Regiospecific, one-pot, and pseudo-five-component synthesis of 6,6′-(arylmethylene)bis(2-(<i>tert</i>-butyl)4-methylphenol) antioxidants using highly sulfonated multi-walled carbon nanotubes under solvent-free conditions
Bibliographic record
Abstract
This is the first report of an innovative, one-pot, pseudo-five-component, and solvent-free synthesis of 6,6′-(arylmethylene)bis(2-(tert-butyl)4-methylphenol) antioxidants from p-cresol, methyl tert-butyl ether, and aldehydes in the presence of sulfonated multi-walled carbon nanotubes (MWCNTs-SO 3 H) as heterogeneous, robust, and reusable catalysts under solvent-free conditions. MWCNTs-SO 3 H was prepared and characterized by some microscopic and spectroscopic techniques including scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, dispersibility in different solvent, and thermogravimetric analysis (one functionality every approximately five carbon atoms). The acidity of the catalyst was measured by acid–base titration (1.80 mmol g −1 ). This reaction proceeds smoothly to give the products in good yields. The catalyst was reused several times without efficient loss of its activity for the preparation of 6,6′-(arylmethylene)bis(2-(tert-butyl)4-methylphenol) antioxidants. In addition, high yields of the products and solvent-free and nontoxicity of the catalyst are other worthwhile advantages of the present method.
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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) | 0.001 | 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".