Perspectives from the 2022 Cohort of the American Chemical Society Summer School on Green Chemistry & Sustainable Energy
Bibliographic record
Abstract
The field of chemistry is uniquely equipped to solve many current and impending global challenges; however, minimizing potential negative impacts on the environment, society, and the economy requires a holistic approach to developing new processes and chemicals. For this reason, there is an urgent need to incorporate green chemistry and systems thinking into chemistry-based disciplines so that the most sustainable, least toxic, and least resource-intensive research directions and methods are prioritized. The next generation of researchers and instructors is poised to implement these approaches; however, most graduate curricula do not include coursework on green chemistry and systems thinking. Every year, the American Chemical Society Green Chemistry Institute hosts the Summer School on Green Chemistry & Sustainable Energy for early career researchers to learn about green chemistry and systems thinking approaches for tackling sustainability goals. In this Perspective, 2022 summer school participants highlight sustainability challenges in their own work that can be addressed using the skills and knowledge acquired at the summer school, including in carbon capture, organic pharmaceutical synthesis, nanomaterial synthesis, catalysis, and other areas. In addition, how green chemistry can meet practical needs in industry settings and be infused in education and government policy is discussed.
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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 | 0.017 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".