Highly selective preparation of valuable dinitronaphthalene from catalytic nitration of 1‐nitronaphthalene with NO<sub>2</sub> over HY zeolite
Bibliographic record
Abstract
Abstract In this work, a simple method for the highly selective preparation of valuable dinitronaphthalene from 1‐nitronaphthalene employing NO2 as a nitrating agent has been developed. The results demonstrated that HY zeolite as an eco‐friendly and stable catalyst exhibits good catalytic performances. The total selectivity to valuable dinitronaphthalene compounds including 1,5‐, 1,4‐, and 1,3‐dinitronaphthalene can reach 87.6 % in our present nitration reaction. Meanwhile, the physico‐chemical properties of catalysts were characterized by XRD, FTIR, TG/DTG, BET, and NH3‐TPD, and the probable nitration reaction mechanism was also suggested in this paper. The present nitration process seems to be a mild, eco‐friendly, and economical route for the preparation of valuable dinitronaphthalene compounds.
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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 | 0.000 |
| 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".