Low Voltage-Enhanced Mechano-Bactericidal Biopatch
Mechano-bactericidal strategies represent a safe and sustainable method for preventing microbial contamination in the postantibiotic era. However, their effectiveness against Gram-positive bacteria (≤55%) is still limited due to the thick peptidoglycan layer in their cell walls. Herein, an intelligent biomimetic nanopillared biopatch is developed. It is assisted by low-voltage (8 V) electrical stimulation from TENG and significantly enhances antibacterial efficacy (>99%) against three types of stubborn Gram-positive bacteria. These collaborative antibacterial behaviors are solely based on purely physical actions, thus avoiding the risk of triggering bacterial resistance. Moreover, the slight mechanical energy generated by human physiological activities is converted into a power source, exhibiting energy-efficient, eco-friendly, and sustainable features. The conductive hydrogel in the biopatch can also act as an intelligent temperature sensor, monitoring, and real-time assessment of wound conditions. This intelligent biopatch holds immense potential for efficient healing and safe management of both acute and chronic wound infections.
Authors
- Lizhi Xu (ORCID: https://orcid.org/0000-0001-6888-0524)
- Shichao Niu (ORCID: https://orcid.org/0000-0003-0208-9996)
- Jie Zhao (ORCID: https://orcid.org/0000-0003-0518-4155)
- Yaozhen Yi
- Changchao Zhang (ORCID: https://orcid.org/0000-0002-2763-9918)
- Chaozong Liu (ORCID: https://orcid.org/0000-0002-9854-4043)
- Haixu Dou
- Yuxiang Chen (ORCID: https://orcid.org/0000-0002-5059-2150)
- Luquan Ren (ORCID: https://orcid.org/0000-0003-1102-8482)
- Zhiwu Han (ORCID: https://orcid.org/0000-0003-1069-7035)
- Ziting Liu
- Shuilin Wu (ORCID: https://orcid.org/0000-0002-1270-1870)
Institutions
- Jilin University (CN)
- Peking University (CN)
- Royal National Orthopaedic Hospital (GB)
- Liaoning Academy of Materials
- University College London (GB)
- University of Hong Kong (HK)
Publication Details
- Journal
- Nano Letters
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1021/acs.nanolett.4c04777
- Citations
- 8
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 2.15