Highly Efficient Carbon Dioxide Electroreduction via DNA-Directed Catalyst Immobilization
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Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Electrochemical reduction of carbon dioxide (CO 2 ) is a promising route to up-convert this industrial byproduct. However, to perform this reaction with a small-molecule catalyst, the catalyst must be proximal to an electrode surface. Efforts to immobilize molecular catalysts on electrodes have been stymied by the need to optimize the immobilization chemistries on a case-by-case basis. Taking inspiration from nature, we applied DNA as a molecular-scale “Velcro” to investigate the tethering of three porphyrin-based catalysts to electrodes. This tethering strategy improved both the stability of the catalysts and their Faradaic efficiencies (FEs). DNA-catalyst conjugates were immobilized on screen-printed carbon and carbon paper electrodes via DNA hybridization with nearly 100% efficiency. Following immobilization, a higher catalyst stability at relevant potentials is observed. Additionally, lower overpotentials are required for the generation of carbon monoxide (CO). Finally, high FE for CO generation was observed with the DNA-immobilized catalysts as compared to the unmodified small-molecule systems, as high as 79.1% FE for CO at −0.95 V vs SHE using a DNA-tethered catalyst. This work demonstrates the potential of DNA “Velcro” as a powerful strategy for catalyst immobilization. Here, we demonstrated improved catalytic characteristics of molecular catalysts for CO 2 valorization, but this strategy is anticipated to be generalizable to any reaction that proceeds in aqueous solutions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it