Template assisted preparation of high surface area macroporous supports with uniform and tunable nanocrystal loadings
Bibliographic record
Abstract
The incorporation of catalytic nanocrystals into macroporous support materials has been very attractive due to their increased catalyst mass activity. This increase in catalytic efficiency is attributed in part to the increased surface area to volume ratio of the catalysts and the use of complementary support materials that can enhance their catalytic activity and stability. A uniform and tunable coating of nanocrystals on porous matrices can be difficult to achieve with some techniques such as electrodeposition. More sophisticated techniques for preparing uniform nanocrystal coatings include atomic layer deposition, but it can be difficult to reproduce these processes at commercial scales required for preparing catalyst materials. In this study, catalytic nanocrystals supported on three dimensional (3D) porous structures were prepared. The demonstrated technique utilized scalable approaches for achieving a uniform surface coverage of catalysts through the use of polymeric sacrificial templates. This template assisted technique was demonstrated with a good control over the surface coverage of catalysts, support material composition, and porosities of the support material. A series of regular porous supports were each prepared with a uniform coating of nanocrystals, such as NaYF4 nanocrystals supported by a porous 3D lattice of Ti1-xSixO2, Pt nanocrystals on a 3D porous support of TiO2, Pd nanocrystals on Ni nanobowls, and Pt nanocrystals on 3D assemblies of Au/TiO2 nanobowls. The template assisted preparation of high surface area macroporous supports could be further utilized for optimizing the use of catalytic materials in chemical, electrochemical, and photochemical reactions through increasing their catalytic efficiency and stability.
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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".