Synthesis of Mesoporous Tungsten Oxide/γ-Alumina and Surfactant-Capped Tungsten Oxide Nanoparticles and Their Catalytic Activities in Oxidative Cleavage of Oleic Acid
Bibliographic record
Abstract
Abstract Unsaturated fatty acids can be converted into mono and dicarboxylic acids, which are applicably valuable materials, through oxidative cleavage reaction in the presence of a highly efficient catalyst/oxidant system. In this work, two types of advanced heterogeneous catalysts have been developed; (i) high surface area mesoporous tungsten oxide/γ-alumina mixed metal oxide, and (ii) surfactant-capped tungsten oxide nanoparticles. Various technique including N 2 adsorption/desorption isotherms, XRD, SEM, EDS, TGA and catalytic test were used to monitor the physicochemical and catalytic properties of these materials. The characterization results revealed that type (i) materials exhibit high surface area and narrow particle size distribution, and the used surfactant could quantitatively enough cap the surface of type (ii) materials. The catalytic activities of these materials in the oxidative cleavage of oleic acid with H 2 O 2 as oxidant were investigated. GC-MS was used to determine the produced amounts of desired products, azelaic and pelargonic acids. The catalytic test results showed more than 90 % conversion for type (ii) catalyst in 5 h reaction at 120 °C with acceptable production yields for azelaic and pelargonic acids. The significantly higher activity of this catalyst compared to type (i) arises from the interesting surface properties of tungsten oxides nanoparticles, which make them able to exploit the good features of homogeneous and heterogeneous catalysts.
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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) | 0.000 | 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 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".