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.Comment: 2017 IEEE 30th International Conference on Micro Electro Mechanical
Systems (MEMS