Multi-channel detection of dopamine and glucose utilizing graphene field effect transistor electrochemical sensor and efficient data fusion algorithm

Schizophrenia, a severe neurological disorder, is linked to aberrations in dopamine (DA) and glucose levels. While numerous methods have been developed for the sensitive and selective detection of either DA or glucose individually, there remains a challenge in achieving simultaneous detection of both with a single platform. To address this, we designed a graphene field effect transistor (GFET) based electrochemical sensor, augmented with 11-mercaptoundecanoic acid-gold nanoclusters (MUA-AuNCs) and pyrene-1-boronic acid (PBA) for enhanced DA and glucose detection. MUA-AuNCs and PBA improved selectivity and sensitivity towards DA and glucose respectively. Moreover, we incorporated a unique data fusion algorithm based on a least squares method. Utilizing a dual-channel sensor setup, this algorithm corrects interference by leveraging accurate DA readings from one channel to compute precise glucose concentrations in mixed solutions, thus ensuring high detection accuracy. This sensor displayed exceptional performance in simultaneous DA and glucose detection, addressing prevalent issues in current methods. Our approach not only has potential to improve schizophrenia monitoring but also provides insights for electrochemical sensor design and optimization.