Acid‐free and highly efficient one‐step nitration of naphthalene with <scp> NO <sub>2</sub> </scp> promoted by <scp> O <sub>2</sub> </scp> ‐ <scp> Ac <sub>2</sub> O </scp> in Fe‐ and Cu‐modified <scp> S <sub>2</sub> O <sub>8</sub> <sup>2</sup> </scp> <sup>−</sup> / <scp> ZrO <sub>2</sub> </scp> catalyst to 1,5‐dinitronaphthalene under mild conditions
Bibliographic record
Abstract
Abstract It is crucial to develop an acid‐free, mild, and efficient strategy for preparing 1,5‐dinitronaphthalene (1,5‐DNN) from naphthalene (NT). This work presented a one‐step NT nitration method using NO 2 conjugated to an O 2 ‐Ac 2 O system over Fe‐/Cu‐modified S 2 O 8 2− /ZrO 2 catalyst (). The effects of different nitration systems and Fe‐/Cu‐modified ZrO 2 with various sulphur sources on NT nitration have been studied. Under optimal conditions, 99.5% of the NT conversion with 56.9% selectivity to 1,5‐DNN was obtained. Synergistic catalysis between the strong acid site and the current nitration system remarkably improved the 1,5‐DNN selectivity. The characterization results demonstrated that the appropriate Fe/Cu metals loadings combined with the covalent persulphates promoted the formation of more active tetragonal ZrO 2 . Chemical bonding of Zr 4+ with S 2 O 8 2− species allowed S 6+ more electrons to withdraw than in SO 4 2− species, which led to stronger acidity and better catalytic activity in the catalyst. Moreover, a plausible catalytic nitration reaction mechanism was proposed. The findings revealed that mild reaction conditions, combined with a solid superacid catalyst and NO 2 ‐O 2 ‐Ac 2 O system, offer significant advantages in reducing acid wastewater discharge and increasing target product selectivity.
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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) | 0.001 | 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".