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Record W4412187269 · doi:10.1002/smll.202505372

An Oxidation‐Resistant High Entropy Alloy for Aqueous Aluminum‐Battery Chemistries

2025· article· en· W4412187269 on OpenAlexaff
Apurva Anjan, Adwitiya Rao, Rohit M. Manoj, Varad Mahajani, Kevin Bhimani, Jonathan D. Poplawsky, Nikhil Koratkar

Bibliographic record

VenueSmall · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsUniversity of Toronto
FundersOak Ridge National LaboratoryRensselaer Polytechnic InstituteU.S. Department of EnergyOffice of ScienceNational Science Foundation
KeywordsOverpotentialAlloyMaterials scienceAqueous solutionElectrolyteElectron transferChemical engineeringAluminiumDissolutionElectrochemistryChemical physicsChemistryMetallurgyPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract Today Lithium (Li)‐ion batteries are ubiquitous from portable electronics to electric vehicles and grid energy storage. However, Li‐ion technology may not be sustainable in the long run; Li is scarce and comprises <0.0065% of the earth's crust. Aluminum (Al) on the other hand, is the most earth‐abundant metal and offers an outstanding theoretical capacity due to three electron transfers per Al atom. However, traditional batteries that utilize Al‐metal face a major obstacle: the formation of a passivating Al₂O₃ layer that blocks Al 3 ⁺ movement. Here, an Al‐based high entropy alloy (Al‐HEA) is reported that enables efficient Al 3 ⁺ transport while also stabilizing the Al‐metal/aqueous‐electrolyte interface. First‐principles calculations reveal that the solid‐solution structure of the Al‐HEA leads Al atoms to transfer electrons to neighboring elements, which thermodynamically suppresses oxidation. Additionally, the Al‐HEA's oxidation process is kinetically sluggish compared to pure Al, keeping the alloy/electrolyte interface open for Al 3+ transport with minimal overpotential. Taking advantage of this, a high‐performing aqueous Al–Selenium (Al–Se) battery is demonstrated that leverages this unique chemistry.

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 score0.195
Threshold uncertainty score0.841

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 head0.007
GPT teacher head0.215
Teacher spread0.207 · 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

Citations10
Published2025
Admission routes1
Has abstractyes

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