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Record W4253285543 · doi:10.1149/ma2019-01/31/1588

Switching between CO<sub>2</sub> Electroreduction Pathways

2019· article· en· W4253285543 on OpenAlexaff
Ali Seifitokaldani

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsMcGill University
Fundersnot available
KeywordsFormateChemistryFaraday efficiencyElectrochemistryElectrolyteHydroniumInorganic chemistryAqueous solutionOxygenAdsorptionPhysical chemistryElectrodeMoleculeCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Electrochemical carbon dioxide reduction (CO2R) is a promising technology to use renewable electricity to convert CO2 into valuable carbon-based products (1-4). Of metal-based electrocatalysts, silver is one of the best known materials to produce CO in aqueous media, via two proton-coupled electron-transfer processes, with a high selectivity of near 100% Faradaic efficiency (FE) (5). Here, in this study, using density functional theory (DFT), we showed that hydronium (H3O+) is a key intermediate in the first oxygen hydrogenation step to form adsorbed carboxyl (*COOH), and lowers the activation energy barrier for CO formation. Removing the hydronium influence, we found that the activation energy barrier for oxygen hydrogenation and therefore adsorbed carboxyl formation increases significantly, while the activation energy barrier for carbon hydrogenation and consequently adsorbed formate (*HCOO) formation reduces. This mechanism suggests that formate formation pathway at lower concentration of hydronium is more favorable than CO formation pathway. Inspired by these DFT results, we designed experiments at highly concentrated KOH solution, to limit the hydronium availability in the aqueous electrolyte. Using a gas diffusion electrode in flow cell configuration enabled us to utilize a very basic electrolyte by separating it from the CO2 gas stream, and also to run experiments at a high current density of 300 mA/cm2. We found that, the CO2R pathways switches from entirely CO formation under neutral condition, to almost 60% FE for formate formation in 11 M KOH. Different in situ and ex situ materials characterization such as XAS, XPS, SEM and XRD, demonstrated that the electrocatalysts before and after the reactions were identical. In addition, control experiments excluded the applied potential effect, confirming that formate formation was a direct effect of alkaline media and lack of hydronium. Observing high selectivity for formate formation on silver in aqueous media and at high current density has never been reported before. We believe that selectivity shift provides new insights into the role of hydronium on CO2 electroreduction processes and the ability for electrolyte manipulation to directly influence transition state kinetics, altering favored CO2 reaction pathways. C.-T. Dinh et al., CO2 electroreduction to ethylene via hydroxide-mediated copper catalysis at an abrupt interface. Science 360, 783 (2018). T.-T. Zhuang et al., Steering post-C–C coupling selectivity enables high efficiency electroreduction of carbon dioxide to multi-carbon alcohols. Nature Catalysis 1, 421-428 (2018). Z.-Q. Liang et al., Copper-on-nitride enhances the stable electrosynthesis of multi-carbon products from CO2. Nat Commun 9, 3828 (2018). M. G. Kibria et al., A Surface Reconstruction Route to High Productivity and Selectivity in CO2 Electroreduction toward C2+ Hydrocarbons. Advanced Materials 30, 1804867 (2018). C. M. Gabardo et al., Combined high alkalinity and pressurization enable efficient CO2 electroreduction to CO. Energy & Environmental Science, (2018). Figure 1

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)0.000
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 score0.002
Threshold uncertainty score0.006

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.237
Teacher spread0.223 · 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
Published2019
Admission routes1
Has abstractyes

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