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Record W3148416643 · doi:10.1039/d1ee00505g

High-entropy energy materials: challenges and new opportunities

2021· article· en· W3148416643 on OpenAlexaff
Yanjiao Ma, Yuan Ma, Qingsong Wang, Simon Schweidler, Miriam Botros, Tongtong Fu, Horst Hahn, Torsten Brezesinski, Ben Breitung

Bibliographic record

VenueEnergy & Environmental Science · 2021
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
FundersHorizon 2020 Framework ProgrammeDeutsche ForschungsgemeinschaftEuropean CommissionCarl-Zeiss-Stiftung
KeywordsElectrochemical energy storageEnergy storageCatalysisNanotechnologyEntropy (arrow of time)Materials scienceElectrochemistryProcess engineeringEngineering physicsChemistryThermodynamicsPhysicsEngineeringPhysical chemistrySupercapacitorOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

An overview of high-entropy materials for energy applications, including H 2 catalysis and storage, CO 2 conversion, O 2 catalysis and electrochemical energy storage, is given and the challenges and opportunities within this field are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.005
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.186
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations899
Published2021
Admission routes1
Has abstractyes

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