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1. Processing code-multiplexed Coulter signals via deep convolutional neural networks

   https://doi.org/10.1039/c9lc00597h  

   Wang, Ningquan; Liu, Ruxiu; Asmare, Norh; Chu, Chia-Heng; Sarioglu, A. Fatih (September 2019, Lab on a Chip)

   Beyond their conventional use of counting and sizing particles, Coulter sensors can be used to spatially track suspended particles, with multiple sensors distributed over a microfluidic chip. Code-multiplexing of Coulter sensors allows such integration to be implemented with simple hardware but requires advanced signal processing to extract multi-dimensional information from the output waveform. In this work, we couple deep learning-based signal analysis with microfluidic code-multiplexed Coulter sensor networks. Specifically, we train convolutional neural networks to analyze Coulter waveforms not only to recognize certain sensor waveform patterns but also to resolve interferences among them. Our technology predicts the size, speed, and location of each detected particle. We show that the algorithm yields a >90% pattern recognition accuracy for distinguishing non-correlated waveform patterns at a processing speed that can potentially enable real-time microfluidic assays. Furthermore, once trained, the algorithm can readily be applied for processing electrical data from other microfluidic devices integrated with the same Coulter sensor network. 

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2. Real-Time Processing of Code-Multiplexed Coulter Signals Based on a Two-Stage Deep Learning Structure

   Wang, Ningquan; Liu, Ruxiu; Asmare, Norh; Sarioglu, A. Fatih (October 2019, MicroTAS 2019)

   Coulter counters electrically detect and size suspended particles from intermittent changes in impedance between electrodes. By combining the impedance-based sensing with microfabrication, Coulter counters can be distributed across a lab-on-a-chip platform for code-multiplexed monitoring of microfluidic manipulations. In this paper, we augment a code-multiplexed Coulter sensor network with a deep learning-based decoding algorithm for multiplexed detection of cancer cells sorted into different microfluidic channels. 

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3. Polycaprolactone-enabled sealing and carbon composite electrode integration into electrochemical microfluidics

   https://doi.org/10.1039/C9LC00417C  

   Klunder, Kevin J.; Clark, Kaylee M.; McCord, Cynthia; Berg, Kathleen E.; Minteer, Shelley D.; Henry, Charles S. (July 2019, Lab on a Chip)

   Combining electrochemistry with microfluidics is attractive for a wide array of applications including multiplexing, automation, and high-throughput screening. Electrochemical instrumentation also has the advantage of being low-cost and can enable high analyte sensitivity. For many electrochemical microfluidic applications, carbon electrodes are more desirable than noble metals because they are resistant to fouling, have high activity, and large electrochemical solvent windows. At present, fabrication of electrochemical microfluidic devices bearing integrated carbon electrodes remains a challenge. Here, a new system for integrating polycaprolactone (PCL) and carbon composite electrodes into microfluidics is presented. The PCL : carbon composites have excellent electrochemical activity towards a wide range of analytes as well as high electrical conductivity (∼1000 S m −1 ). The new system utilizes a laser cutter for fast, simple fabrication of microfluidics using PCL as a bonding layer. As a proof-of-concept application, oil-in-water and water-in-oil droplets are electrochemically analysed. Small-scale electrochemical organic synthesis for TEMPO mediated alcohol oxidation is also demonstrated. 

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4. Surface roughness effects on ionic polymer-metal composite (IPMC) sensitivity for compression loads

   https://doi.org/10.1117/12.2613127  

   Nagel, William S.; Hussain, Omar A.; Fakharian, Omid; Aureli, Matteo; Leang, Kam K. (January 2022, Electroactive Polymer Actuators and Devices (EAPAD) XXIV)

   The soft and compliant nature of ionic polymer-metal composite (IPMC) sensors has recently been investigated for various applications in soft robotic and mechatronic devices. Recent results of physics-based chemoelectromechanical modeling suggest that IPMC asymmetric surface roughening may enhance the sensitivity under compression. This paper presents initial experimental results on IPMC compression sensors fabricated with varying degrees of asymmetric surface roughness. The roughness is created through a simple mechanical sanding process on the base polymer material, referred to as "polymer abrading technique'", followed by traditional electroless plating to create electrodes. Sample sensors are characterized by measuring the voltage response under different compressive loads. The results show consistently increased sensor sensitivity of the asymmetrically roughened IPMCs versus a control sample. Sensitivity increases non-monotonically with rougher electrode surfaces, where maximum sensitivity of about 0.0433 mV/kPa is achieved with sensor electrodes with 53-74~micrometer abrasions. More variability is also observed through augmented electrode roughness, suggesting greater flexibility for IPMC sensor design. These results align with predictions from the existing physics-based chemoelectromechanical model. 

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5. Decoding of Code-Multiplexed Coulter Sensor Signals via Deep Learning

   https://doi.org/10.1109/TRANSDUCERS.2019.8808195  

   Wang, Ningquan; Liu, Ruxiu; Asmare, Norh; Anandakumar, Dakshitha B; Sarioglu, A. Fatih (June 2019, Transducers 2019)

   Code-multiplexed Coulter sensors can easily be integrated into microfluidic devices and provide information on spatiotemporal manipulations of suspended particles for quantitative sample assessment. In this paper, we introduced a deep learning-based decoding algorithm to process the output waveform from a network of code- multiplexed Coulter sensors on a microfluidic device. Our deep learning-based algorithm both simplifies the design of coded Coulter sensors and increases the signal processing speed. As a proof of principle, we designed and fabricated a microfluidic platform with 10 code-multiplexed Coulter sensors, and used a suspension of human ovarian cancer cells as a test sample to characterize the system. Our deep learning-based algorithm resulted in an 87% decoding accuracy at a sample processing speed of 800 particles/s. 

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