Constructing a Localized Buffer Interlayer to Elevate High-Rate CO<sub>2</sub>-to-C<sub>2+</sub> Electrosynthesis
Bibliographic record
Abstract
Catalytic surface and interface engineering for the electrosynthesis of multicarbon chemicals from CO 2 are widely investigated, while the selective regulation of mass transport for reactant CO 2 and intermediate CO remains rarely explored, which is a critical challenge limiting the C 2+ production rate. Here, we strategically construct a buffer interlayer with soluble ionic liquid (IL) additives between the aqueous electrolyte and the catalytic surface, which not only regulates the microenvironment of CO and CO 2 at different reaction stages but also stabilizes catalytic sites. The CO residence time is extended in the buffer interlayer ascribed to the attractive interactions via dipole–dipole interactions and hydrogen bonding. CO 2 and its transport are enhanced by the buffer reactions in the aqueous interlayer within the flow-through compact cell. Meanwhile, the utilization of ILs stabilizes active sites (Cu 2 O-derived Cu) by facilitating the regeneration of Cu 2 O through the applied potentials. Consequently, C 2+ products are synthesized at a high rate with a partial current density of 1.30 A/cm 2 for over 200 h. This concept is further scaled to a 100 cm 2 flow cell, exhibiting a carbon loss below 6%. Such a systematic investigation establishes a general construction strategy for the buffer interlayer and catalytic sites in electrolysis.
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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) | 0.001 | 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 | 0.001 |
| 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".