Accelerate Mass Transport of Proton and Carbon Sources by Super‐Hygroscopic and Porous Nanosheets for Continuous CO<sub>2</sub>‐To‐Ethylene Upgrade
Bibliographic record
Abstract
Abstract Gas‐water/catalyst triple‐phase interface and the microenvironment play critical roles in the reaction kinetics and production rate of electrochemical carbon dioxide reduction reactions (CO2RR), which steer concerted proton‐electron transfer steps. Inspired by Tillandsia leaves, which efficiently capture H2O and CO2 from the air, copper nanosheets with dual‐functional channels are we designed: the superhygroscopic network enables capillary condensation, converting H2O(g) into H2O(l) to form H2O channels that ensure a stable supply of protons, while the CO2 channels formed by the microporous structure enhance the diffusion of CO2, thus enriching the carbon source. This synergistic design creates an optimal microenvironment for CO2 conversion by simultaneously delivering both protons and CO2 to the reaction interface. Time‐of‐flight secondary‐ion mass spectroscopy (TOF‐SIMS), X‐ray absorption spectroscopy (XAS) and multiphysics simulations further reveal the designed H2O and CO2 channels in the microenvironment to boost mass transports. Hence, the Faradaic efficiency (FE) for ethylene reaches up to 96% at ‐200 mA cm−2 with such localized triple‐phase interfaces, which simultaneously exhibits ultra‐high stability for over 170 h in the membrane electrode assembly (MEA) system. This strategy provides a construction methodology of H2O and CO2 channels for improving the selectivity and stability of electrochemical CO2 upgrades.
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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".