Antimicrobial Activity of 3D-Printed Acrylonitrile Butadiene Styrene (ABS) Polymer-Coated with Silver Nanoparticles
Bibliographic record
Abstract
Medical devices with antimicrobial properties are a potential long-term solution to the high rate of multi-drug-resistant healthcare-associated infections. Silver nanoparticles (AgNPs) are an established agent for effectively eliminating a wide range of microbial strains. AgNPs have been commonly incorporated into traditional plastic materials; however, recently, there has been increased interest in using AgNPs combined with 3D-printing technology for medical devices due to the accessibility and customizability of 3D-printed products. This study reports a novel method of utilizing acetone to partially dissolve 3D-printed polymer acrylonitrile butadiene styrene (ABS) plastic to attach a layer of AgNPs. The antimicrobial properties of this AgNP-coated surface were tested against several microbial strains prevalent in healthcare-associated infections. AgNP-coated ABS (AgNP-ABS) plastic demonstrated significant elimination of viable bacteria within 4 h for all tested bacterial species (Acinetobacter baumannii, non-pathogenic and pathogenic Escherichia coli, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus aureus) and within 19 h for the tested fungus Candida albicans. The longevity of adhesion of AgNPs to the ABS plastic was assessed by checking antibacterial activity against A. baumannii after repeat use cycles. AgNP-ABS plastic showed decreased antibacterial efficacy with repeated use but maintained the ability to eliminate microbes within 3 h for up to eight use cycles. The AgNP-coated ABS plastic showed efficacy as an antimicrobial surface, and future studies will consider its applicability in the production of medical devices.
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How this classification was reachedexpand
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) | 0.003 | 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 teacher head, 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".