MétaCan
Menu
Back to cohort
Record W4283169822 · doi:10.3389/fenrg.2022.862551

Review: High-Entropy Materials for Lithium-Ion Battery Electrodes

2022· article· en· W4283169822 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Energy Research · 2022
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersOffice of Energy Research and DevelopmentNatural Resources Canada
KeywordsAnodeBattery (electricity)Materials scienceElectrolyteElectrodeCathodeNanotechnologyLithium-ion batteryElectrochemistryEnergy densityEnergy storageElectronicsEngineering physicsElectrical engineeringPower (physics)EngineeringChemistryPhysics

Abstract

fetched live from OpenAlex

The lithium-ion battery is a type of rechargeable power source with applications in portable electronics and electric vehicles. There is a thrust in the industry to increase the capacity of electrode materials and hence the energy density of the battery. The high-entropy (HE) concept is one strategy that may allow for the compositional variability needed to design new materials for next-generation batteries. Inspired by HE-alloys, HE-oxides are an emerging class of multicomponent ceramics with promising electrochemical properties. This review will focus on the application of these materials to the development of new battery electrodes with insight into the materials’ structure/property relationship and battery performance. We highlight recent results on HE-oxides for the cathode and anode. In addition, we discuss some emerging results on HE-solid electrolytes and HE-alloy anodes.

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.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.534
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.279
Teacher spread0.259 · 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