An Alternative Method for the Selective Synthesis of Ortho-nitro Anilines Using Bismuth Nitrate Pentahydrate
Bibliographic record
Abstract
Background: Nitroaromatic compounds are important scaffolds used for the syn-thesis of a variety of compounds, such as explosives, herbicides, dyes, perfumes and phar-maceuticals. Bismuth nitrate pentahydrate is a widely used reagent in organic synthesis; how-ever, its utility as a nitrating agent for anilines is underexplored. Objective: The aim of this work is to propose and find the proper reaction conditions of an alternative nitrating agent constituted by a mixture of bismuth nitrate / acetic anhydride in DCM with a series of substituted anilines under mild reflux. Methods: Several anilines having both activating and deactivating substituents in the ortho, meta and para positions were the substrate for the nitration reaction. Experimental conditions were performed in “one-pot” conditions before product purification. Results: Bi(NO3)3•5H2O demonstrated to be effective and somehow regioselective when it came to the nitration of anilines in the ortho position. Although other products were also identified under these conditions, in most cases, the ortho derivative was the major or even the only product obtained with moderate to high yields in the range of 50% – 96%. Conclusion: Bi(NO3)3•5H2O is an efficient and safe nitrating agent since the use of concen-trated and corrosive acids like sulfuric and nitric is avoided; furthermore, bismuth nitrate is low-priced and no special care nor equipment is required.
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 | 0.000 | 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".