One-Pot Synthesis of Dialkyl Hexane-1,6-Dicarbamate from 1,6-Hexanediamine, Urea, and Alcohol over Zinc-Incorporated Berlinite (ZnAlPO4) Catalyst
Bibliographic record
Abstract
Dialkyl hexane-1,6-dicarbamate was synthesized, for the first time, by a one-pot reaction of 1,6-hexanediamine (HDA), urea, and alcohols, including methanol, ethanol, propanol, and butanol, in a self-designed batch reactor, using zinc-incorporated berlinite (ZnAlPO4) as a catalyst. The yield of dibutyl hexane-1,6-dicarbamate (2) was systematically investigated as a function of Zn/Al molar ratio, reaction temperature, reaction time, catalyst usage and urea/HDA/butanol molar ratio. Based on these studies, the optimized reaction conditions were as follows: molar ratio urea/HDA/butanol = 2.6:1:8.6, catalyst usage = 3.0 g, reaction temperature = 493 K, reaction time = 6 h and reaction pressure = 1.2 MPa; a yield of 2 of 89.7% was achieved over the ZnAlPO4 (molar ratio Zn/Al = 0.04) catalyst. The catalysts were characterized by X-ray photoelectric spectroscopy (XPS) and scanning electron microscope (SEM). Additionally, based on these experimental results, it was also proposed that the catalysis recycle of the one-pot synthesis of 2 from urea, HDA, and butanol over the ZnAlPO4 catalyst.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".