Glucose metabolism-inspired poker-pattern microfluidic chip for bacterial detection and identification

This research presents an innovative poker-pattern microfluidic chip, specifically the club micromixer structure on a chip, designed to utilize microfluidics and machine learning for bacteria detection and discrimination. This microfluidic chip structure draws inspiration from playing card patterns and employs chaotic flow and Dean vortices to achieve effective mixing. This study utilizes bacterial metabolism to fabricate a dual-mode detection strategy on chip, combining colorimetric method with fluorescence techniques, both of which exploit metabolic interactions between bacteria and glucose. The limits of detection (LOD) are 8.8 and 7.6 CFU·mL⁻¹ respectively. The micromixer exhibits higher sensitivity in Escherichia coli (E. coli) detection, as well as improving mixing efficiency and maintaining favorable pressure drop characteristics. The integration of machine learning cluster analysis methods further augments the system's capacity to discriminate among various bacterial species. The microfluidic chip demonstrates its practicality in real samples, such as milk, highlighting its prospective utility in the realms of food safety and public health surveillance.