Impact of metal oxides on the selectivity of NiBi/C catalysts for high-value C-3 products in glycerol electrooxidation reaction
Bibliographic record
Abstract
Enhancing the catalytic efficiency and specificity of non-noble Nickel-based catalysts for glycerol electro-oxidation reaction (GEOR) is an ongoing challenge. Recently, metal oxides have attracted attention due to their unique physical properties, such as chemical stability at high anodic potentials, presence of oxygen vacancies, which can significantly influence reaction kinetics and pathways. In this study, we explored the impact of metal oxides CeO 2 , SnO 2 , and Sb 2 O 3 • SnO 2 (ATO) on the electrochemical performance and product selectivity of NiBi/C and Ni/C catalysts in glycerol electrooxidation (GEOR). The Ni/C-X and NiBi/C-X (X= CeO2, SnO2, and ATO) catalysts were synthesized through a sodium borohydride reduction method at room temperature and comprehensively characterized using various physiochemical and electrochemical techniques. Among the composite support catalysts, Ni and NiBi/C-X, Ni/C-ATO exhibited the highest peak current density, reaching 96 mA cm -2 at 1.50 V vs. RHE. To further assess the catalysts' performance, continuous electrolysis, coupled with HPLC analysis, was conducted to determine product selectivity. Notably, NiBi/C-ATO demonstrated remarkable selectivity, achieving 100 % lactate selectivity under mild conditions. These findings contribute valuable insights into optimizing non-noble Nickel-based catalysts for GEOR, particularly highlighting the promising performance of Ni/C-ATO in terms of selectivity towards valuable products. The DFT calculations provided insight into the synergistic interactions for GEOR over Ni-Ni and Bi-Bi nanoparticles.
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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".