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Record W4241522792 · doi:10.1149/ma2017-02/45/1973

(Keynote) Experimental Considerations for Electrocatalytic CO<sub>2</sub> Reduction

2017· article· en· W4241522792 on OpenAlexaff
Aaron T. Marshall, Calvin F.C. Lim, Hani Taleshi Ahangari, David A. Harrington

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsElectrolyteCathodeSelectivityChemistryCatalysisElectrodeHydrogenInorganic chemistryElectrocatalystChemical engineeringMaterials scienceElectrochemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Electrocatalytic reduction of CO2 has the potential to convert CO2 into carbon-based fuels by using renewable energy. While a wide range of electrocatalytic materials have been investigated, the process is still limited by poor reaction selectivity or large overpotentials. Hence many have studied the effects of surface oxides, crystal facets, and nanoparticles on the activity and selectivity of CO2 reduction. Others have also shown that the reaction is sensitive to conditions such as temperature, CO2 pressure, electrolyte buffer concentration and electrode porosity. These effects mean that it can be challenging to determine the underlying cause for differences in intrinsic catalytic behaviour [1,2], and thus it is important to understand the effect of experimental conditions on CO2reduction. Firstly, we will review the literature and our own experimental work which clearly highlight the importance of interfacial pH on the reaction [2-4]. It is well known that in weak buffers such as KHCO3, the pH at the surface of the cathode is significantly higher than the bulk due to the hydrogen evolution and CO2 reduction reactions. By conducting experiments over a range of KHCO3 concentrations (and thus different interfacial pH values), it is clear that this alters the selectivity and activity of CO2 reduction. As the hydrodynamics at the cathode surface will also alter interfacial pH, we have examined the effect of hydrodynamics on the CO2 reduction reaction using a Cu rotating cylinder electrode [5]. Given that the enhanced mass transport will also increase the CO2 concentration at the electrode surface [6], it seems clear that mass transfer effects should influence the reaction selectivity. We confirm that this is indeed an important factor and suggest that increasing mass transport not only decreases the interfacial pH (closer to bulk values) but also decreases the surface coverage of CO on the cathode (a key immediate during CO2 reduction), which ultimately lowers the current going to the CO2reduction reaction. As these experiments revealed the importance of both KHCO3 concentration and mass transport, we developed a numerical model to predict how the bulk electrolyte composition changes during long term electrolysis experiments. This model was validated against experiment data and shows that changes in the bulk electrolyte (ionic resistance, pH, CO2-electrolyte equilibria) can occur over the course of long-term electrolysis experiments. These changes can complicate the interpretation of long-term electrode behaviour as well as the control of the electrochemical process. From these findings, we suggest a range of strategies to improve the experimental aspects of electrochemical CO2reduction investigations. [1] A.S. Hall, Y. Yoon, A. Wuttig, Y. Surendranath, Mesostructure-Induced Selectivity in CO2Reduction Catalysis, Journal of the American Chemical Society 137 (2015) 14834-14837. [2] R. Kas, R. Kortlever, H. Yılmaz, M.T.M. Koper, G. Mul, Manipulating the Hydrocarbon Selectivity of Copper Nanoparticles in CO2Electroreduction by Process Conditions, ChemElectroChem 2 (2015) 354-358. [3] K.J.P. Schouten, E. Pérez Gallent, M.T.M. Koper, The influence of pH on the reduction of CO and to hydrocarbons on copper electrodes, Journal of Electroanalytical Chemistry 716 (2014) 53-57. [4] A.S. Varela, M. Kroschel, T. Reier, P. Strasser, Controlling the selectivity of CO2electroreduction on copper: The effect of the electrolyte concentration and the importance of the local pH, Catalysis Today 260 (2016) 8-13. [5] C.F.C. Lim, D.A. Harrington, A.T. Marshall, Effects of mass transfer on the electrocatalytic CO2 reduction on Cu, Electrochimica Acta (2017) in press. [6] N. Gupta, M. Gattrell, B. MacDougall, Calculation for the cathode surface concentrations in the electrochemical reduction of CO2 in KHCO3solutions, Journal of Applied Electrochemistry 36 (2006) 161-172. [7] K. Hara, A. Tsuneto, A. Kudo, T. Sakata, Electrochemical Reduction of CO2 on a Cu Electrode under High Pressure: Factors that Determine the Product Selectivity, Journal of The Electrochemical Society 141 (1994) 2097-2103.

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 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)0.003
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.036
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0360.012

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 head0.027
GPT teacher head0.292
Teacher spread0.265 · 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
Published2017
Admission routes1
Has abstractyes

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