BARKER-CODED NODE-PORE RESISTIVE PULSE SENSING WITH BUILT-IN COINCIDENCE CORRECTION

Area of Science:

• Biophysics
• Nanotechnology
• Analytical Chemistry

Background:

• Resistive pulse sensing (RPS) measures particle concentration and size distribution in fluids.
• Current RPS methods face limitations in dynamic range due to channel dimensions and coincidence events (multiple particles simultaneously).
• Discarding coincidence data reduces throughput and introduces errors in particle size distributions.

Purpose of the Study:

• To develop a method to resolve coincidence events in RPS.
• To enhance the dynamic range and accuracy of particle sizing in RPS.
• To overcome limitations of current resistive pulse sensing techniques.

Main Methods:

• Channel coding to generate a Manchester-encoded Barker-Code sequence for system response.
• Formulating particle parameter estimation as a sparse inverse problem.
• Employing a successive interference cancellation algorithm to resolve coincidences and estimate particle size and velocity.
• Modifying the algorithm for device variations and flow stochasticity.

Main Results:

• Demonstrated ability to resolve coincidence events in resistive pulse sensing.
• Successfully screened particles of different sizes through a Barker-encoded device.
• Indicated a potential increase in the device's dynamic range for particle detection.

Conclusions:

• The proposed Barker-coded RPS method effectively resolves coincidence events.
• This approach enhances data accuracy and throughput by utilizing previously discarded coincidence data.
• The technique offers a pathway to significantly improve the dynamic range and reliability of particle analysis using resistive pulse sensing.