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Record W4285399839 · doi:10.1149/ma2022-01391767mtgabs

(Invited) Anion Exchange Membrane and Ionomer Development for Electrochemical CO<sub>2</sub> Reduction

2022· article· en· W4285399839 on OpenAlexaffabout
Peter Mardle, Zhengming Jiang, Zhiqing Shi, Steven Holdcroft

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsNational Research Council CanadaSimon Fraser University
Fundersnot available
KeywordsElectrochemistryElectrolysisElectrolyteMaterials scienceIon exchangeChemical engineeringMembraneElectrolytic cellNanotechnologyHydroxideProcess engineeringIonChemistryElectrodeEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Recent developments have proven the economic potential of electrochemical reduction of CO2 to value added chemicals, where single cells are now capable of achieving high energetic efficiencies at industrially relevant current densities. 1 These advances are in no small part due to the increased ionic conductivity, hydroxide stability and commercial availability of anion exchange membranes (AEMs). However, there currently exists little understanding as to how these materials affect the efficiency of CO2 conversion devices because the research community is only now beginning to understand the variety and complexity of the transport processes involved. 2 In collaboration with Ionomr Innovations Inc. and the National Research Council of Canada, and as part of the Energy for Clean Materials Challenge Program, we have made advances in the understanding of how AEM properties affect device performance and how we can develop materials tailor-made for CO2 electrolyser technology. Here, we demonstrate the development of a zero-gap single cell design, utilizing first generation Aemion® materials for the conversion of CO2 to CO with an energetic efficiency of 40% at 200 mA cm-2. 3 Despite the initially high energetic efficiency, we demonstrate how the crossover of carbonate dianions results in the reduction of anolyte pH and deconvolute how this results in a diminished cell efficiency over extended operation. From this, we show how functionalization of the polymer electrolyte structure can reduce this degradation mechanism while retaining high energetic efficiencies. In addition, we demonstrate how under milder electrolysis conditions, the total cell efficiency has a significant dependency on the flux of alkali metal cationic species from the supporting anolyte to the cathode. We show that due to the large promotion effect of cations for the electrochemical CO2 reduction, AEM design not only influences ohmic resistances in the cell, but also greatly affects the charge transfer resistance (RCT) of the cathode to a much greater extent than other electrochemical conversion devices. We thus make correlations between water permeability and perm-selectivity of AEMs to the overall CO2 conversion efficiency. We then discuss the incorporation of anion exchange ionomers in the cathode catalyst layer of CO2 electrolysis cells and how the ionomer parameters define the efficiency and selectivity of Ag catalysts towards electrochemical CO2 reduction. Through this work, we demonstrate the influencing factors of AEM and ionomer materials on the efficiency of electrochemical CO2 conversion and conclude that further advances are paramount for the adoption of this promising technology, which is integral in closing the carbon loop of the petrochemical industry and meeting our wider climate change targets. References: P. De Luna et al., Science, 364, eaav3506 (2019). D. Salvatore, C. Gabardo, A. Reyes, and S. Holdcroft, Nat. Energy, 6, 339–348 (2021). P. Mardle, S. Cassegrain, F. Habibzadeh, Z. Shi, and S. Holdcroft, J. Phys. Chem. C, 125, 25446–25454 (2021). 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: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.008

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 head0.236
Teacher spread0.222 · 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
Published2022
Admission routes2
Has abstractyes

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