ConfChem Conference on Educating the Next Generation: Green and Sustainable Chemistry—Greening the Organic Curriculum: Development of an Undergraduate Catalytic Chemistry Course
Bibliographic record
Abstract
Catalysis continues to revolutionize the science and art of organic chemistry, with the 2010, 2005, and 2001 Chemistry Nobel Prizes awarded for developments in new metal-catalyzed reactions. Using catalytic strategies is additionally one of the 12 principles of green chemistry. It is therefore essential that undergraduate students learning organic synthesis be exposed to modern catalytic approaches from both a theoretical and practical perspective. Described here is the creation of a third-year laboratory-intensive undergraduate course entitled Organic Synthesis Techniques, and its focus on teaching green and sustainable chemistry principles. The course showcases seven cutting-edge catalytic methodologies including phase-transfer catalysis, organocatalysis, Lewis and Brønsted acid catalysis, and transition-metal catalysis. Associated lectures are devoted to discussion of green chemical principles and practice with relevant industrial case studies, laboratory techniques undertaken, operative catalytic mechanisms, and instrumental methods of analysis. This communication summarizes one of the invited papers to the ConfChem online conference Educating the Next Generation: Green and Sustainable Chemistry, held from May 7 to June 30, 2010 and hosted by the ACS DivCHED Committee on Computers in Chemical Education (CCCE).
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".