Chemoselective Hydrogenation with Supported Organoplatinum(IV) Catalyst on Zn(II)-Modified Silica
Bibliographic record
Abstract
Abstract Well-defined organoplatinum(IV) sites were grafted on a Zn(II)-modified SiO2 support via surface organometallic chemistry in toluene at room temperature. Solid-state spectroscopies including XAS, DRIFTS, DRUV–vis, and solid-state (SS) NMR enhanced by dynamic nuclear polarization (DNP), as well as TPR-H2 and TEM techniques revealed highly dispersed (methylcyclopentadienyl)methylplatinum(IV) sites on the surface ((MeCp)PtMe/Zn/SiO2, 1). In addition, computational modeling suggests that the surface reaction of (MeCp)PtMe3 with Zn(II)-modified SiO2 support is thermodynamically favorable (ΔG = −12.4 kcal/mol), likely due to the increased acidity of the hydroxyl group, as indicated by NH3-TPD and DNP-enhanced 17O{1H} SSNMR. In situ DRIFTS and XAS hydrogenation experiments reveal the probable formation of a surface Pt(IV)-H upon hydrogenolysis of Pt-Me groups. The heterogenized organoplatinum(IV)-hydride sites catalyze the selective partial hydrogenation of 1,3-butadiene to butenes (up to 95%) and the reduction of nitrobenzene derivatives to anilines (up to 99%) with excellent tolerance of reduction-sensitive functional groups (olefin, carbonyl, nitrile, halogens) under mild reaction conditions.
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".