Pt <sub> <i>x</i> </sub> Cu <sub> 100– <i>x</i> </sub> /C Bimetallic Catalyst toward Glycerol Electrooxidation in Alkaline Electrolyte: Tuning the Product Selectivity to Glyceric Acid
Bibliographic record
Abstract
Valorization of glycerol through electrocatalytic oxidation is a promising and environmentally friendly method that can utilize renewable electricity inputs. Currently, a key challenge facing the development of glycerol oxidation electrolyzers is the lack of cost-effective catalysts with high activity, stability, and selectivity. In this study, bimetallic Pt x Cu 100– x /C catalysts synthesized by a chemical reduction method with various metal ratios are shown to enhance glycerol oxidation reaction performance with the addition of Cu to Pt compared to both Pt/C synthesized by the same methods as well as commercial Pt/C. Among the synthesized catalysts, Pt 31 Cu 69 /C was determined as the best-performing, exhibiting the highest Pt-mass normalized current density (5.9 mA μg Pt –1 ), the highest geometrical current density (75.3 mA cm –2 ), and a low onset potential (∼0.38 V vs RHE). Pt 31 Cu 69 /C also achieved a high selectivity to glyceric acid (75%) and C 3 products (86%) in an alkaline electrolyte over 10 h of chronoamperometry at 0.6 V vs RHE. Moreover, under these same conditions, Pt 31 Cu 69 /C produced 2.5-fold higher amount of glyceric acid in comparison to the synthesized Pt/C catalyst via glycerol electrooxidation. The time and electrode potential-dependent product analysis of glycerol electrooxidation reaction for the Pt 31 Cu 69 /C catalyst revealed that the addition of Cu to Pt inhibits C–C bond breaking and leads to an increased selectivity of C 3 products. Moreover, a reaction pathway of glycerol electrooxidation was proposed for Pt 31 Cu 69 /C, highlighting the possible chemical conversions that occur in the alkaline electrolyte.
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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".