3D Bimetallic Platinum‐Nickel Electrodes for Electro‐Oxidation of Glycerol at Ambient Conditions
Bibliographic record
Abstract
Abstract Partial electro‐oxidation using renewable electricity offers a sustainable route for valorizing glycerol, a major by‐product of biofuel production. This study introduces a bimetallic electrode with nanostructured platinum dendrites on nickel foam (Pt/NiF), achieving a peak geometric current density of 235 mA cm−2 and a Pt‐mass normalized current density of 3.71 for glycerol electro‐oxidation at 0.92 V versus a reversible hydrogen electrode (RHE) in 3 m KOH electrolyte containing 1 m glycerol, outperforming most previously reported Pt‐containing catalysts. The Pt/NiF electrode demonstrates over 92% cumulative selectivity toward C3 products, with 64% selectivity for lactic acid at 0.65 V versus RHE over 5 h of testing. This research also highlights the role of chemical oxidation pathways (isomerization and rearrangements) in converting glycerol to lactic acid. After 5 h at 0.65 V versus RHE, the Pt/NiF electrode maintains 35% of its initial current density, plateauing at 12.2 mA cm−2 (0.15 ), with performance loss likely due to surface poisoning by carbon‐based reaction intermediates/byproducts or passivating platinum (hydr)oxide species. These findings pave the way for developing low‐platinum group metal catalysts with high glycerol oxidation affinity and highlight the importance of considering chemical transformations during catalyst evaluation and reactor design.
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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 | 0.001 | 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".