Code-division multiplexed resistive pulse sensor networks for spatio-temporal detection of particles in microfluidic devices

TL;DR

This paper advances microfluidic particle detection by developing a multiplexed resistive pulse sensor network using non-orthogonal codes and machine learning for decoding, enabling efficient spatial particle analysis.

Contribution

It introduces a novel multiplexing approach with non-orthogonal codes and a machine learning-based decoding algorithm for microfluidic resistive pulse sensors.

Findings

Successfully fabricated a 10-sensor multiplexed device

Achieved accurate decoding of particle signals

Enhanced sensor network capacity using new coding and decoding methods

Abstract

Spatial separation of suspended particles based on contrast in their physical or chemical properties forms the basis of various biological assays performed on lab-on-achip devices. To electronically acquire this information, we have recently introduced a microfluidic sensing platform, called Microfluidic CODES, which combines the resistive pulse sensing with the code division multiple access in multiplexing a network of integrated electrical sensors. In this paper, we enhance the multiplexing capacity of the Microfluidic CODES by employing sensors that generate non-orthogonal code waveforms and a new decoding algorithm that combines machine learning techniques with minimum mean-squared error estimation. As a proof of principle, we fabricated a microfluidic device with a network of 10 code-multiplexed sensors and characterized it using cells suspended in phosphate buffer saline solution.