Fabrication of Nano-Silver–Silver Ion Composite Antibacterial Agents for Green Powder Coatings
Bibliographic record
Abstract
Nano-silver is characterized by broad-spectral, strong and stable antibacterial properties, which make it a promising material in coating applications. However, the efficiency of nano-silver is generally low in the coating of films. Here, we developed a series of highly active and durable silver ions–nano-silver antimicrobial agents for powder coatings. To optimize antimicrobial activity and durability, two different nano-silver generation methods, i.e., in situ and ex situ methods combined with different carrier materials, i.e., zeolite with high ion-exchange ability and montmorillonite of layered structure were adopted and investigated. All four antibacterial additives show high activity with a reduction rate of over 99.99% and R value of over 5. The ex situ generated nano-silver antibacterial agents with both carriers exhibit higher activity in the initial antibacterial property and antibacterial durability that the coating films are able to maintain over 99% antimicrobial reduction after 20 cycles (1200 times) of soap solution wiping. They also show a lower yellowish color difference of less than three compared to the films with in situ generation method. The one with montmorillonite as carrier shows the stronger antibacterial property with an R value of 5.88 and slightly better film appearance of lower color difference, smaller reduction in gloss and increase in haze as compared to zeolite carrier due to the layered structure.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".