Activity and Selectivity of Nanostructured Sulfur-Doped Pd/SBA-15 Catalyst for Vegetable Oil Hardening
Bibliographic record
Abstract
Sunflower and canola oils were hardened over a novel sulfur-promoted Pd catalyst. The formulated catalyst comprised of 0.7 wt % palladium nanoparticles promoted with 0.3 wt % sulfur highly dispersed on mesoporous silica SBA-15 (0.7% Pd−0.3% S/SBA-15). The effect of temperature (80−130 °C) and H 2 pressure (3.6−9.3 atm) on the activity, selectivity, and trans (TFA) and saturated (SFA) fatty acids formation were studied for both oils. Under similar temperature and H 2 -pressure conditions, sunflower and canola oils exhibited different reactivities toward hydrogenation. For both oils, the activity of the catalyst increased with the temperature and with the pressure. However, increasing the temperature raised the levels of trans C18:1 especially at low hydrogen pressure. Partial hydrogenation of both oils from initial iodine (IV o ) values of 120−130 to a final value of 90 with the sulfur-doped Pd catalyst yielded modified oils with a low TFA level (6%) and also controlled the formation of SFA (∼7%) under mild temperature conditions (80 °C) and relatively moderate hydrogen pressure (9 atm). The sulfur-doped Pd catalyst proved to be versatile and more active than the Pd catalyst using similar Pd loading and reaction conditions but slightly less selective toward monoene formation. It was found that the intraparticle diffusion limitation for triglycerides does not occur during the hydrogenation of vegetable oils, but the hydrogen transfer limitation cannot be neglected and could partially explain the formation of trans -fatty acids. Modulating the Weisz−Prater modulus used to demonstrate the presence of H 2 intraparticle gradients could be a criterion to control the hydrogenation and cis−trans isomerization activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".