Simple Protocol for Generating TiO<sub>2</sub> Nanofibers in Organic Media
Bibliographic record
Abstract
Titania nanofibers in high yields can be accessed by treating titanium isopropoxide (Ti(O i Pr) 4 ) with acetic acid (AcOH) in heptanes when R ≥ 4.2, where R = mol of AcOH/mol of Ti(O i Pr) 4 . Electron microscopic (SEM and TEM) images of the samples confirmed that high-aspect-ratio nanofibers with diameters in the 10−20 nm range are produced under these conditions, whereas agglomerated spherical nanoparticles are produced at R ≤ 3.8. Powder X-ray diffraction and UV-vis data confirm the anatase crystalline phase after calcination at 400 °C, with the progressive formation of the rutile phase upon heating to higher temperatures. N 2 physisorption analyses showed the calcined fibers prepared at R = 5.5 have surface areas of 95 m 2 /g. The self-assembly pathway leading to the nanofibers was delineated by in situ attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy in tandem with electrospray ionization mass spectrometry (ESI-MS). It was found that the hexanuclear building block, Ti 6 O 6 (OAc) 6 (O i Pr) 6 ( TAC1 ), is formed during the initial stages of the reactions, and that the axially ligated isopropoxide ligands of this complex are subsequently hydrolyzed to facilitate the one-dimensional condensation of the macromolecules at R ≥ 4.2. Incomplete hydrolysis at lower acid ratios impedes this axial growth, resulting in spherical nanoparticles.
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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 | 0.000 |
| 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; 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".