Miniaturized Chip Integrated Ecological Sensor for the Quantitation of Milnacipran Hydrochloride in the Presence of Its Impurities in Dosage Form and Human Plasma

Abstract

The sensitivity and selectivity of potentiometric sensors towards their targets in various matrices have been challenging for analysts since their development. Modifications of the sensor composition provide the sensitivity and selectivity of potentiometric sensors towards their targets in various matrices have been challenging for analysts since their development. Modifications of the sensor composition provide a way to achieve the utmost sensitivity and selectivity. Molecular imprinting, together with conducting polymers and graphite derivatives, represents a robust composite that attains better sensor performance. Besides, the miniaturization of potentiometric sensors is advantageous to the sensitivity and greenness of the developed sensors due to their smaller portable size, lower sample volume, and lower waste. These advantages provide these sensors with a fast track toward developing wearable versions of ion-selective sensors, which are valuable in point-of-care services in different clinical settings. Here, we report on the first recyclable on-chip miniaturized potentiometric sensors integrated by a printed Ag/AgCl quasi-reference electrode for the quantitation of milnacipran hydrochloride (MLN) in its dosage form and human plasma in the presence of its impurities. The sensing module of the sensor is modified by a molecularly imprinted conducting polymer (polyaniline), which is computationally optimized to achieve maximum selectivity towards MLN in the presence of its impurities. Moreover, the designated sensor composition is optimized by a self-validated ensemble modeling experimental design seeking the composition leading to optimal sensor performance. The proposed on-chip sensor successfully achieved a LOD value of (7.94 × 10−7 M) and a linear range of (1 × 10−6 − 1 × 10−2 M) with a green analytical profile as redeemed from the Agree assessment score.