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Record W2940224493 · doi:10.1002/cctc.201900395

A Rational Design of Cu<sub>2</sub>O−SnO<sub>2</sub> Core‐Shell Catalyst for Highly Selective CO<sub>2</sub>‐to‐CO Conversion

2019· article· en· W2940224493 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemCatChem · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence Fund
KeywordsOverpotentialElectrocatalystCatalysisFaraday efficiencyMaterials scienceElectrochemistryTinChemical engineeringNoble metalElectrolyteInorganic chemistryNanotechnologyChemistryElectrodeMetallurgyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The electrochemical reduction of CO 2 (CO 2 RR) is a versatile method that is capable of simultaneously reduce CO 2 emission and produce valuable fuels and chemicals. However, its application is hindered by the lack of cost‐effective catalysts and significant overpotential requirement. In this work, we report a low‐cost and surfactant/capping agent free method to synthesize cubic Cu 2 O−SnO 2 core‐shell electrocatalyst, whose thickness can be easily controlled by the content of tin precursor. The optimized Cu 2 O−SnO 2 catalyst with a 5 nm‐thick shell achieved over 90 % faradaic efficiency towards CO at a low overpotential of 390 mV, which is comparable to some of the noble metal catalysts. The catalyst also exhibited good stability over 18 hours of test at −0.6 V vs. RHE in 0.5 M KHCO 3 electrolyte . This work provides a widely applicable strategy for developing a low‐cost electrocatalyst for CO 2 conversion.

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 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 score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.247
Teacher spread0.229 · 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