Synthesis of quaternary metal oxides immobilized on APTMS-coated magnetite: an efficient and reusable nanocatalyst for Knoevenagel condensation under green conditions
Bibliographic record
Abstract
A simple, green, and highly efficient procedure has been developed for Knoevenagel condensation of malononitrile and aromatic aldehydes to the corresponding benzylidenemalononitriles in the presence of Fe3O4@APTMS@Zr–Sb–Ni–Zn nanoparticles (NPs) as a durable nanocatalyst. The heterogeneous nanocomposite was prepared by immobilization of Zr–Sb–Ni–Zn mixed metal oxides on APTMS-coated magnetite. The synthesized catalyst was characterized using Fourier-transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), X-ray diffraction (XRD), vibration sample magnetometer (VSM), transmission electron microscopy (TEM), and Brunauer–Emmett–Teller analysis (BET). In this approach, the condensation of malononitrile and aromatic aldehydes were done in water under reflux conditions to give the corresponding products within 3–60 min in 89%–95% yields. The magnetically recoverable catalyst was recycled and reused about four times without remarkable loss of activity. This method offers various benefits such as mild and eco-friendly reaction conditions, short reaction times, high purity of products, excellent efficiency, use of water as green solvent, and reusability and recoverability of the nanostructure catalyst.
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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".