Self-Cleaning Ceramic Tiles Produced via Stable Coating of TiO2 Nanoparticles
Bibliographic record
Abstract
The high photocatalytic power of TiO2 nanoparticles has drawn great attention in environmental and medical applications. Coating surfaces with these particles enables us to benefit from self-cleaning properties and decomposition of pollutants. In this paper, two strategies have been introduced to coat ceramic tiles with TiO2 nanoparticles, and the self-cleaning effect of the surfaces on degradation of an organic dye under ultraviolent (UV) exposure is investigated. In the first approach, a simple one-step heat treatment method is introduced for coating, and different parameters of the heat treatment process are examined. In the second method, TiO2 nanoparticles are first aminosilanized using (3-Aminopropyl)triethoxysilane (APTES) treatment followed by their covalently attachment onto CO2 plasma treated ceramic tiles via N-(3-Dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride (EDC) and N-Hydroxysuccinimide (NHS) chemistry. We monitor TiO2 nanoparticle sizes throughout the coating process using dynamic light scattering (DLS) and characterize developed surfaces using X-ray photoelectron spectroscopy (XPS). Moreover, hydrophilicity of the coated surfaces is quantified using a contact angle measurement. It is shown that applying a one-step heat treatment process with the optimum temperature of 200 °C for 5 h results in successful coating of nanoparticles and rapid degradation of dye in a short time. In the second strategy, the APTES treatment creates a stable covalent coating, while the photocatalytic capability of the particles is preserved. The results show that coated ceramic tiles are capable of fully degrading the added dyes under UV exposure in less than 24 h.
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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 | 0.001 |
| 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) | 0.000 | 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".