Generalized Routes to Mesostructured Silicates with High Metal Content
Bibliographic record
Abstract
Abstract Nanostructured materials with high metal content are interesting for a number of applications, including catalysis as well as energy conversion and storage. Here we elaborate an approach that combines the advantages of simple silica sol-gel chemistry with the ability to tailor metal composition and structure by introducing a ligand that connects a silane with an amino acid or hydroxy acid. Reacting this ligand with a metal acetate generates a precursor for a range of metal-silica nanocomposites. Comparing this chemistry with conventional organic ligand-metal complexes that can be physically mixed into sol-gel derived silicates elucidates advantages, e.g. of going to high metal loadings. Resulting nanomaterials are characterized by a combination of small-angle X-ray scattering (SAXS), transmission electron microscopy (TEM), and solid-state nuclear magnetic resonance (NMR) to reveal structural characteristics on multiple lengths scales, i.e. from the microscopic (molecular) level (NMR) all the way to the mesoscale (SAXS) and macroscale (TEM).
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".