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 (Ni x Bi 1− x /C, where x = 100, 95, 90 and 50 at%) were fabricated and utilized in a 25 cm 2 electrolysis cell. RESULTS The as‐fabricated catalysts were characterized and analyzed by various physicochemical and electrochemical characterizations. Using a three‐electrode electrochemical cell, Ni 95 Bi 5 /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 Ni 95 Bi 5 /C had higher selectivity to glycerate C 3 product compared to Ni/C. Additionally, optimizing experimental conditions (applied potential, residence time and temperature) to achieve higher selectivity to C 3 products was thoroughly studied. The selectivity to C 3 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 C 3 products. The main products formed on NixBi 1− 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 C 3 products was significantly enhanced. © 2022 Society of Chemical Industry (SCI).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".