Yolk–Shell Hybrid Materials with a Periodic Mesoporous Organosilica Shell: Ideal Nanoreactors for Selective Alcohol Oxidation
Bibliographic record
Abstract
Abstract This contribution describes the preparation of multifunctional yolk–shell nanoparticles (YSNs) consisting of a core of silica spheres and an outer shell based on periodic mesoporous organosilica (PMO) with perpendicularly aligned mesoporous channels. The new yolk–shell hybrid materials were synthesised through a dual mesophase and vesicle soft templating method. The mesostructure of the shell, the dimension of the hollow space (4∼52 nm), and the shell thickness (16∼34 nm) could be adjusted by precise tuning of the synthesis parameters, as evidenced by X‐ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and nitrogen sorption investigations. Various metal nanoparticles (e.g., Au, Pt, and Pd) were encapsulated and confined in the void space between the core and the shell using impregnation and reduction of adequate metal precursors. The selective oxidation of various alcohol substrates was then carried out to illustrate the benefits of such an architecture in catalysis. High conversion (∼100%) and excellent selectivity (∼99%) were obtained over Pd nanoparticles encapsulated in the hybrid PMO yolk–shell structures.
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) | 0.000 | 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".