Ureate Titanium Catalysts for Hydroaminoalkylation: Using Ligand Design to Increase Reactivity and Utility
Bibliographic record
Abstract
Hydroaminoalkylation describes the atom-economical catalytic synthesis of amines by forming new C sp 3 –C sp 3 bonds using readily available amine and alkene feedstocks. Herein, we describe an earth-abundant and cost-efficient titanium catalyst generated in situ using commercially available Ti(NMe 2 ) 4 and a simple to synthesize urea proligand. This system demonstrates high TOFs for hydroaminoalkylation with unactivated substrates and features easy to use commercially available titanium amido precursors. Additionally, a high catalytic activity, scope of reactivity, and regioselectivity are all demonstrated in the transformation of unactivated terminal olefins with various alkyl and aryl secondary amines. Finally, syntheses of useful amine-containing monomers suitable for the generation of amine-containing materials, as well as amine-containing building blocks for medicinal chemistry, are disclosed. These preparative methods avoid the necessity of glovebox techniques and are modified to be useful to all synthetic chemists.
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".