Electrokinetic Analyses Uncover the Rate‐Determining Step of Biomass‐Derived Monosaccharide Electroreduction on Copper
Bibliographic record
Abstract
Abstract Electrochemical biomass conversion holds promise to upcycle carbon sources and produce valuable products while reducing greenhouse gas emissions. To this end, deep insight into the interfacial mechanism is essential for the rational design of an efficient electrocatalytic route, which is still an area of active research and development. Herein, we report the reduction of dihydroxyacetone (DHA)—the simplest monosaccharide derived from glycerol feedstock—to acetol, the vital chemical intermediate in industries, with faradaic efficiency of 85±5 % on a polycrystalline Cu electrode. DHA reduction follows preceding dehydration by coordination with the carbonyl and hydroxyl groups and the subsequent hydrogenation. The electrokinetic profile indicates that the rate‐determining step (RDS) includes a proton‐coupled electron transfer (PCET) to the dehydrated intermediate, revealed by coverage‐dependent Tafel slope and isotopic labeling experiments. An approximate zero‐order dependence of H+ suggests that water acts as the proton donor for the interfacial PCET process. Leveraging these insights, we formulate microkinetic models to illustrate its origin that Eley–Rideal (E−R) dominates over Langmuir–Hinshelwood (L−H) in governing Cu‐mediated DHA reduction, offering rational guidance that increasing the concentration of the adsorbed reactant alone would be sufficient to promote the activity in designing practical catalysts.
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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".