A Molecular Surface Functionalization Approach to Tuning Nanoparticle Electrocatalysts for Carbon Dioxide Reduction
Bibliographic record
Abstract
Conversion of the greenhouse gas carbon dioxide (CO2) to value-added products is an important challenge for sustainable energy research, and nanomaterials offer a broad class of heterogeneous catalysts for such transformations. Here we report a molecular surface functionalization approach to tuning gold nanoparticle (Au NP) electrocatalysts for reduction of CO2 to CO. The N-heterocyclic (NHC) carbene-functionalized Au NP catalyst exhibits improved faradaic efficiency (FE = 83%) for reduction of CO2 to CO in water at neutral pH at an overpotential of 0.46 V with a 7.6-fold increase in current density compared to that of the parent Au NP (FE = 53%). Tafel plots of the NHC carbene-functionalized Au NP (72 mV/decade) vs parent Au NP (138 mV/decade) systems further show that the molecular ligand influences mechanistic pathways for CO2 reduction. The results establish molecular surface functionalization as a complementary approach to size, shape, composition, and defect control for nanoparticle 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 | 0.001 |
| 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".