MétaCan
Menu
Back to cohort
Record W3024640691 · doi:10.1149/ma2020-01361503mtgabs

Combined Simulation and Experimental Study of Electrolysis Flow Cell for Continuous CO<sub>2</sub> Conversion

2020· article· en· W3024640691 on OpenAlexaff
Guobin Wen, Bohua Ren, Jeff T. Gostick, Zhongwei Chen

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectrolyteElectrolysisGas diffusion electrodeChemical engineeringElectrodeElectrochemistryPolymer electrolyte membrane electrolysisChemistryMaterials scienceInorganic chemistry

Abstract

fetched live from OpenAlex

Electrochemical carbon dioxide reduction reaction (CO2RR) is a promising strategy to sequester CO2 while synthesizing valuable chemicals and utilizing intermittent renewable energy supply from solar and wind energy.1 Electrolysis is often studied in H-cells that are composed of planar electrodes immersed in an aqueous electrolyte, which have severely limited mass transport across the electrolyte and hydrodynamic boundary layer.2-3 To avoid these limitations alkaline flow cells with a gas diffusion electrode (GDE) operated in a flow-by mode are sometimes used to achieve more realistic conditions. Although they provide higher current densities (CD) and energy efficiencies (EE), they suffer from carbonate salt precipitation in the stagnant pores of the GDE, moreover in KOH electrolyte CO2 is parasitically converted to bicarbonate. To remedy the latter problem neutral electrolytes, such as K2SO4 or KHCO3, can replace alkaline electrolytes, but these have so far demonstrated low EE due to high ohmic resistance and overpotentials in the GDE. In this work we present a flow-through compact membrane electrode assemble (MEA) electrolysis cell for continuous CO2RR, which has following advantages. Firstly, the neutral electrolytes flowed through the porous electrode with carbon in the form of dissolved CO2 and HCO3 —.4 Electrolysis was carried out to produce CO gas and formate ions, which only need to pass through a thin boundary layer with minimized mass transport resistance. The porous electrode was pressed onto the membrane to ensure good ionic conductivity at the electrode−electrolyte interface. Secondly, flowing electrolyte eliminated degradation related to electrolyte flooding and carbonate precipitation. Finally, the overpotential was lowered through catalyst tuning and localized alkaline environment,5 contributing to cost competitive electroreduction of CO2 to CO, which exhibited partial current density (PCDCO) exceeding 150 mA cm−2 at cell overpotentials (|ηcell|) less than 2 V. Reference 1. De Luna, P.; Hahn, C.; Higgins, D.; Jaffer, S. A.; Jaramillo, T. F.; Sargent, E. H., What would it take for renewably powered electrosynthesis to displace petrochemical processes? Science 2019, 364 (6438). 2. Liu, M.; Pang, Y.; Zhang, B.; De Luna, P.; Voznyy, O.; Xu, J.; Zheng, X.; Dinh, C. T.; Fan, F.; Cao, C.; de Arquer, F. P. G.; Safaei, T. S.; Mepham, A.; Klinkova, A.; Kumacheva, E.; Filleter, T.; Sinton, D.; Kelley, S. O.; Sargent, E. H., Enhanced electrocatalytic CO2 reduction via field-induced reagent concentration. Nature 2016, 537 (7620), 382-386. 3. Wen, G.; Lee, D. U.; Ren, B.; Hassan, F. M.; Jiang, G.; Cano, Z. P.; Gostick, J.; Croiset, E.; Bai, Z.; Yang, L.; Chen, Z., Orbital Interactions in Bi-Sn Bimetallic Electrocatalysts for Highly Selective Electrochemical CO2 Reduction toward Formate Production. Adv. Energy Mater. 2018, 8 (31), 1802427. 4. Weng, L. C.; Bell, A. T.; Weber, A. Z., Modeling gas-diffusion electrodes for CO2 reduction. Phys Chem Chem Phys 2018, 20 (25), 16973-16984. 5. Verma, S.; Hamasaki, Y.; Kim, C.; Huang, W.; Lu, S.; Jhong, H.-R. M.; Gewirth, A. A.; Fujigaya, T.; Nakashima, N.; Kenis, P. J. A., Insights into the Low Overpotential Electroreduction of CO2 to CO on a Supported Gold Catalyst in an Alkaline Flow Electrolyzer. ACS Energy Letters 2018, 3 (1), 193-198.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.253
Teacher spread0.239 · 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 designSimulation or modeling
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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicCO2 Reduction Techniques and CatalystsFrench-language works237,207