Self-Cleaning Ceramic Tiles Produced via Stable Coating of TiO2 Nanoparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it