Putting the squeeze on imine synthesis: citrus juice as a reaction medium in the introductory organic laboratory
Bibliographic record
Abstract
A less hazardous and energy efficient reaction performed using freshly squeezed citrus juice as solvent has been designed and implemented within a sophomore level organic chemistry laboratory. The primary learning objectives are to enable students to (i) identify and reflect upon various green chemistry principles such as waste prevention, atom economy, less hazardous synthesis, use of safer chemicals, catalysis, design for energy efficiency, and inherently safer chemistry for accident prevention; (ii) use proton NMR spectroscopic data to characterize a synthesized Schiff base (imine); and (iii) describe the reaction mechanism for imine formation, including the reasoning for why citrus juice is an excellent reaction medium. Specifically, 4-nitrobenzaldehyde is combined with 4-methoxyaniline at room temperature in the presence of four different fruit juices as reaction media to successfully synthesize an imine that is expensive to procure commercially. This is followed by students undertaking reduction of the imine to form a secondary amine which has a dramatically distinct color due to the disruption in conjugation. In performing this overall reductive amination, students expand their knowledge on acid-catalyzed imine synthesis and its mechanism, strengthen their practical skills in the laboratory, and reflect on green chemistry principles within the context of fundamental organic reactivity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".