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Record W2968138744 · doi:10.1039/c9ta05981d

Single iron atoms stabilized by microporous defects of biomass-derived carbon aerogels as high-performance cathode electrocatalysts for aluminum–air batteries

2019· article· en· W2968138744 on OpenAlexaff
Ting He, Yaqian Zhang, Yang Chen, Zhenzhu Zhang, Haiyan Wang, Yongfeng Hu, Min Liu, Chih‐Wen Pao, Jeng‐Lung Chen, Lo Yueh Chang, Zhifang Sun, Juan Xiang, Shaowei Chen

Bibliographic record

VenueJournal of Materials Chemistry A · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsCanadian Light Source (Canada)Saskatoon Medical ImagingAlberta Hospital EdmontonUniversity of Alberta
FundersChina Scholarship CouncilDivision of ChemistryNational Natural Science Foundation of China
KeywordsAerogelMicroporous materialMaterials scienceAluminiumCarbon fibersCatalysisCathodeChemical engineeringPorosityPlatinumBiomass (ecology)Inorganic chemistryMetallurgyNanotechnologyComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Biomass-derived carbon aerogel with hierarchical porosity and FeN <sub>4</sub> single atom sites outperforms platinum towards the oxygen reduction reaction in alkaline media and can be used as the cathode catalyst for aluminium–air batteries.

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)0.001
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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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

Citations106
Published2019
Admission routes1
Has abstractyes

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