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Record W2899632179 · doi:10.1002/anie.201812316

Cover Picture: Tuning Cu/Cu<sub>2</sub>O Interfaces for the Reduction of Carbon Dioxide to Methanol in Aqueous Solutions (Angew. Chem. Int. Ed. 47/2018)

2018· paratext· en· W2899632179 on OpenAlexaff
Xiaoxia Chang, Tuo Wang, Zhi‐Jian Zhao, Piaoping Yang, Jeffrey Greeley, Rentao Mu, Gong Zhang, Zhongmiao Gong, Zhibin Luo, Jun Chen, Yi Cui, Geoffrey A. Ozin, Jinlong Gong

Bibliographic record

VenueAngewandte Chemie International Edition · 2018
Typeparatext
Languageen
FieldMaterials Science
TopicCopper-based nanomaterials and applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCatalysisMethanolAqueous solutionChemistryMetalChemical engineeringNanoparticleNanotechnologyInorganic chemistryMaterials sciencePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Interfacial sites play critical roles in catalysis. However, the nature of the active interfacial sites for the photoelectroreduction of CO2 is not well understood. In their Communication on page 15415 ff., J. Gong and co-workers describe the deposition of metallic Cu nanoparticles on Cu2O films and their interactions, as well as the dependence of the CO2 reduction pathway on the Cu/Cu2O interface. The carefully designed Cu/Cu2O interfaces balance the binding strengths of the H* and CO* intermediates, which enables efficient methanol production. Interfacial sites play critical roles in catalysis. However, the nature of the active interfacial sites for the photoelectroreduction of CO2 is not well understood. In their Communication on page 15415 ff., J. Gong and co-workers describe the deposition of metallic Cu nanoparticles on Cu2O films and their interactions, as well as the dependence of the CO2 reduction pathway on the Cu/Cu2O interface. The carefully designed Cu/Cu2O interfaces balance the binding strengths of the H* and CO* intermediates, which enables efficient methanol production. MOF Membranes Water Splitting Analytical Methods

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), Insufficient payload (model declined to judge)
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 score0.158
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.023
GPT teacher head0.280
Teacher spread0.257 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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