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Record W2965240903 · doi:10.1002/anie.201907250

Back Cover: A Single‐Atom Iridium Heterogeneous Catalyst in Oxygen Reduction Reaction (Angew. Chem. Int. Ed. 28/2019)

2019· paratext· en· W2965240903 on OpenAlexaff
Meiling Xiao, Jianbing Zhu, Gaoran Li, Na Li, Shuang Li, Zachary P. Cano, Lu Ma, Peixin Cui, Pan Xu, Gaopeng Jiang, Huile Jin, Shun Wang, Tianpin Wu, Jun Lü, Aiping Yu, Dong Su, Zhongwei Chen

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typeparatext
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIridiumCatalysisHomogeneousAtom (system on chip)Oxygen atomChemistryOxygen reduction reactionCover (algebra)Front coverNitrogenHomogeneous catalysisNitrogen atomPhysical chemistryElectrochemistryPhysicsOrganic chemistryMoleculeRing (chemistry)Computer science

Abstract

fetched live from OpenAlex

Single-atom catalysts combine the advantages of homogeneous and heterogeneous catalysis. In their Research Article on page 9640 ff., Z. Chen et al. describe the design and synthesis of a single-atom iridium catalyst coordinated with four nitrogen atoms (Ir-N-C) to mimic homogeneous iridium porphyrins for high-efficiency oxygen reduction reaction 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.401
Threshold uncertainty score0.855

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.4010.266

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 head0.011
GPT teacher head0.240
Teacher spread0.228 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2019
Admission routes1
Has abstractyes

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