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Optical Detection of E. coli Bacteria by Mesoporous Silicon Biosensors
Published on: November 20, 2013
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Indirect Detection of Bacteria on Optically Enhanced Porous Silicon Membrane-Based Biosensors Using Selective Lytic
Roselien Vercauteren1, Audrey Leprince2, Manon Nuytten2
1Institute for Information and Communication Technologies, Electronics and Applied Mathematics, UCLouvain, 1348 Louvain-la-Neuve, Belgium.
ACS Sensors
|July 6, 2023
Summary
This study presents a novel biosensor for detecting bacteria indirectly using bacterial lysate. The innovative porous silicon sensor achieves high sensitivity and selectivity for *Bacillus cereus* detection in under two hours.
Area of Science:
- Biosensor technology
- Nanomaterials
- Microbiology
Background:
- Traditional biosensors often rely on surface-bound bio-probes for selectivity.
- Porous silicon (PSi) offers unique optical and physical properties for sensing applications.
- Bacterial detection requires sensitive and selective methods, especially in complex samples.
Purpose of the Study:
- To develop a novel biosensor for the indirect detection of bacteria via their lysate.
- To enhance selectivity by incorporating lytic enzymes directly into the assay, rather than relying on surface bio-probes.
- To improve the sensitivity and reduce assay time for bacterial detection.
Main Methods:
- Fabrication of porous silicon membranes using standard microfabrication techniques.
- Coating of PSi sensors with titanium dioxide (TiO2) layers via atomic layer deposition for passivation and optical enhancement.
- Utilizing bacteriophage-encoded PlyB221 endolysin as a lytic agent for targeted bacterial lysis.
- Indirect detection of bacterial lysate by monitoring changes in the optical properties of the PSi membrane.
Main Results:
- The developed TiO2-coated PSi biosensor demonstrated high sensitivity for *Bacillus cereus* detection, reaching 10^3 CFU/mL.
- The assay achieved a total detection time of 1 hour and 30 minutes.
- The biosensor exhibited excellent selectivity and versatility, successfully detecting *B. cereus* in a complex sample matrix.
Conclusions:
- The developed biosensor platform offers a sensitive, selective, and rapid method for indirect bacterial detection.
- The strategy of using lytic enzymes for analyte-specific targeting overcomes limitations of traditional bio-probe based sensors.
- This approach holds promise for various applications requiring efficient bacterial identification.

