Cation‐Infused Bilayer Ionomer Coating Enables High Partial Current Density Toward Multi Carbon Products in CO<sub>2</sub> Electrolysis
Bibliographic record
Abstract
Abstract Electrochemical CO2 reduction (eCO2R) stands as a pivotal technology for carbon recycling by converting CO2 into value‐added products. While significant strides have been made in generating multi‐carbon (C2+) products like ethylene (C2H4) and ethanol (C2H5OH) at industrial‐scale current densities with high Faradaic efficiency (FE), cathode flooding and (bi)carbonate salt accumulation remain a fundamental concern in an alkaline electrolyte. In this work, ion‐conducting polymers are used to tailor the micro‐environment mitigating cathode flooding and salt precipitation and thus, enhancing the local CO2 availability. The impact of cation and anion exchange ionomer layers, specifically Nafion and Sustainion XA‐9 are examined on overall eCO2R performance. The use of an ultra‐thin bilayer configuration significantly reduces cathode flooding and salt accumulation by ≈58% compared to commercial anion exchange membrane (AEM). Alongside, cation infusion improves the C─C bond formation inducing a favorable micro‐environment for selective C2+ formation. This cation‐infused bilayer ionomer (CIBLI) achieves a high partial current density of ≈284 mA cm−2 toward C2+ products maintaining a stable eCO2R performance for 24 hours (h). This scalable approach of directly deposited ultra‐thin CIBLI offers a minimal conversion energy of 117 GJ/ ton C2+ products with an energy efficiency (EE) of 29% at 350 mA cm−2 current density in one‐step CO2 conversion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".