng, Yijia Wang, Oleksandr Voznyy, Gisele Azimi

Bibliographic record

VenueAdvanced Energy Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsElectrolyteMaterials scienceElectrochemistryInterphaseIonic liquidAqueous solutionCathodic protectionSalt (chemistry)Chemical engineeringOxideAluminiumInorganic chemistryMetalIonic bondingElectrodeIonCatalysisChemistryMetallurgyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Rechargeable aqueous aluminum metal batteries (AAMBs) have long been considered unachievable because of the spontaneously formed ionically passivating oxide film and hydrogen evolution reaction on Al. In response, two solid electrolyte interphase (SEI) construction methods, namely, 5 m (mol kg −1 ) Al(OTF) 3 ‐based water‐in‐salt electrolyte (Al‐WiSE) and chloroaluminate ionic liquid (IL) pretreatment have been recently reported and seemingly reversible AAMBs were achieved. However, the SEI forming ability of a relatively low concentration Al‐WiSE and the fundamental nature of the IL‐derived SEI remain unclear. Here, with thorough computational, electrochemical, and spectroscopic characterizations, it is revealed that contrary to previous reports, neither of the methods build a stable and effective SEI, and hydrogen evolution reaction remains as the cathodic reaction, without Al deposition. This is the underlying reason for the poor voltage and cyclabilities of current AAMBs. Using insights gained in this work, suggestions for future research is offered on reliable electrolytes and interphases to enable truly reversible AAMBs.

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 score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.282
Teacher spread0.272 · 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

Citations108
Published2021
Admission routes2
Has abstractyes

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