Sol–gel synthesis of linear Sn-doped TiO<sub>2</sub>nanostructures
Bibliographic record
Abstract
The sol–gel synthesis of linear Sn-doped TiO2 (TDT) nanostructures with high aspect ratios is reported. These binary metal-oxide nanostructures are readily accessed by treating titanium isopropoxide (Ti(OiPr)4) with appropriate quantities of acetic acid (AcOH) and Sn(OAc)4 in heptanes to generate linear macromolecules that form nanofibers upon calcination. While these linear nanostructures are isolated at low R values (0.05–0.20), where R is defined as the molar ratio of Sn(OAc)4 to Ti(OiPr)4, axially directional growth is not favored at higher R values (0.30–0.50). Scanning and transmission electron microscope (SEM and TEM) imaging of the nanofibers revealed diameters in the 10–20 nm range and lengths in excess of 1 mm. Elemental mapping by STEM-EDS techniques indicates a homogeneous distribution of Sn ions throughout the linear TDT structures. The molecular intermediates that form during the early stages of the reaction were monitored using electrospray-ionization mass spectrometry to confirm the presence of metal-oxide clusters containing both Sn and Ti ions. These intermediates then undergo polycondensation reactions to form the final linear product, thereby indicating the homogeneous incorporation of Sn into the TiO2 lattice and rules out the possibility of independent SnO2 and TiO2 aggregates. Powder X-ray diffraction data indicate that pure TiO2 nanostructures are anatase when calcined at 500 °C, but show a propensity to adopt the rutile phase at progressively higher Sn concentrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".