Synthesis of Gold Catalysts Supported on Mesoporous Silica Materials: Recent Developments
Bibliographic record
Abstract
Mesoporous silica materials (MSM) with ordered and controllable porous structure, high surface area, pore volume and thermal stability are very suitable catalyst supports, because they provide high dispersion of metal nanoparticles and facilitate the access of the substrates to the active sites. Since the conventional wet-impregnation and deposition-precipitation methods are not appropriate for the incorporation of gold nanoparticles (AuNPs) into MSM, considerable efforts have been made to develop suitable methods to synthesize Au/MSM catalysts, because the incorporation of AuNPs into the channel system can prevent their agglomeration and leaching. In this review, we summarize the main methods to synthesize active gold catalysts supported on MSM. Examples and details of the preparative methods, as well as selected applications are provided. We expect this article to be interesting to researchers due to the wide variety of chemical reactions that can be catalyzed by gold supported catalysts.
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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) | 0.001 | 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".