Inkjet Printing of Silver Nanoparticle-bound Biomaterials on CottonFabric to Prevent Antimicrobial Resistance
Bibliographic record
Abstract
The emergence of multi-resistant bacteria, untreatable with conventional medicines, is a significant global health concern. This study proposes a unique solution to this problem by digitally inkjet printing biomaterials bound with silver nanoparticles (NP) on cotton textiles. The silver nanoparticles, known for their effective antimicrobial properties, are stabilized, and made biocompatible by the enzymes. The use of digital inkjet printing allows for precise application of these NP-biomaterial conjugates, ensuring uniform coverage and optimal performance. This approach aims to prevent the spread of antimicrobial-resistant bacteria through cotton textiles in medical care environments, enhancing patient safety. The inkjet printing technology used in this study offers high-resolution patterning, enabling the creation of complex designs with multiple materials. This flexibility allows for the development of textiles with varying antimicrobial properties, tailored to specific applications in the medical field. Furthermore, the use of cotton, a natural and breathable material, ensures the comfort and safety of patients, making it an ideal choice for this application.
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) | 0.000 | 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".