Nanocast mesoporous mixed metal oxides for catalytic applications
Bibliographic record
Abstract
Since the initial discovery of ordered mesoporous silica in early 1990s, considerable innovations were achieved regarding their synthesis, characterization and applications. One of the best outcomes of these intense research efforts is the development of a solid templating method called “nanocasting”, which is based on using mesoporous silica (or carbon) as a rigid template. This solid-to-solid replication method opened the pathway for synthesizing high surface area non-silica mesostructured materials that are challenging to obtain through conventional self-assembly processes which are based on amphiphilic soft structure-directing agents. In particular, the replicated metal oxide mesostructures obtained by this method were found to be highly versatile for a wide range of applications, especially in catalysis, owing to their large specific surface area. Furthermore, the nanocasting method is particularly suited for the synthesis of mixed metal compositions, favored by the possible confinement of mixed precursors in the nanopores of the template. In this account, we discuss some of the recent developments regarding the synthesis of nanocast mixed metal oxides and their perspectives of catalytic applications. It is here the choice of the authors to place emphasis on a few representative examples of compositions (e.g., non-noble metal-based catalysts, perovskites) and catalytic reactions (e.g., hydrogen production, gas-phase oxidation).
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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) | 0.003 | 0.002 |
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".