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Record W3185044190 · doi:10.1149/ma2021-0126955mtgabs

Catalyst-Proximal Plastrons for Enhancing Electrocatalytic Reduction of CO<sub>2</sub> on Copper in Aqueous-Phase Systems

2021· article· en· W3185044190 on OpenAlexaff
Sami Khan, Jonathan Hwang, Yang Shao‐Horn, Kripa K. Varanasi

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFormateCopperCatalysisHydrogenAqueous solutionChemistryMethaneChemical engineeringInorganic chemistrySolubilityMethanolMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Electrocatalytic reduction of CO2 (CO2RR) is an attractive method of converting CO2 to solar fuels. Much research in this field has been focused on developing novel catalysts to enhance the activity and product distribution (selectivity). Copper has been studied widely as it can produce both one carbon (C1) products such as methane and formate, and products with two or more carbons (C2+) such as ethylene, ethanol, and n-propanol1. Copper is not immune from the competing hydrogen evolution reaction: the poor solubility of CO2 in water (~34 mM at ambient conditions) limits the CO2RR current density, and hydrogen generation is favored if the aqueous CO2 concentration becomes locally undersaturated close to the catalyst during CO2RR2. This limitation is even more pronounced for nanotextured copper, because the increased active surface area leads to faster depletion of local CO2 thereby promoting hydrogen evolution over CO2RR. Hence, overcoming these CO2 availability limitations can enable higher CO2RR activity while reducing co-evolution of hydrogen. In this work, we develop a gasphilic CO2 trap that increases gas-liquid mass transfer and maintains supersaturated CO2 concentration around the catalyst during CO2RR. Gasphilic surfaces need a special combination of surface chemistry and texture to capture bubbles and form a sheet of gas underwater which is called a plastron3. By creating pyramidal textures, CO2 bubbles are efficiently captured within the textures and form a CO2 plastron. When this plastron is placed proximal to both smooth and nanostructured copper catalysts during CO2RR, the current density is enhanced and maintained throughout the reaction, when compared to two commonly used methods of CO2 delivery: headspace and bubbling in the bulk electrolyte. The plastron allows for quick replenishment of CO2 during the CO2RR reaction in the vicinity of the catalyst. As a result, the H2 Faradaic efficiency is reduced from 33% to 13% on smooth copper, and from 62% to 33% on nanostructured copper. This is accompanied by an increased production of C2+ products including ethylene, ethanol and propanol, as well as acetone and acetate at Faradaic efficiencies exceeding 1%. We highlight the importance of this catalyst-proximal plastron approach by comparing against recent aqueous-phase CO2RR studies, and discuss how this approach can inform optimal design of continuous CO2RR systems such as gas-diffusion electrodes. [1] Nitopi, S., Bertheussen, E., Scott, S. B., Liu, X., Engstfeld, A. K., Horch, S., Seger, B., Stephens, I. E. L., Chan, K., Hahn, C., Nørskov, J. K., Jaramillo, T. F. & Chorkendorff, I. Progress and Perspectives of Electrochemical CO2 Reduction on Copper in Aqueous Electrolyte. Chem. Rev. 119, 7610–7672 (2019). [2] Lobaccaro, P., R. Singh, M., Lee Clark, E., Kwon, Y., T. Bell, A. & W. Ager, J. Effects of temperature and gas–liquid mass transfer on the operation of small electrochemical cells for the quantitative evaluation of CO2 reduction electrocatalysts. Physical Chemistry Chemical Physics 18, 26777–26785 (2016). [3] Panchanathan, D., Rajappan, A., Varanasi, K. K. & McKinley, G. H. Plastron Regeneration on Submerged Superhydrophobic Surfaces Using In Situ Gas Generation by Chemical Reaction. ACS Appl. Mater. Interfaces 10, 33684–33692 (2018).

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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 score0.003

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 communication0.0000.000
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 head0.014
GPT teacher head0.266
Teacher spread0.252 · 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
Published2021
Admission routes1
Has abstractyes

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