Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning
This work addresses the problem of limited analyte detection and reduced robustness in biosensors for medical diagnostics, environmental monitoring, and food safety, representing an incremental improvement with a novel method.
The researchers tackled the limitation of traditional biosensors that require specific capture agents by developing a capture agent-free platform using porous silicon arrays and machine learning, achieving reproducible classification, quantification, and discrimination of three proteins down to concentrations as low as 0.02g/L (300-450nM).
Biosensors are an essential tool for medical diagnostics, environmental monitoring and food safety. Typically, biosensors are designed to detect specific analytes through functionalization with the appropriate capture agents. However, the use of capture agents limits the number of analytes that can be simultaneously detected and reduces the robustness of the biosensor. In this work, we report a versatile, capture agent free biosensor platform based on an array of porous silicon (PSi) thin films, which has the potential to robustly detect a wide variety of analytes based on their physical and chemical properties in the nanoscale porous media. The ability of this system to reproducibly classify, quantify, and discriminate three proteins is demonstrated to concentrations down to at least 0.02g/L (between 300nM and 450nM) by utilizing PSi array elements with a unique combination of pore size and buffer pH, employing linear discriminant analysis for dimensionality reduction, and using support vector machines as a classifier. This approach represents a significant step towards a low cost, simple and robust biosensor platform that is able to detect a vast range of biomolecules.