Axial suspension plasma sprayed Ag-TiO2 coating for enhanced photocatalytic and antimicrobial properties
Bibliographic record
Abstract
Ag-TiO2 coating has been extensively studied for its antimicrobial and photocatalytic applications. In this study, we developed a straightforward and efficient method with use of Axial Suspension Plasma Spray (ASPS) to deposit Ag-TiO2 coatings. In ASPS, a suspension containing AgNO3 solution and TiO2 particles (pure anatase) was prepared and plasma sprayed on SS304 stainless steel substrate to form the Ag-TiO2 coating. The microstructure, photocatalytic activity, and antibacterial property against E-coli (CFT073 and C1214) and Methicillin-resistant Staphylococcus aureus (MSRA, USA300) of the deposited coating were characterized. Results showed the deposited Ag-TiO2 is a composite coating consisting of anatase, rutile and Ag nanoparticles. The introduction of a small amount of Ag (up to 0.2at% of Ag/Ti in the coating) exhibited a significant increase in photocatalytic activity, achieving a rate at least twice as fast as that of the TiO2 coating without Ag in the methyl orange degradation test. Moreover, these Ag-TiO2 coatings demonstrated remarkable antimicrobial activity, completely inactivating E. coli CFT073, effectively inhibiting the growth of E. coli C1214 by four orders of magnitude and MRSA by nearly three orders of magnitude. The employment of ASPS, in conjunction with AgNO3 solution, presents a simple and effective approach for the production of nano Ag particles in a composite coating. This technique has a great potential in surface modification on medical devices for anti-infective purpose.
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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".