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Record W4281739397 · doi:10.1016/j.xinn.2022.100268

Spin engineering of single-site metal catalysts

2022· article· en· W4281739397 on OpenAlexafffund
Zichuang Li, Ruguang Ma, Qiangjian Ju, Qian Liu, Lijia Liu, Yufang Zhu, Minghui Yang, Jiacheng Wang

Bibliographic record

VenueThe Innovation · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsWestern University
FundersBasic Energy SciencesProgram of Shanghai Academic Research LeaderOffice of ScienceNatural Sciences and Engineering Research Council of CanadaU.S. Department of EnergyState Key Laboratory of ASIC and System, Fudan UniversityNational Natural Science Foundation of China
KeywordsSpin (aerodynamics)CatalysisMicroporous materialSinglet stateSpin statesSpin crossoverTransition metalChemistryNanotechnologyChemical physicsMaterials scienceAtomic physicsCrystallographyPhysicsInorganic chemistryThermodynamicsExcited state

Abstract

fetched live from OpenAlex

Single-site metal atoms (SMAs) on supports are attracting extensive interest as new catalytic systems because of maximized atom utilization and superior performance. However, rational design of configuration-optimized SMAs with high activity from the perspectives of fundamental electron spin is highly challenging. Herein, N-coordinated Fe single atoms are successfully distributed over axial carbon micropores to form dangling-FeN 4 centers (d-FeN 4 ). This unique d-FeN 4 demonstrates much higher intrinsic activity toward oxygen reduction reaction (ORR) in HClO 4 than FeN 4 without micropore underneath and commercial Pt/C. Both theoretical calculation and electronic structure characterization imply that d-FeN 4 endows central Fe with medium spin (t 2g 4 e g 1 ), which provides a spin channel for electron transition compared with FeN 4 with low spin. This leads to the facile formation of the singlet state of oxygen-containing species from triplet oxygen during the ORR, thus showing faster kinetics than FeN 4 . This work provides an in-depth understanding of spin tuning on SMAs for advanced energy catalysis.

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 score0.003

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 head0.013
GPT teacher head0.211
Teacher spread0.199 · 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

Citations43
Published2022
Admission routes2
Has abstractyes

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