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Record W4362591598 · doi:10.1007/s12274-023-5513-5

Isolated Cu-Sn diatomic sites for enhanced electroreduction of CO2 to CO

2023· article· en· W4362591598 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNano Research · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsMcGill University
Fundersnot available
KeywordsCatalysisSelectivityElectrolysisChemistryFaraday efficiencyChemisorptionRedoxInorganic chemistryHydrogenDiatomic moleculeMaterials scienceElectrochemistryChemical engineeringPhysical chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Electrochemical CO2 reduction reaction (CO2RR) to high-value product, CO, not only provides a key feedstock for the well-established Fischer—Tropsch process but also mitigates the greenhouse effect. However, it suffers from sluggish reaction kinetics, competitive hydrogen evolution reaction, and low selectivity. Herein, we report non-precious Cu-Sn diatomic sites anchored on nitrogen-doped porous carbon (CuSn/NPC) as an efficient catalyst for CO2RR to CO. The catalyst exhibits outstanding selectivity with CO Faradaic efficiency (FE) up to 99.1%, much higher than those of individual Cu (66.2%) and Sn (51.3%) single-atom catalysts. Moreover, high stability is confirmed by consecutive 24 h electrolysis with high selectivity from CO2 to CO. Theoretical calculations reveal an obvious activation of CO2 with weakened C—O bonds and distorted CO2 configuration upon chemisorption on the CuSn/NPC catalyst. It is also suggested CuSn/NPC is more selective for the CO2RR with dominant CO production during the electrolysis, rather than the competing hydrogen evolution reaction.

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.

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 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 score0.026
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.059
GPT teacher head0.408
Teacher spread0.349 · 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