Zr Doping on One-Dimensional Titania Nanomaterials Synthesized in Supercritical Carbon Dioxide
Bibliographic record
Abstract
The growth mechanism of one-dimensional metal oxide nanotubular structures is of tremendous current interest to tailor materials using "green" synthetic procedures for emerging industries in alternative energy and biomaterials. In this study, ZrO(2)-modified TiO(2) nanorods and tubular structures were successfully synthesized via a surfactant-free sol-gel route using supercritical carbon dioxide (scCO(2)) as the solvent/drying agent. The effect of metal alkoxide concentration (0.35-1.4 mol/L), acid/metal alkoxide ratio (R = 3-7), and Zr ratio (0-20%) was examined on the morphology and crystallinity of the resulting nanostructures as measured by electron microscopy (SEM and TEM), EDX, XPS, and XRD. The electron microscopy results showed that the crystal growth of the synthesized binary Ti-Zr nanomaterials could be tailored by changing the operating variables with nanotubular structure formed at metal alkoxide concentration of 1.2 mol/L, R = 5-6, and Zr ratio between 4% and 20%. Gelation kinetics for this new system was also studied and revealed that increasing alkoxide concentration and R value enhanced the gelation kinetics. In situ and powder FTIR results revealed that this Ti-Zr binary system follows a similar reaction scheme to that of either single-component system, showing the flexibility of this approach for tailoring nanotubular production.
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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".