Hydrothermal growth: Influence of Process parameters to design TiO2 nanostructures
Bibliographic record
Abstract
Over the past several decades, numerous research activities have been focused on fabrication of titanium oxide (TiO2) films owing to their broad applications in various fields such as medicine [1] (cancer treatment, antimicrobial), energy [2] (water splitting, photovoltaic), environment (air and water purification), gas sensors, photocatalysis, and self-cleaning [3].Currently, various deposition methods have been employed to develop the TiO2 including sol gel [4], solvothermal, chemical vapor deposition, thermal oxidation and hydrothermal method [5].When specifically compared to other methods, the hydrothermal method has many advantages: (i) the required equipment and processing conditions is easier, (ii) during crystallization processes, growing crystals/crystallites tend to reject impurities present in the growth environment, (iii) by changing the hydrothermal conditions (such as titanium precursor concentration, reaction time, reaction temperature, additives, substrate orientation, and pH of growth solution), crystalline products could be easily modified with different compositions, morphologies and structures.However, slight variations in these parameters result in significant alterations of the properties of TiO2.Herein, we report the formation chemistry, growth mechanism of TiO2 nanostructures in the surface of FTO substrate.The effects of key hydrothermal experimental conditions have been discussed to understand the different obtained morphologies.Indeed, XRD and Raman analysis confirmed the formation of the rutile phase of TiO2.Morphological studies showed that we can obtain nanorods with a controlled sizes, between 0.3 and 3.2 µm of length, and the presence of seed layers on FTO allows to have a denser surface with vertical orientation of NRs of TiO2.Finally, we demonstrated that a specific position of the substrate can lead to nanoflowers formation.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".