Multiplexed molecular biomarker analysis using an expanded library of nanoelectronically barcoded particles enabled through machine learning analysis

Electronically barcoded micro-particles have been demonstrated for use in various multiplexed molecular biomarker assays. Traditional optical and plasmonic methods for barcoding are capable of high throughput and high sensitivity, but require bulky instrumentation for readout, which cannot be easily made into a portable device. Previously, we reported a novel impedance based barcoding technique by fabricating tunable nano-capacitors on micro-particle surfaces thus modulating the overall particle impedance. In this work, we expand the library of barcoded particles using atomic layer deposited oxides of varying thickness and dielectric permittivity and study the effect of thickness and dielectric permittivity using multi-frequency impedance flow cytometry and utilize machine learning to classify different particle barcodes.