Selective hydrogenation of oleic acid to fatty alcohols using <scp>Rh‐Sn‐B</scp> / <scp> TiO <sub>2</sub> </scp> catalysts: Influence of <scp>Sn</scp> content
Bibliographic record
Abstract
Abstract The influence of the catalyst Sn content on the production of fatty alcohol from oleic acid by selective hydrogenation was studied using Rh‐Sn‐B catalysts supported on TiO 2 . The crystal phase of the support was analyzed by X‐ray diffraction (XRD), the reduction state of the metal phase by temperature‐programmed reduction (TPR), and the electronic state of surface species by X‐ray photoelectron spectroscopy (XPS). The metal activity was evaluated by the dehydrogenation of cyclohexane. It was found that the increase in Sn content leads to a proportional drop in the catalytic activity, which could be related to a metallic interaction between Rh and Sn, as shown by TPR. Oxide and metallic Sn, as well as Rh 0 and Rh 3+ , were found by XPS on the catalyst surface. Metallic Rh was, however, found in higher concentration than oxidized Rh in all cases. The yield to fatty alcohols increased with Sn content, and its maximum value for oleyl and stearyl alcohol was 96%. Furthermore, a higher yield (88.3%) was obtained out of unsaturated fatty alcohol (oleyl alcohol), which has proved to be more valuable than saturated alcohol. This was attributed to an adequate Rh/Sn ratio, which modulates the hydrogenating activity of Rh and makes the metal function more selective for hydrogenation of the carbonyl group. The influence of the support on the catalyst performance decreases as the Sn content increases. The support has a practically negligible influence on the catalyst activity for 4–5 wt.% of Sn content.
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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) | 0.001 | 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 | 0.001 |
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".