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Record W4285399017 · doi:10.1149/ma2022-01391776mtgabs

Pulse Electrodeposition of Cu on Porous Ag Framework for Electrochemical CO<sub>2</sub> Conversion to Ethanol

2022· article· en· W4285399017 on OpenAlexaff
Jiwon Park, Chaehwa Jeong, Moony Na, Yusik Oh, Yongsoo Yang, Hye Ryung Byon

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsOxygenateCarbon monoxideCatalysisElectrochemistryChemistryElectrolyteSelectivityFaraday efficiencyYield (engineering)Inorganic chemistryElectrochemical reduction of carbon dioxideCarbon dioxideAqueous solutionSolubilityChemical engineeringElectrodeMaterials scienceOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Electrochemical conversion of carbon dioxide (CO 2 ) gas is a simple and cost-benefit method to produce economically valuable materials such as carbon monoxide (CO), multi-carbon (C 2+ ) oxygenate, and hydrocarbons. In particular, ethanol (EtOH) production is attractive to be applied for conventional transport fuels. However, there has been a lack of techniques to yield predominant EtOH from CO 2 . Copper (Cu), as the well-known and exclusive C 2+ catalyst, thermodynamically prefers the production of (C 2 H 4 ) compared to EtOH. Previous studies suggested that increased concentration of carbon monoxide (CO) as the intermediate of CO 2 ameliorated the EtOH selectivity. 1,2 Because the solubility of CO is very little in an aqueous electrolyte solution, surface diffusion of CO has been considered to enhance the local CO concentration. It suggests that catalyst interface is crucial to increasing EtOH yield, while little knowledge was developed. We prepared porous Ag inverse opal (AgIO) frameworks with a uniform pore size of ~ 600 nm. The CO 2 gas was electrochemically reduced to CO with &gt; 90% selectivity at -1.05 V vs. RHE. In addition, the CO settled in the pore, providing an opportunity for sequential electrochemical/chemical reactions. We deposited the ultra-thin Cu layer (2~10 nm) upon the Ag surface through the pulse electrodeposition technique (PED). This catalyst, indicated as PEDCu/AgIOs, performed ~33 % Faradaic efficiency (FE) of EtOH at -1.05 V vs. RHE. Moreover, PEDCu/AgIOs showed a high oxygenates ratio relative to hydrocarbons (FE oxygenate /FE C2H4 ) of 2.28, while H 2 evolution was significantly suppressed. These results suggested that as-prepared thin Cu film was partially segregated, and the Ag surface was exposed, forming the mixed Ag and Cu interfaces in the given electrochemical condition. This hypothesis was confirmed by negligible EtOH from the Ag-free CuIOs structure. More importantly, the resulting EtOH selectivity outperformed Cu nanoparticles (~7 nm diameter) dispersed on AgIOs (7.45 % FE) and aggregated Cu nanostructures on AgIOs (17.65 % FE) prepared by the constant current mode of electrodeposition. We, therefore, anticipated that CO spillover was promoted by the remaining and ultra-thin Cu layer. Further experiments were conducted by controlling of the thickness, pore size, and interpore size of AgIO frameworks to understand their roles. PEDCu/AgIOs with larger AgIOs thickness and smaller pore size showed higher EtOH selectivity, while the interpore size did not significantly affect the product selectivity. In the presentation, I will discuss key parameters to determine EtOH selectivity and the presumable pathway of EtOH production in details. References Ting, L. R. L.; Piqué, O.; Lim, S. Y.; Tanhaei, M.; Calle-Vallejo, F.; Yeo, B. S., Enhancing CO 2 Electroreduction to Ethanol on Copper–Silver Composites by Opening an Alternative Catalytic Pathway. ACS Catal. 2020, 10 (7), 4059-4069. Gurudayal; Perone, D.; Malani, S.; Lum, Y.; Haussener, S.; Ager, J. W., Sequential Cascade Electrocatalytic Conversion of Carbon Dioxide to C-C Coupled Products. ACS Appl. Energy Mater. 2019 , 2 (6), 4551-4559.

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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 score0.007
Threshold uncertainty score0.654

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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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