Solventless Acid‐Free Synthesis of Mesostructured Titania: Nanovessels for Metal Complexes and Metal Nanoclusters
Bibliographic record
Abstract
Abstract A new and highly reproducible method to obtain mesostructured titania materials is introduced in this contribution. The mesostructured titania is obtained by employing self‐assembled structures of non‐ionic alkyl‐poly(ethylene oxide) surfactants as templates. The materials are produced without additional solvents such as alcohols, or even water. Only the titanium(IV) ethoxide and the surfactant (C12EO10) are needed. Water, in the form of that attached to the surfactant and from the atmosphere, induces growth of titania nanoclusters in the synthesis sol. It is indicated that these nanoclusters interact with the surfactant EO‐head groups to form a new titanotropic amphiphile. The new amphiphiles self‐assemble into titanium nanocluster–surfactant hybrid lyotropic phases, which are transformed to the final mesostructured materials by further condensation of the titania network. The titania materials can be obtained also with noble‐metal particles immobilized in the mesostructured framework. It is seen that when different metal salts are used as the metal precursors, different interactions with the titania walls are found. The materials are characterized by X‐ray diffraction (XRD), polarization optical microscopy (POM), transmission electron microscopy (TEM), UV‐vis spectroscopy, and micro‐Raman analysis.
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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 | 0.001 |
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".