Ni-Ag Bimetallic Magnetic Catalyst Improves the Performance of the Catalytic Transfer Hydrogenated Soybean Oil
Bibliographic record
Abstract
The role of Ni-Ag bimetallic magnetic catalysts in the catalytic transfer of hydrogenated soybean oil was studied. First, a Ni-Ag0.15/PVP-DB-171/SiO2/Fe3O4 magnetic catalyst with a magnetic saturation value of 10.431 emu / g was prepared. It was found that the addition of the metal Ag promoter enhanced the dispersion of Ni on the PVP-DB-171/SiO2/Fe3O4 support. The conditions of the catalytic transfer hydrogenation (CTH) (temperature 80°C, catalyst loading 0.23%, donor concentration 0.32 mol /50 mL H2O, and time 90 min) showed the effects of the bimetallic catalysts on the soybean oil hydrogenation process. The hydrogenated soybean oil linolenic acid, linoleic acid and oleic acid reaction rate constants were 4.95×10–2, 8.6×10–3 and 7.54×10–4, respectively. The selectivity of linolenic acid and linoleic acid is as high as 5.75 and 11.4, respectively; the iodine value (IV) of soybean oil after hydrogenation is 102 g I2/100g and the trans fatty acids(TFAs) content is only 1.7%. The use efficiency of the catalyst decreased to 60% after 8 cycles. Catalytic transfer hydrogenation has important research significance and application prospects for the preparation of low-trans hydrogenated oils and fats. This method also provides a theoretical basis for the development of the oil hydrogenation industry.
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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".