Preparation and Characterization of New<i>C</i><sub>2</sub>‐ and<i>C</i><sub>1</sub>‐Symmetric Nitrogen, Oxygen, Phosphorous, and Sulfur Derivatives and Analogs of TADDOL. Part I
Bibliographic record
Abstract
Abstract The chloro alcohols 4 – 6 derived from TADDOLs (= α , α , α′ , α′ ‐tetraaryl‐1,3‐dioxolan‐4,5‐dimethanols) are used to prepare corresponding sulfanyl alcohols, ethers, and amines ( Scheme 1 and Table 1 ). The dithiol analog of TADDOL and derivatives thereof, 45 – 49 , were also synthesized. The crystal structures of 16 representatives of this series of compounds are reported ( Figs. 1 – 3 and Scheme 2 ). The thiols were employed in Cu‐catalyzed enantioselective conjugate additions of Grignard reagents to cyclic enones, with cycloheptenone giving the best results (er up to 94 : 6). The enantioselectivity reverses from Si ‐addition with the sulfanyl alcohol to Re ‐addition with the alkoxy or dimethylamino thiols ( Table 4 ). Cu I ‐Thiolates, 50 – 53 , could be isolated in up to 84% yield ( Scheme 2 ) and were shown to have tetranuclear structures in the gas phase (by ESI‐MS), in solution (CH 2 Cl 2 , THF; by vapor‐pressure osmometry and by NMR pulsed‐gradient diffusion measurements; Table 5 ), and in the solid state (X‐ray crystal structures in Scheme 2 ). The Cu complex 50 of the sulfanyl alcohol is stable in air and in the presence of weak aqueous acid, and it is a highly active catalyst (0.5 mol‐%) for the 1,4‐additions, leading to the same enantio‐ and regioselectivities observed with the in situ generated catalyst (6.5 mol‐%; Scheme 3 ). Since the reaction mixtures contain additional metal salts (MgX 2 , LiX) it is not possible at this stage, to propose a mechanistic model for the conjugate additions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".