Metal Nanoparticle Impregnated Controlled-size Silica Macrospheres as a Microwave-transparent Catalyst System for MACOS
Bibliographic record
Abstract
Background: Metal films in microwave-assisted, continuous-flow organic synthesis (MACOS) have shown to act as heterogeneous catalysts for a variety of reactions, but have difficulty due to difficult to control heating and occurrence of laminar flow which limits the contact of the reagents with the catalyst surface. The aim of this paper is to describe a microwave-transparent supported metal catalyst with high surface area and its use in MACOS. Methods: Millimeter sized, monodisperse, macroscopic spherical silica beads loaded with Ni, Cu, and Pd nanoparticles were prepared through use of a single-step emulsion procedure via a sol-gel process and used to perform Heck cross-coupling reactions in MACOS. Results: The size of the spheres was readily controlled to a maximum diameter of 1300 m by varying the stirring rate of the emulsion mixture. Pd loadings of up to 4.3 wt.% were obtained, and confirmed to be present as nanoparticles through PXRD spectroscopy and TEM imaging. The metal-loaded spheres were found to be essentially microwave-transparent, allowing for use as catalytic beads in microwave flow reactors. In addition, no mechanical dislodgement of the nanoparticles or degradation of their catalytic activity was observed over repeated usage. Conclusion: Metal-nanoparticle-impregnated silica macrospheres were found to be an effective catalyst for use in MACOS by providing access to the use of heterogeneous metal catalysts with controllable heating. Further testing of various metals and reactions can be performed to increase the scope of possible reactions to be catalysed in MACOS using metal-impregnated macrospheres as catalysts. Keywords: Catalyst, flow chemistry, MACOS, microwave, nanoparticle.
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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) | 0.000 | 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".