Nanomorphology-Enhanced Gas-Evolution Intensifies CO<sub>2</sub> Reduction Electrochemistry
Bibliographic record
Abstract
Nanostructured CO 2 reduction catalysts now achieve near-unity reaction selectivity at increasingly improved Tafel slopes and low overpotentials. With excellent surface reaction kinetics, these catalysts encounter CO 2 mass transport limitations at current densities ca. 20 mA cm –2 . We show here that – in addition to influencing reaction rates and local reactant concentration – the morphology of nanostructured electrodes enhances long-range CO 2 transport via their influence on gas-evolution. Sharper needle morphologies can nucleate and release bubbles as small as 20 μm, leading to a 4-fold increase in the limiting current density compared to a nanoparticle-based catalyst alone. By extending this observation into a diffusion model that accounts for bubble-induced mass transport near the electrode’s surface, diffusive transport can be directly linked to current densities and operating conditions, identifying efficient routes to >100 mA cm –2 production. We further extend this model to study the influence of mass transport on achieving simultaneously high selectivity and current density of C2 reduction products, identifying precise control of the local fluid environment as a crucial step necessary for producing C2 over C1 products.
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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".