An Overview of Glycerol Electrooxidation Mechanisms on Pt, Pd and Au
Bibliographic record
Abstract
Abstract In the most recent decade, glycerol electrooxidation (GEOR) has attracted extensive research interest for valorization of glycerol: the conversion of glycerol to value‐added products. These reactions at platinum, palladium, and gold electrodes have a lot of uncertainty in their reaction mechanisms, which has generated some controversies. This review gathers many reported experimental results, observations and proposed reaction mechanisms in order to draw a full picture of GEOR. A particular focus is the clarification of two propositions: Pd is inferior to Pt in cleaving the C−C bonds of glycerol during the electrooxidation and the massive production of CO 2 at high overpotentials is due to the oxidation of the already‐oxidized carboxylate products. It is concluded that the inferior C−C bond cleavability with Pd electrodes, as compared with Pt electrodes, is due to the inefficiency of deprotonation, and the massive generation of CO 2 as well as other C1/C2 side products is partially caused by the consumption of OH – at the anodes, as a lower pH reduces the amount of carboxylates and favors the C−C bond scission. A reaction mechanism is proposed in this review, in which the generation of side products are directly from glycerol (“competition” between each side product) rather than from the further oxidation of C2/C3 products. Additionally, GEOR results and associated interpretations for Ni electrodes are presented, as well as a brief review on the performances of multi‐metallic electrocatalysts (most of which are nanocatalysts) as an introduction to these future research hotpots.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".