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Record W4285400097 · doi:10.1149/ma2022-01361609mtgabs

Capping-Ligand Density on Cu Nanoparticles Determining Multi-Carbon Product Selectivity in Electrochemical CO<sub>2</sub> Reduction

2022· article· en· W4285400097 on OpenAlexaff
Yusik Oh, Hye Ryung Byon

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsCatalysisElectrochemistryCarbon monoxideAdsorptionYield (engineering)NanoparticleCopperSyngasElectrochemical reduction of carbon dioxideCarbon fibersSelectivityChemistryChemical engineeringMaterials scienceInorganic chemistryNanotechnologyOrganic chemistryMetallurgyElectrodeComposite material

Abstract

fetched live from OpenAlex

Carbon dioxide (CO2) emission from the combustion of fossil fuel causes global warming and climate change. Much attention has been paid for recycling of CO2 gas, namely converting this greenhouse gas to high-value fuels such as multi-carbon products (ethylene, ethanol, propanol, etc.). In particular, electrochemical CO2 reduction reaction (CO2RR) is one of the promising methods operated at ambient pressure and room temperature. By using gold and silver catalysts, carbon monoxide (CO) is successfully yielded with >80% Faradaic efficiency. In contrast, it is still challenging to produce multi-carbon (i.e., C2+) products. Copper (Cu) is known as the sole catalyst to yield C2+ products by affording the suitable binding energy to the intermediate of *CO (the asterisk denotes the surface adsorption). Although various synthetic methods for the preparation of Cu nanoparticles (NPs) have been developed to increase the surface area and design of the preferred facets for the CO2 reduction, the highly sensitive Cu surface in air and from contaminants resulted in variable Faradaic efficiencies for C2+ production. In particular, the capping ligands, which coat the Cu surface to stabilize the designed nanostructures, should significantly influence the catalytic efficiency and stability. However, their effects have been little studied. Here, we show the enhanced catalytic activity and stability of Cu NPs for the C2+ yield by eliminating the capping ligands.[1] The pristine Cu NPs with ~8 nm of diameter were prepared by using a capping ligand of tetradecylphosphonate (TDP). After UV-ozone treatment, the TDP ligands were eliminated in part; The phosphorous signal was attenuated, and the Cu surface was oxidized in X-ray photoelectron spectra. In addition, the carbonyl oxygen (O=C) emerged from the damaged TDP. In contrast, the carbon substrate and the surface roughness of the Cu were insignificantly changed. These UV-ozone-treated Cu NPs showed ~50% Faradaic efficiency of the C2+ conversion (FEC2+) at –0.98 V vs. RHE, which was twice as high as that of the pristine Cu NPs (~25%). In particular, the FEC2+ gradually increased as the remaining capping ligands were removed, indicating the increased catalytic sites for 3 h CO2RR. In sharp contrast, the FEC2+ from the pristine Cu NPs was consistently low despite the partial stripping of the capping ligands, suggesting the negligible formation of the C2+ active sites. For prolonged chronoamperometry tests for 20 h, these FEC2+ values were retained for both catalysts, while the size and shape of Cu catalysts were differently changed. The UV-ozone-treated ones proceeded little agglomeration and contained many grain boundaries. In contrast, the pristine Cu NPs were transformed to the cubic particles with ~100 nm size. The distinct alternations of Cu structures are closely related to the yield of the C2+ active sites, which I will discuss in this presentation. [1] J. Mater. Chem. A, 9, 11210 (2021) Figure 1

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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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