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A self‐powered biosensor based on triboelectric nanogenerator for dual‐specificity bacterial detection.
- Source :
- InfoMat; Mar2024, Vol. 6 Issue 3, p1-14, 14p
- Publication Year :
- 2024
-
Abstract
- Pathogenic and corrosive bacteria pose a significant risk to human health or economic well‐being. The specific, sensitive, and on‐site detection of these bacteria is thus of paramount significance but remains challenging. Taking inspiration from immunoassays with primary and secondary antibodies, we describe here a rational design of microbial sensor (MS) under a dual‐specificity recognition strategy using Pseudomonas aeruginosa (P. aeruginosa) as the detection model. In the MS, engineered aptamers are served as the primary recognition element, while polydopamine‐N‐acetyl‐D‐galactosamine (PDA‐Gal NAc) nanoparticles are employed as the secondary recognition element, which will also generate and amplify changes in the output voltage signal. To achieve self‐powering capability, the MS is constructed based on a triboelectric nanogenerator (TENG) with the specific aptamers immobilized on the TENG electrode surface. The as‐prepared MS‐TENG system exhibits good stability in output performance under external forces, and high specificity toward P. aeruginosa, with no cross‐reactivity observed. A linear relationship (R2 = 0.995) between the output voltage and P. aeruginosa concentration is established, with a limit of detection estimated at around 8.7 × 103 CFU mL−1. The utilization of PDA‐Gal NAc nanoparticles is found to play an important role in enhancing the specific and reliability of detection, and the underlying mechanisms are further clarified by computational simulations. In addition, the MS‐TENG integrates a wireless communication module, enabling real‐time monitoring of bacterial concentration on mobile devices. This work introduces a pioneering approach to designing self‐powered smart microbial sensors with high specificity, using a double recognition strategy applicable to various bacteria beyond P. aeruginosa. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 25673165
- Volume :
- 6
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- InfoMat
- Publication Type :
- Academic Journal
- Accession number :
- 176212105
- Full Text :
- https://doi.org/10.1002/inf2.12508