Electrolysis of glycerol to value‐added chemicals in alkaline media
Bibliographic record
Abstract
Abstract BACKGROUND Glycerol, a by‐product of biodiesel production, is produced in large quantities, exceeding its demand. The saturation of glycerol resulted in a sharp reduction in its market value and the surplus waste may pose a risk to the environment. By means of electrochemical technologies, glycerol could be oxidized into value‐added products such as glycerate, tartronate and lactate. In the present work, carbon‐supported NiBi catalysts with different atomic ratios (NixBi1−x/C, wherex = 100, 95, 90 and 50 at%) were fabricated and utilized in a 25 cm2 electrolysis cell. RESULTS The as‐fabricated catalysts were characterized and analyzed by various physicochemical and electrochemical characterizations. Using a three‐electrode electrochemical cell, Ni95Bi5/C showed the highest current density of 104 mA cm−2, with an onset potential of 1.32 V versus a reversible hydrogen electrode. Long‐term chronoamperometry was performed in a glycerol electrolysis cell accompanied by the product analysis using high‐performance liquid chromatography. It was found that Ni95Bi5/C had higher selectivity to glycerate C3 product compared to Ni/C. Additionally, optimizing experimental conditions (applied potential, residence time and temperature) to achieve higher selectivity to C3 products was thoroughly studied. The selectivity to C3 value‐added products was enhanced by adjusting the operating conditions. CONCLUSION Small addition of bismuth to Ni/C enhanced both catalytic activity and selectivity to C3 products. The main products formed on NixBi1−x/C were formate and glycerate, while the secondary products were glycolate, tartronate, oxalate and lactate. By running electrolysis under optimal conditions, the selectivity to C3 products was significantly enhanced. © 2022 Society of Chemical Industry (SCI).
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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 | 0.001 |
| 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".