Code-division multiplexed resistive pulse sensor networks for spatio-temporal detection of particles in microfluidic devices
This work addresses the need for spatio-temporal particle detection in lab-on-a-chip devices, representing an incremental improvement in multiplexing capacity.
The paper tackles the problem of detecting particles in microfluidic devices by enhancing a resistive pulse sensor network with non-orthogonal code waveforms and a machine learning-based decoding algorithm, achieving a proof-of-concept demonstration with 10 multiplexed sensors using cells in buffer solution.
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.