Structural role of polyethylene glycol in the formation of anatase nanocrystalline titania at low temperature
Bibliographic record
Abstract
Abstract Anatase nanocrystalline TiO2 thin films were obtained by a sol–gel dip‐coating method, in which the nanocrystallization is effected by a simple hot water treatment of the deposited films at temperatures below 90 °C under atmospheric pressure for 1 h. The dip‐coating sol was prepared by reacting titanium tetra‐n‐butoxide [Ti(OnBu)4] with polyethylene glycol (PEG) in ethanol. Films obtained from a sol that do not contain PEG show no sign of crystallization, demonstrating the importance of PEG in the crystallization process. Raman studies of reaction dynamics show that PEG undergoes a nucleophilic substitution reaction replacing butoxy groups in Ti(OnBu)4. Stoichiometric reactions of Ti(OnBu)4 with PEG in polar and nonpolar solvents were performed, and they yielded different titanium–PEG hybrid polymers, which were isolated and characterized by various spectroscopic techniques such as IR, Raman, solid‐state NMR and MALDI‐TOF‐MS. NMR studies evidenced the location and the way in which PEG is bonded with titanium atoms in the titanium–PEG hybrid polymers. On the basis of these studies, we have proposed structures for these polymers. It is demonstrated that the structure of the obtained polymers plays an important role in the formation of anatase TiO2 nanoparticles in hot water at temperatures below 90 °C under atmospheric pressure. Copyright © 2009 John Wiley & Sons, Ltd.
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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".