Integrating Green Chemistry and Life Cycle Assessment into a Second-Year Organic Chemistry Laboratory
Bibliographic record
Abstract
Chemistry is not inherently ‘green’ but chemists can adapt their practices to make chemical processes ‘greener’ and lower environmental and human toxicity. To make chemistry greener, we need to train chemists to consider their role in minimizing human and environmental harm and give them the skills to choose safer and ‘greener’ syntheses. Beyond Benign has created a Green Chemistry Commitment to be signed by universities which gives them the responsibility to incorporate green chemistry into the undergraduate chemistry curriculum. One way to meet this commitment and teach about the impacts of chemical synthesis is through Life Cycle Assessment which quantitatively considers the human and environmental toxicity of synthetic routes. This project sought to adapt an existing second-year undergraduate chemistry laboratory experiment to incorporate ideas of green chemistry and Life Cycle Assessment. The experiment was performed with varying experimental conditions, including concentration, reaction time, catalyst loading, type of catalyst, and workup procedure to develop three procedures that can be performed or analyzed by students. Life Cycle Assessments were conducted on each of these procedures to scaffold a post-laboratory activity that showcases the effects of these varied experimental conditions on impact-based metrics. The adapted experiment prompts students to reflect on the ‘green-ness’ of each procedure based on metrics like smog formation, global warming, and human toxicity, as well as potential trade-offs with yield. This adaptation can be applied as a post-laboratory exercise to other undergraduate chemistry experiments without modifying the laboratory procedure. This work exemplifies how principles of green chemistry can be incorporated into educational settings without requiring a complete redesign of curricula. Future work may investigate how these principles may be incorporated into existing lecture activities of undergraduate chemistry courses.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".