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Record W4377140828 · doi:10.1021/acsami.3c02859

Modulation of the Coordination Environment of Copper for Stable CO<sub>2</sub> Electroreduction with Tunable Selectivity

2023· article· en· W4377140828 on OpenAlexaff
Jianing Gui, Linbo Li, Baiyang Yu, Dan Wang, Bing Yang, Qingqing Gu, Yong Zhao, Yongfa Zhu, Ying Zhang

Bibliographic record

VenueACS Applied Materials & Interfaces · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersJiangnan UniversityChongqing Science and Technology CommissionNational Natural Science Foundation of China
KeywordsSelectivityCopperCatalysisMaterials scienceDensity functional theoryCarbon monoxideElectrochemistryImidazoleMoleculeAdsorptionInorganic chemistryDesorptionPhysical chemistryChemistryElectrodeComputational chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Manipulating the product selectivity of an electrochemical CO 2 reduction reaction (CO 2 RR) is challenging due to the unclear and uncontrollable active sites. Here, we report stable CO 2 RR operation with tunable product selectivity over a family of molecule-modulated copper catalysts. The coordination environment of Cu in catalysts is modulated by an imidazole-based molecule via different synthetic routes. Various carbonaceous products ranging from carbon monoxide, methane, and ethylene were selectively produced via, respectively, tuning the coordination environment of copper atoms from Cu–N, Cu–C, and Cu–Cu. Density functional theory (DFT) calculations reveal that the Cu–N sites weaken the adsorption energy of the *CO intermediate, which is beneficial for CO desorption. The Cu–C and Cu–Cu sites, respectively, facilitate the formation of *OCOH and *(CO) 2 intermediates, favoring the CH 4 and C 2 H 4 pathways. This work provides a stable and simple model system for studying the influence of coordination elements on the product selectivity of CO 2 RR.

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 score0.006
Threshold uncertainty score0.400

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 head0.010
GPT teacher head0.222
Teacher spread0.212 · 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

Citations25
Published2023
Admission routes1
Has abstractyes

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