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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".