A Comparative Study of Solvent-Free and Highly Efficient Pinene Hydrogenation over Pd on Carbon, Alumina, and Silica Supports
Bibliographic record
Abstract
Solvent-free catalytic hydrogenation of pinene over Pd on carbon, alumina, and silica supports were compared. Despite the absence of solvent, Pd catalyst on all three supports yields to a quantitative conversion of pinene to pinanes and a higher selectivity for the cis isomer (72–89%). The temperature affected similarly the selectivity of the reaction for all three catalysts in favor of the cis -pinane isomer. However, recycling of the Pd on the three tested supports varied greatly. If Pd/C and Pd/alumina were successfully recycled 13 and 14 times, respectively, Pd/silica could be used only once to convert pinenes into pinanes. Remaining very effective during all 13 cycles, Pd/C was the best catalyst/support of the three tested ones for the solvent-free hydrogenation of pinene. The influence of H 2 pressure on pinene conversion rates were investigated for Pd/C catalyst. An extremely low leaching rate of palladium in pinenes and pinanes determined by ICP/MS confirmed the heterogeneous nature of this catalytic solvent-free reaction. The hydrogenation reaction presented in this paper is an alternate environmentally friendly pathway for pinane synthesis.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".