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Record W2052205171 · doi:10.1039/c4cp01455c

Fe–N bonding in a carbon nanotube–graphene complex for oxygen reduction: an XAS study

2014· article· en· W2052205171 on OpenAlexaff
Jigang Zhou, Paul N. Duchesne, Yongfeng Hu, Jian Wang, Peng Zhang, Yanguang Li, Tom Regier, Hongjie Dai

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2014
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsDalhousie UniversityCanadian Light Source (Canada)
Fundersnot available
KeywordsX-ray absorption spectroscopyXANESGrapheneCarbon nanotubeCatalysisNanotubeMetalAbsorption spectroscopyMaterials scienceCarbon fibersElectronic structureAbsorption (acoustics)ChemistryInorganic chemistryCrystallographySpectroscopyNanotechnologyComputational chemistryOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

The electronic structure study of carbon nanotube-graphene complexes has been performed using comprehensive X-ray absorption spectroscopy (XAS) at Fe L- and K-edges, along with C, N and O K-edges. The results obtained from the study of an iron-containing carbon nanotube-graphene complex (NT-G) have been compared in great detail with those of an iron-free carbon nanotube-graphene complex (pNT-G) and iron phthalocyanine (FePc). It has been confirmed that complex-like Fe(3+) in a high spin state is the major iron component in NT-G. The C and N K-edge XANES further confirmed that Fe is very likely to be bonded to N in NT-G. This Fe-N species should be the active site for enhanced oxygen reduction reaction (ORR) activity in NT-G. A unique O K-edge X-ray absorption spectroscopic feature has been observed in NT-G, which might be caused by chemisorbed O2 on the Fe-N site. Such knowledge is important for the understanding of this specific complex, and the knowledge should benefit the rational design of other carbon/metal/nitrogen-containing ORR catalysts with further improved performance.

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)0.000
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 score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.257
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.

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

Citations92
Published2014
Admission routes1
Has abstractyes

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