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Record W4308298060 · doi:10.1007/s12274-022-4979-x

Modulating the electronic spin state by constructing dual-metal atomic pairs for activating the dynamic site of oxygen reduction reaction

2022· article· en· W4308298060 on OpenAlexaff
Shenghua Ye, Shuhua Xie, Yaqi Lei, Xiuyuan Yang, Jing Hu, Lirong Zheng, Zhida Chen, Yonghuan Fu, Xiangzhong Ren, Yongliang Li, Xiaoping Ouyang, Qianling Zhang, Jianhong Liu, Xueliang Sun

Bibliographic record

VenueNano Research · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsWestern University
Fundersnot available
KeywordsManganeseElectronic structureSpin statesSpin (aerodynamics)MetalMaterials scienceOxygenChemistryCrystallographyPhysicsInorganic chemistryComputational chemistryMetallurgyThermodynamics

Abstract

fetched live from OpenAlex

In this study, dual-metal atomic pairs of manganese (Mn)-iron (Fe) binuclear sites (BNSs) with two conjoint MnN 4 and FeN 4 moieties (MnFeN 8 ) anchored onto a graphite-like structure (GLS) (Mn-Fe BNSs/GLS) were constructed. The binuclear MnFeN 8 structure was verified experimentally and theoretically. Magnetic measurements and Gaussian calculations reveal that this unique Mn-Fe BNSs exhibit strong short-range electronic interaction between Mn and Fe sites, which decouples two paired d electrons in Fe sites, thereby transforming Fe sites from an intermediate to a high spin state. The optimal electronic configuration of Fe sites and their binuclear structure facilitate an oxygen reduction reaction (ORR) thermodynamically and dynamically, respectively, endowing Mn-Fe BNSs with improved ORR 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 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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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

Citations30
Published2022
Admission routes1
Has abstractyes

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