High-Density Nanosharp Microstructures Enable Efficient CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
Conversion of CO 2 to CO powered by renewable electricity not only reduces CO 2 pollution but also is a means to store renewable energy via chemical production of fuels from CO. However, the kinetics of this reaction are slow due its large energetic barrier. We have recently reported CO 2 reduction that is considerably enhanced via local electric field concentration at the tips of sharp gold nanostructures. The high local electric field enhances CO 2 concentration at the catalytic active sites, lowering the activation barrier. Here we engineer the nucleation and growth of next-generation Au nanostructures. The electroplating overpotential was manipulated to generate an appreciably increased density of honed nanoneedles. Using this approach, we report the first application of sequential electrodeposition to increase the density of sharp tips in CO 2 electroreduction. Selective regions of the primary nanoneedles are passivated using a thiol SAM (self-assembled monolayer), and then growth is concentrated atop the uncovered high-energy planes, providing new nucleation sites that ultimately lead to an increase in the density of the nanosharp structures. The two-step process leads to a new record in CO 2 to CO reduction, with a geometric current density of 38 mA/cm 2 at −0.4 V (vs reversible hydrogen electrode), and a 15-fold improvement over the best prior reports of electrochemical surface area (ECSA) normalized current density.
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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".