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Record W3078616015 · doi:10.1002/smll.202003438

Engineering Solvation Complex–Membrane Interaction to Suppress Cation Crossover in 3 V Cu‐Al Battery

2020· article· en· W3078616015 on OpenAlexafffund
Huimin Wang, Yang Sun, Matthew Li, Gaoran Li, Kaiming Xue, Zhongwei Chen, Denis Y. W. Yu

Bibliographic record

VenueSmall · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooCity University of Hong Kong
KeywordsSolvationBattery (electricity)CrossoverMaterials scienceMembraneChemical physicsIonChemistryNanotechnologyComputational chemistryThermodynamicsOrganic chemistryComputer sciencePhysics

Abstract

fetched live from OpenAlex

Metal-metal batteries such as the 3 V Cu-Al system are highly desirable for large-scale energy storage owing to their low cost and excellent scalability of Cu and Al foils. However, the dissolved Cu cations will crossover from the cathode to the anode leading to poor electrochemical performance. In this work, it is demonstrated that the reversibility of the Cu-Al battery depends strongly on the interaction of the Cu ions with the electrolyte solvent and subsequently the affinity of the solvated Cu ion with the membrane separator. Specifically, a series of common carbonate-based electrolyte solvents are investigated via molecular dynamics and contact angle measurements to understand the interaction between the solvents and a polypropylene (PP) membrane, as well as that between cations and solvent. Among different solvents, fluoroethylene carbonate (FEC) is shown to drastically enhance the coulombic efficiency to 97%, compared to that of 27% with dimethyl carbonate. Remarkable cyclability of a 3 V Cu-Al battery with 3 m LiTFSI FEC and PP membrane up to 1000 cycles is further demonstrated. This finding opens new opportunities for the development of low-cost, high performance Cu-Al systems for stationary applications.

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 score0.087
Threshold uncertainty score0.647

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 head0.039
GPT teacher head0.258
Teacher spread0.219 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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