Green Hydrogenation: Solvent-Free Hydrogenation of Pinenes for An Undergraduate Organic Chemistry Laboratory
Bibliographic record
Abstract
Abstract Reorienting organic chemistry laboratories with an emphasis on systemic thinking can only be beneficial to students. Addressing the challenges of climate change and the development of renewable resources with a fundamental reaction, such as the hydrogenation of an alkene, will allow students to see organic chemistry as a science beneficial to society. For this purpose, a green organic laboratory experiment in which students performed solvent-free catalytic hydrogenation of β- or α-pinene was developed. Using a homemade high- or low-pressure autoclave, solvent-free hydrogenation of β- or α-pinene over Pd/C produced a mixture of cis- and trans-pinanes. The monitoring of the reaction by NMR, IR, and GC-MS allowed the students to be familiar with the isomerization of an alkene and the shift of an equilibrium according to Le Chatelier’s principle. The filtration of the catalyst near the halftime of reaction and the continuation of the hydrogenation off catalyst allowed the students to confirm the heterogeneous nature of the hydrogenation.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".