Machine Learning-Driven, Sensor-Integrated Microfluidic Device for Monitoring and Control of Supersaturation for Automated Screening of Crystalline Materials

Abstract

Integrating sensors in miniaturized devices allow for fast and sensitive detection and precise control of experimental conditions. One of the potential applications of a sensor-integrated microfluidic system is to measure the solute concentration during crystallization. In this study, a continuous-flow microfluidic mixer is paired with an electrochemical sensor to enable in situ measurement of the supersaturation. This sensor is investigated as the predictive measurement of the supersaturation during the antisolvent crystallization of l-histidine in the water–ethanol mixture. Among the various metals tested in a batch system for their sensitivity toward l-histidine, Pt showed the highest sensitivity. A Pt-printed electrode was inserted in the continuous-flow microfluidic mixer, and the cyclic voltammograms of the system were obtained for different concentrations of l-histidine and different water-to-ethanol ratios. The sensor was calibrated for different ratios of antisolvent and concentrations of l-histidine with respect to the change of the measured anodic slope. Additionally, a machine-learning algorithm using neural networks was developed to predict the supersaturation of l-histidine from the measured anodic slope. The electrochemical sensors have shown sensitivity toward l-histidine, l-glutamic acid, and o-aminobenzoic acid, which consist of functional groups present in almost 80% of small-molecule drugs on the market. The machine learning-guided electrochemical sensors can be applied to other small molecules with similar functional groups for automated screening of crystallization conditions in microfluidic devices.

Three-Step Mechanism of Antisolvent Crystallization

“…A sample of growth rate measurements is shown in Figure S2. Details of the experimental methods are reported in our previous works. − …”

Ultrahigh-sensitivity multi-parameter tacrolimus solution detection based on an anchor planar millifluidic microwave biosensor

“…18,19 It can be used to detect very small amounts of liquid samples, such as 10 −9 -10 −18 L. 20 Micro-uidics technology is characterized by its small volume, rapid analysis speed, automatic completion of the entire process of sample analysis, and increasing integration scale, allowing for the development of high-cost, compact, and one-time detection instruments. [21][22][23] Currently, many effective detection technologies have been validated in microuidic devices, including electrochemical methods, 24 uorescence detection methods, 25 and RF sensing methods. 26 For example, Xu et al detected tumor-derived exosomes using a magnetic microuidic chip; 27 Peng et al used online uorescence derivatization ow injection in a microuidic device for the detection of Cr(III) and Cr(VI) in water samples aer solid-phase extraction; 28 Liu et al used a microuidic RF biosensor to monitor cell growth.…”

Harnessing Deep Neural Networks to Analyze Multi‐Channel Anion Sensing Characteristics of a Ru(II)‐Pyrazolyl‐Bis(Benzimidazole) Complex

“…The technological progress of ML is now manifested in nearly all branches of science and technology [1–11] . Through proper handling of powerful computation and high‐throughput experimentation, ML has expedited the scientific research and technological development [12–31] . Even though the adoption of data‐guided growth of materials is inspiring to recognize the accurate potential of ML models, they should also have the potentiality over solely predictive ability.…”

“…[1][2][3][4][5][6][7][8][9][10][11] Through proper handling of powerful computation and highthroughput experimentation, ML has expedited the scientific research and technological development. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] Even though the adoption of data-guided growth of materials is inspiring to recognize the accurate potential of ML models, they should also have the potentiality over solely predictive ability. The prediction and inner execution of models must offer appropriate explanation to the human specialists.…”