Synthesis, Organo-Functionalization, and Catalytic Properties of Tungsten Oxide Nanoparticles As Heterogeneous Catalyst for Oxidative Cleavage of Oleic Acid As a Model Fatty Acid into Diacids
Bibliographic record
Abstract
A series of tungsten oxide nanoparticles (NPs) has been synthesized via a green and straightforward approach exploiting bare tungsten powder as a precursor. The synthesized NPs were further organo-functionalized by cetyltrimethylammonium bromide (CTAB) in order to adjust their surface state and enhance their compatibility with biphasic oxidation of vegetable oils with H 2 O 2 . Simply, different structures of tungsten oxide were observed, which were characterized by XRD, FTIR, TGA, TEM, N 2 adsorption/desorption isotherms, and zeta potential analysis. All the synthesized nanocatalysts could fully convert oleic acid, and the highest yield of production of the desired diacid (azelaic acid), ∼80%, was achieved by optimization of the CTA + amount on the nanocatalyst’s surface, which show excellent activity compared to the reported heterogeneous works. Thanks to the organo-functionalization, this water-tolerant catalyst exhibited no significant leaching, as well as convenient recovery and steady reuse without a noticeble decrease in activity, at least up to four cycles.
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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".