In Situ Hydrogen Peroxide Production for Selective Oxidation of Benzyl Alcohol over a Pd@Hierarchical Titanium Silicalite Catalyst
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Using in situ generated H 2 O 2 is potentially an effective approach for benzyl alcohol selective oxidation. While the microporous titanium silicate (TS-1) supported with Pd is promising for selective oxidation, the Pd particles are preferentially anchored on the external surface, which leads to the problems such as non-uniform dispersion and low thermal stability. Here, we prepared a [email protected] catalyst in which the Pd subnanoparticles were encapsulated in the channels of the hierarchical TS-1 (HTS-1), for benzyl alcohol selective oxidation with in situ produced H 2 O 2 . We find that the oxidation rate of benzyl alcohol by in situ H 2 O 2 over the [email protected] is up to 4268.8 mmol h –1 kg cat –1, and the selectivity of benzaldehyde approaches 100%. In contrast to the conventional Pd/HTS-1, the present [email protected] benefits the benzyl alcohol selective oxidation due to the increased dispersion of Pd particles (forming uniformly dispersed subnano-sized particles), as well as the confinement effect and hierarchical porosity of the HTS-1 host. We further suggested that hydrogen peroxide produced in situ from the molecular hydrogen and oxygen over the Pd sites can be spilled over to the framework Ti 4+ sites, forming the Ti-OOH active species, which selectively oxidizes the chemisorbed benzyl alcohol to benzaldehyde on the Pd sites.
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".