Rational Design of Efficient Palladium Catalysts for Electroreduction of Carbon Dioxide to Formate
Bibliographic record
Abstract
The electrochemical reduction of CO 2 into renewable chemical products such as formic acid is an important and challenging goal. Traditional Pd catalysts suffer from CO poisoning, which leads to current density decay and short operating lifetimes. Here we explored the ability to control Pd nanoparticle surface morphology to amplify catalytic activity and increase stability in the electroreduction of CO 2 to formate. Through computational studies we have elucidated trends in intermediate binding which govern the selectivity and catalytic activity. We then rationally synthesized Pd nanoparticles having an abundance of high-index surfaces to maximize electrocatalytic performance. This catalyst displays a record current density of 22 mA/cm 2 at a low overpotential of −0.2 V with a Faradaic efficiency of 97%, outperforming all previous Pd catalysts in formate electrosynthesis. The findings presented in this work provide rational design principles which highlight morphological control of high-index surfaces for the effective and stable catalytic electroreduction of CO 2 to liquid fuels.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".