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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it