MétaCan
Menu
Back to cohort
Record W4391638538 · doi:10.1149/ma2023-02472380mtgabs

Visualizing the Effect of Temperature on Carbonate Salt Production in CO<sub>2</sub> Electrolysis

2023· article· en· W4391638538 on OpenAlexaff
Vasant Batta, Qianpu Wang, Spencer Lytle, Aimy Bazylak

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsNational Research Council CanadaUniversity of Toronto
Fundersnot available
KeywordsCarbonateElectrolysisSalt (chemistry)Production (economics)Environmental scienceMaterials scienceChemistryMetallurgyElectrodeEconomicsPhysical chemistryElectrolyte

Abstract

fetched live from OpenAlex

Carbon dioxide (CO2) electrolysis is a novel and promising technology to tackle anthropogenic climate change by reducing our dependency on fossil fuels and creating a sustainable carbon cycle. However, current CO2 electrolysis suffers from low CO2 reduction reaction (CO2RR) selectivity and rapid gas diffusion electrode (GDE) degradation and flooding due to carbonate precipitation. As the hydrophilic precipitate accumulates in the porous structure of the cathode GDE, the liquid anolyte invades the pores, blocking the catholyte from reaching the catalyst layer. This effect promotes the adverse hydrogen evolution reaction and thus decreases the CO2RR selectivity (1). Current literature focuses on limiting carbonate precipitate formation as opposed to understanding the factors affecting its growth (2). While carbonate precipitation is dependent on the specific electrolyte in a flow cell, the impact of parameters, such as operating temperature and GDE porosity, on the nature of carbonate precipitation still need to be investigated (3). In this study, we examine the relationship between cell temperature with carbonate precipitate accumulation. A zero-gap alkaline membrane electrode assembly (MEA) was used with potassium carbonate as the anolyte and humidified CO2 as the catholyte. Electrochemical impedance spectroscopy was used to calculate the activation, ohmic, and mass transport losses of the cell before and after a constant current density phase where faradaic efficiency was measured to quantify the CO2RR selectivity degradation due to salt accumulation. Furthermore, to characterize precipitate growth, the cathode GDEs were imaged before and after electrochemical testing using scanning electron microscopy (SEM) and wavelength dispersive spectroscopy (WDS). To quantify precipitate-driven flooding over operation time, identical MEAs were imaged using operando X-ray synchrotron radiography over a range of temperatures. Through-plane images of the operating cell were processed to find the electrolyte thickness in the cathode GDE as a function of time. Radiography results revealed progressive, cyclical flooding as well as preferential gas pathways. Moreover, after four hours of constant current operation and at a cell temperature of 60℃, the catalyst layer exhibited large amounts of uniformly distributed precipitates, while the GDE contained large, localized precipitate crystals. This indicates that at high temperatures, carbonate precipitates significantly block the catalyst layer and cause flooding into the substrate layer. This work provides new insight into the effect of temperature on carbonate precipitate buildup, deepening the understanding of the mechanism responsible for precipitate degradation. M. E. Leonard, L. E. Clarke, A. Forner-Cuenca, S. M. Brown, and F. R. Brushett, ChemSusChem, 13 (2020). Y. Xu et al., ACS Energy Lett, 6, 809–815 (2021). E. R. Cofell, U. O. Nwabara, S. S. Bhargava, D. E. Henckel, and P. J. A. Kenis, ACS Appl Mater Interfaces, 13, 15132–15142 (2021).

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 score0.001
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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