Mechanisms of Phase Transformations of TiO<sub>2</sub> Nanotubes and Nanorods
Bibliographic record
Abstract
Phase transformations of titanium dioxide (TiO 2 ) nanotubes and nanorods at elevated temperatures are studied using molecular dynamics (MD) and replica exchange molecular dynamics (REMD) utilized here in the same way as simulated annealing. In the study, TiO 2 nanotubes with amorphous (amT) and anatase structures (anT) as well as TiO 2 amorphous nanorods (amR) that are amenable to experimental investigation are considered at various temperatures. It is found that amT and amR transform into a rutile rod, while anT transforms into a brookite nanotube. It is demonstrated that transformation of anT starts from TiO 4 and TiO 5 complexes found in the surfaces of the system, in contrast to amT and amR, where initial grains of the new phase may develop throughout the entire system starting from TiO 5 and TiO 6 complexes. The evolution of the number of TiO x ( x = 4, 5, 6, 7) complexes indicates that the transformation of amT and amR occurs almost suddenly relative to the transformation of anT. The initial grains of transformation of amT have a structure close to rutile, while those of anT have brookite features. To our knowledge, we report the first simulations of phase transformations of TiO 2 nanotubes and nanorods where simulations are performed beyond μs.
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) | 0.002 | 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".