Highly Efficient Carbon Dioxide Electroreduction via DNA-Directed Catalyst Immobilization
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".