Hydrogenation of Sunflower Oil over Bimetallic Supported Catalysts on Mesostructured Silica Material
Bibliographic record
Abstract
Hydrogenation of vegetable oils using stable catalysts with satisfactory activity and selectivity as well as very low trans fatty acid (TFA) and saturated fatty acid (SFA) production is a challenging task. It is known that unhealthy TFA formation is the result of positional and conjugative isomerization side-reactions occurring during hydrogenation. From this standpoint, it is possible to formulate active, selective and stable catalysts which would minimize the production of TFA and SFA.Monometallic Pd and bimetallic Pd-Me (Me=Mo, Ni, Co, Ru, and Sr,) highly dispersed on mesostructured SBA-silica material with pore size ranging from 6 to 7 nm, BET-specific surface of 800-900 m²/g, and metal nominal total loading up to 1.0 % w/w, were comparatively investigated as catalysts for lowering the unhealthy trans (TFA) and saturated (SFA) fatty acids and maximizing the highly health-beneficial cis-monoenes production during the hydrogenation of sunflower oil at 110 oC under hydrogen pressure of 5 atm.The Pd-catalyst at nominal metal loading of 0.8 % supported on nanostructured support was active and selective for the hydrogenation of sunflower oil under mild process conditions. It produced less saturated acid and reached a good selectivity towards monoenes. In all cases, the consecutive impregnations of Pd and a second metal on the mesoporous silica support preserved the mesoporous structure of the support with slight modification of the textural characteristics in terms of BET surface area, pore size distribution and total pore volume. The addition of nominal 0.2 % Co, Sr or Ru to 0.8% Pd-catalyst enhanced its activity. However, the addition of nominal 0.2 % Ni or Mo dropped the activity of monometallic Pd-catalyst significantly. It is clearly shown that Ru had some promoting effect to inhibit further the formation of trans fatty acids. The addition of a second metal to Pd-catalyst had no significant effect on the formation of C18:0.High degree of metal-metal, metal-support as well as metal-oil interactions would greatly influence the reaction mechanisms of the vegetable oil hydrogenation.
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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) | 0.000 | 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) | 0.000 | 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".