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.
Illustrated is a continuous-flow microfluidic device with patterned surface to induce faster nucleation of metal–organic frameworks (MOFs) and other slow-growing crystals, where the cyclonic flow allows trapping of crystals to grow them under controlled conditions.
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