Pore Engineering for Directional CO<sub>2</sub> Enrichment in Urea Electrosynthesis
Bibliographic record
Abstract
Electrochemical synthesis of urea from CO 2 and nitrate offers a sustainable pathway to address both carbon emissions and nitrogen pollution. However, achieving high C–N coupling selectivity remains challenging due to competing hydrogen evolution reactions and insufficient CO 2 utilization. Herein, we implement a nanopore-structure engineering strategy to precisely tailor pore length and surface chemistry in metal-free porous carbon frameworks. Oxygen-functionalized surfaces augment CO 2 binding affinity via dipole–quadrupole interactions, while elongated pores induce directional CO 2 enrichment by establishing a H 2 O-deficient nanoenvironment that prolongs the residence time of CO 2 through capillary gating. This dual modulation of gas–liquid–solid interactions enhances urea selectivity and suppresses hydrogen evolution, yielding a 28% increase in Faradaic efficiency and 12% improvement in urea yield. Our findings propose a novel nanopore-level design concept that shall support the rational development of porous carbon supports across gas–liquid–solid electrocatalytic systems.
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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) | 0.000 | 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".