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Record W3117422100 · doi:10.1021/acs.jpclett.0c02812

Oxygen Coordination on Fe–N–C to Boost Oxygen Reduction Catalysis

2020· article· en· W3117422100 on OpenAlexafffund
Hangjia Shen, Hong‐Jie Peng, Rui Cao, Liu Yang, Yan Gao, Ayse Turak, Tiju Thomas, Xuyun Guo, Ye Zhu, Jiacheng Wang, Minghui Yang

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMcMaster University
FundersChina Postdoctoral Science FoundationMinistry of Science and Technology of the People's Republic of ChinaOntario Ministry of Research, Innovation and ScienceNational Natural Science Foundation of China
KeywordsCatalysisOxygenOxygen atomAtom (system on chip)ChemistryCoordination numberMetalCoordination complexSynchrotronCatalytic cycleOxygen reductionCrystallographyNanotechnologyMaterials sciencePhysical chemistryMoleculePhysicsIonOrganic chemistryComputer scienceElectrochemistryElectrode

Abstract

fetched live from OpenAlex

The coordination environments of iron (Fe) in Fe–N–C catalysts determine their intrinsic activities toward oxygen reduction reactions (ORR). The precise atomic-level regulation of the local coordination environments is thus of critical importance yet quite challenging to achieve. Here, atomically dispersed Fe–N–C catalyst with O–Fe–N 2 C 2 moieties is thoroughly studied for ORR catalysis. Advanced synchrotron X-ray characterizations, along with theoretical modeling, explicitly unraveled the penta-coordinated nature of the Fe center in the catalytic domain and the energetically optimized ORR pathways on the well-tailored O–Fe–N 2 C 2 moieties. The combined structure identification from both experiments and theory provides an opportunity to understand the role of the coordination environments in directing the catalytic activity of single-atom or single-site catalysts; not only the center metal atom but also the whole coordinating atoms participate in the catalytic cycle.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.216
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 source (direct Gemma or distilled Codex), 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

Citations26
Published2020
Admission routes2
Has abstractyes

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