MétaCan
Menu
Back to cohort
Record W4360620353 · doi:10.1002/smll.202370079

Engineering Energy Level of FeN<sub>4</sub> Sites via Dual‐Atom Site Construction Toward Efficient Oxygen Reduction (Small 12/2023)

2023· article· en· W4360620353 on OpenAlexaff
Zhaoyan Luo, Xianliang Li, Tingyi Zhou, Yi Guan, Jing Luo, Lei Zhang, Xueliang Sun, Chuanxin He, Qianling Zhang, Yongliang Li, Xiangzhong Ren

Bibliographic record

VenueSmall · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsWestern University
Fundersnot available
KeywordsCatalysisOxygen reductionMoietyLeverage (statistics)Oxygen reduction reactionOxygenOxygen atomKinetic energyAtom (system on chip)Materials scienceNanotechnologyChemistryPhysical chemistryStereochemistryPhysicsComputer scienceOrganic chemistryElectrodeElectrochemistryMolecule

Abstract

fetched live from OpenAlex

Efficient Oxygen Reduction In article number 2205283, Lei Zhang, Xueliang Sun, Xiangzhong Ren, and co-workers leverage single-atom Fe-N-C as a model system and report an adjacent Ru-N4 moiety modulation effect to optimize the catalyst's electronic configuration and oxygen reduction reaction (ORR) performance. The resultant FeN4/RuN4 catalyst shows excellent ORR performance with a halfwave potential of 0.958 V and a high kinetic current density of 28.57 mA cm−2, which significantly surpasses that of commercial Pt/C.

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 score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.232
Teacher spread0.191 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueSmallSame topicAdvanced Nanomaterials in CatalysisFrench-language works237,207