3D‐Printable Fluoropolymer Gas Diffusion Layers for CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
Abstract The electrosynthesis of value‐added multicarbon products from CO2 is a promising strategy to shift chemical production away from fossil fuels. Particularly important is the rational design of gas diffusion electrode (GDE) assemblies to react selectively, at scale, and at high rates. However, the understanding of the gas diffusion layer (GDL) in these assemblies is limited for the CO2 reduction reaction (CO2RR): particularly important, but incompletely understood, is how the GDL modulates product distributions of catalysts operating in high current density regimes > 300 mA cm−2. Here, 3D‐printable fluoropolymer GDLs with tunable microporosity and structure are reported and probe the effects of permeance, microstructural porosity, macrostructure, and surface morphology. Under a given choice of applied electrochemical potential and electrolyte, a 100× increase in the C2H4:CO ratio due to GDL surface morphology design over a homogeneously porous equivalent and a 1.8× increase in the C2H4 partial current density due to a pyramidal macrostructure are observed. These findings offer routes to improve CO2RR GDEs as a platform for 3D catalyst design.
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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".