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Record W2896294412 · doi:10.1002/anie.201805256

Tuning Cu/Cu<sub>2</sub>O Interfaces for the Reduction of Carbon Dioxide to Methanol in Aqueous Solutions

2018· article· en· W2896294412 on OpenAlexaff
Xiaoxia Chang, Tuo Wang, Zhi‐Jian Zhao, Piaoping Yang, Jeffrey Greeley, Rentao Mu, Gong Zhang, Zhongmiao Gong, Zhi‐Bin Luo, Jun Chen, Yi Cui, Geoffrey A. Ozin, Jinlong Gong

Bibliographic record

VenueAngewandte Chemie International Edition · 2018
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsCarbon dioxideAqueous solutionMethanolReduction (mathematics)Electrochemical reduction of carbon dioxideInorganic chemistryChemistryMaterials scienceChemical engineeringCarbon monoxideOrganic chemistryCatalysisMathematicsEngineering

Abstract

fetched live from OpenAlex

Abstract Artificial photosynthesis can be used to store solar energy and reduce CO 2 into fuels to potentially alleviate global warming and the energy crisis. Compared to the generation of gaseous products, it remains a great challenge to tune the product distribution of artificial photosynthesis to liquid fuels, such as CH 3 OH, which are suitable for storage and transport. Herein, we describe the introduction of metallic Cu nanoparticles (NPs) on Cu 2 O films to change the product distribution from gaseous products on bare Cu 2 O to predominantly CH 3 OH by CO 2 reduction in aqueous solutions. The specifically designed Cu/Cu 2 O interfaces balance the binding strengths of H* and CO* intermediates, which play critical roles in CH 3 OH production. With a TiO 2 model photoanode to construct a photoelectrochemical cell, a Cu/Cu 2 O dark cathode exhibited a Faradaic efficiency of up to 53.6 % for CH 3 OH production. This work demonstrates the feasibility and mechanism of interface engineering to enhance the CH 3 OH production from CO 2 reduction in aqueous electrolytes.

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)0.000
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 score0.036
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.024
GPT teacher head0.278
Teacher spread0.254 · 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.

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

Citations229
Published2018
Admission routes1
Has abstractyes

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