Synthesis of magnetic core@dual shell <scp> Fe <sub>3</sub> O <sub>4</sub> </scp> @ <scp> SiO <sub>2</sub> </scp> @ <scp> WO <sub>3</sub> </scp> nanocatalysts for olefin double bond oxidative cleavage
Bibliographic record
Abstract
Abstract Herein, we report a new and efficient method for preparing Fe 3 O 4 @SiO 2 @WO 3 core@dual shell nanocatalysts featuring enhanced catalytic activity for the oxidative cleavage of the oleic acid double bond and fast magnetic separation. Various characterization techniques including EDS, XPS, SEM, and advanced elemental HRTEM mapping were used to characterize this type of magnetic core@dual shell nanocatalysts. The obtained core@shell structure is composed of a central magnetite core with a strong response to external fields, an interlayer of SiO 2 , and an outer layer of WO 3 nanocrystals. This core@shell nanocatalyst exhibited higher catalytic activity than reported tungsten based heterogenous catalysts for the oxidative cleavage of the oleic acid double bond. The presence of the SiO 2 interlayer prevents any deterioration of the catalytic efficiency of the WO 3 nanocrystal shell as well as the chemical and thermal stability of the Fe 3 O 4 core. This nanocatalyst can be easily recycled by applying an external magnetic field while maintaining its catalytic activity during at least three cycles of use.
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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".