Selective Electrified Propylene-to-Propylene Glycol Oxidation on Activated Rh-Doped Pd
Bibliographic record
Abstract
Renewable-energy-powered electrosynthesis has the potential to contribute to decarbonizing the production of propylene glycol, a chemical that is used currently in the manufacture of polyesters and antifreeze and has a high carbon intensity. Unfortunately, to date, the electrooxidation of propylene under ambient conditions has suffered from a wide product distribution, leading to a low faradic efficiency toward the desired propylene glycol. We undertook mechanistic investigations and found that the reconstruction of Pd to PdO occurs, followed by hydroxide formation under anodic bias. The formation of this metastable hydroxide layer arrests the progressive dissolution of Pd in a locally acidic environment, increases the activity, and steers the reaction pathway toward propylene glycol. Rh-doped Pd further improves propylene glycol selectivity. Density functional theory (DFT) suggests that the Rh dopant lowers the energy associated with the production of the final intermediate in propylene glycol formation and renders the desorption step spontaneous, a concept consistent with experimental studies. We report a 75% faradic efficiency toward propylene glycol maintained over 100 h of operation.
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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".