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Record W3049756924 · doi:10.20964/2020.09.26

New Synthesis route of Iron-Based Catalyst for Electrochemical Oxygen Reduction Reaction

2020· article· en· W3049756924 on OpenAlexaff
Qin Lu, Xiaochun Yu, Jun Li, Shuangyan Li, Yiyan Liu, Peng‐Cheng Qian, Jichang Wang, Shun Wang, Huile Jin

Bibliographic record

VenueInternational Journal of Electrochemical Science · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsOxygen reduction reactionElectrochemistryCatalysisReduction (mathematics)OxygenChemistryChemical engineeringCombinatorial chemistryElectrodeMathematicsOrganic chemistryEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Among existed oxygen reduction electrocatalysts, iron-based catalysts have shown great advantages of low cost and extraordinary reactivity, which are even comparable to commercialized platinum based catalysts. However, the propensity of iron catalysts to aggregate and passivate has emerged as a fundamental barrier to high-power fuel cell applications. In this study, biomass egg yolk derived carbon nanotubes were designed as an armor to host iron complexes, offering multiple active sites such as Fe-N x , Fe 3 C, Fe 2 P for efficient oxygen reduction reaction (ORR). Although the true active sites of iron-based catalysts on the enhanced ORR activities is still under debates, a consensus on the contributions of Fe-N x active center has been reached via a smart material design in this work, which enables ORR onset potential at 0.9 V vs. RHE with excellent four-electron selectivity in alkaline media. Meanwhile, the state-of-the-art carbon shells promote the performance stability remarkably (retaining above 96% of its activity after 27 hours).

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)0.002
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 score0.073
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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