The First Inexpensive, Simplified and Large Scale Synthesis of p-tert-butylcalix[7] and [9]arenes
Bibliographic record
Abstract
The nuclear industry and the rare earth elements mining need methods in order to separate lanthanides and actinides. Large organic macrocycles have demonstrated their contribution to this domain. Due to the lack of an efficient and reproducible method of synthesis, the chemistry of large p-tert-butylcalix[n] arenes (with 6 < n < 10) has been much less explored than the one of their inferior homologs. Excepted for the p-tert-butylcalix[8]arene which is obtained in good yields, the procedures described in the literature for the preparation of p-tert-butylcalix[7] and [9]arenes involve many steps of purification and lead to, in the very best case, a couple of grams of product, after one month of labor. In this work, a set of experiments with varying parameters has demonstrateed the crucial role of the solvent and of the amount of catalyst in the macrocyclization reaction. Then, a scale-up study was done, the procedure being adapted and realized on a semi industrial scale reactor. Finally, an original purification pathway avoiding the numerous silica-gel chromatographies and recrystallizations was set up and optimized. This simplified procedure opens gateways for the preparation of a few dozen grams of pure p-tertbutylcalix[7] and [9]arenes and should lead to the preparation of new selective complexants.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".