Color-Coordinated Photocatalysis of the One-Pot Synthesis of Schiff Bases from Benzyl Alcohol and Nitro Compounds Using a Hybrid Magnetic Catalyst
Bibliographic record
Abstract
The versatility and significance of imines (Schiff bases) make them highly attractive for many industrial applications. This study investigates photocatalytic routes for the one-pot synthesis of Schiff bases using alcohol and an aromatic nitro compound as reagents, rather than the more conventional amine and aldehyde or ketone. Utilizing photoirradiation at 370 nm with TiO2 loaded with various metals, we demonstrate the exceptional efficiency of the one-pot synthesis of Schiff bases under an inert atmosphere. Notably, the Fe3O4@TiO2 magnetic catalyst offers an excellent option for synthesizing the corresponding imine, achieving a remarkable production rate of 6.8 mmol h−1 during the first 6 h of irradiation with UVA light and reaching over 99% yield after 20 h. This success is attributed to a series of reactions involving the photocatalytic oxidation of benzyl alcohol to benzaldehyde and the simultaneous in situ reduction of nitrobenzene to aniline. The subsequent catalytic condensation of these products, facilitated by the active sites at the TiO2-metal interface, ultimately yields the desired imine. Additionally, while irradiation in the UVA region alone can photocatalyze the process, incorporating blue light (450 nm) accelerates it significantly. Dual-wavelength irradiation increased the production of the benzaldehyde to 77.9 mmol and more than doubled the Schiff base yield, from 7.5 mmol (with UVA light) to 17 mmol in 3 h of irradiation. Additionally, the reusability of the catalyst under simultaneous 450 nm and 370 nm light exposure significantly enhanced Schiff base production, which rose from 16.9 mmol to 48.9 mmol after adding fresh 0.1 M nitrobenzene for the second use. This highlights the effectiveness of color-coordinated catalysis in advancing sustainable chemistry through two-color photochemistry. The magnetic catalytic system not only demonstrates remarkable performance but also shows excellent reusability, representing a promising alternative for sustainable and efficient chemical transformations.
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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".