Chemical Synthesis of Innovative Silver Nanohybrids with Synergistically Improved Antimicrobial Properties
Bibliographic record
Abstract
Jianhua Yan,1 Qifei Wang,1,2 Junlin Yang,2 Paige Rutter,1 Malcolm Xing,3 Bingyun Li1 1Department of Orthopaedics, School of Medicine, West Virginia University, Morgantown, WV, 26506, USA; 2Spine Center, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 3Department of Mechanical Engineering, University of Manitoba, Winnipeg, R3T2N2, CanadaCorrespondence: Bingyun Li, Department of Orthopaedics, School of Medicine, West Virginia University, 64 Medical Center Drive, Morgantown, WV, 26506, USA, Tel +1 681-285-5956 ; +1 304-293-1075, Fax +1 304-293-7070, Email bili@hsc.wvu.eduBackground: The wide use of antibiotics has created challenges related to antibiotic-resistant bacteria, which have been increasingly found in recent decades. Antibiotic resistance has led to limited choices of antibiotics. Multiple old antimicrobial agents have high antimicrobial properties toward bacteria, but they unfortunately also possess high toxicity toward humans. For instance, silver (Ag) compounds were frequently used to treat tetanus and rheumatism in the 19th century and to treat colds and gonorrhea in the early 20th century. However, the high toxicity of Ag has limited its clinical use.Purpose: We aimed to reformulate Ag to reduce its toxicity toward human cells like osteoblasts and to optimize its antimicrobial properties.Results: Ag, an old antimicrobial agent, was reformulated by hybriding nanomaterials of different dimensions, and silver nanoparticles (AgNPs) of controllable sizes (95– 200 nm) and varying shapes (cube, snowflake, and sphere) were synthesized on carbon nanotubes (CNTs). The obtained AgNP-CNT nanohybrids presented significantly higher killing efficacy against Staphylococcus aureus (S. aureus) compared to AgNPs at the same molar concentration and showed synergism in killing S. aureus at 0.2 and 0.4 mM. AgNPs presented significant osteoblast toxicity; in contrast, AgNP-CNT nanohybrids demonstrated significantly enhanced osteoblast viability at 0.04– 0.8 mM. The killing of S. aureus by AgNP-CNT nanohybrids was fast, occurring within 15 min.Conclusion: Ag was successfully reformulated and Ag nanohybrids with various AgNP shapes on CNTs were synthesized. The nanohybrids presented significantly enhanced antimicrobial properties and significantly higher osteoblast cell viability compared to AgNPs, showing promise as an innovative antimicrobial nanomaterial for a broad range of biomedical applications.Keywords: silver nanoparticle, nanohybrid, antimicrobial, cytotoxicity, reformulation, antibiotic resistance
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
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".