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Mi-S3.1-GIB II: Premios SEIB-Fenin (II) Lugar: Salón de Actos Presidente de la sesión: Elisabete Aramendi Ecenarro Presidente de la sesión: Juan Carlos Antony García Presidente de la sesión: Pablo Laguna Trabajos seleccionados para el Premio SEIB-FENIN (estudiantes del Grado en Ingeniería Biomédica). | |
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16:52 - 17:03
Development of Graphene-Based Field-Effect Transistor Biosensors for Depression-Related Biomarkers Detection 1: Dept. of Automatic Control. Universitat Politècnica de Catalunya; 2: Dept. of Electronic Egineering, Universitat Politècnica de Catalunya; 3: Inst. for Medical Engineering and Science. Massachussetts Institute of Technology; 4: Microsystems Technology Laboratories. Massachussetts Institute of Technology; 5: Institute of Bioengineering of Catalonia.; 6: CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) This work focuses on the development of a graphene-based field-effect transistor (GFET) biosensor for the real-time detection of biomarkers associated with major depressive disorder (MDD). The main objective is to design a graphene-based biosensor that allows the measurement of molecular biomarkers related to MDD, such as inflammatory proteins and metabolites, for early diagnosis, monitoring of its progression, and potential personalized treatment strategies. On the biological side, the graphene surface is functionalized with biorecognition elements to enable the selective detection of IL-6, a biomarker linked to depressive processes. This involves the use of chemical linkers and validation through advanced characterization. In parallel, the electronic development includes the design of a printed circuit board (PCB) and the integration of a chip based on graphene field-effect transistors (GFETs), arranged in a 64×64 array. Electrical measurements are performed to assess the sensor’s performance and stability, providing the basis for its future application in portable diagnostic systems. These results provide a solid foundation for advancing GFET technology toward multiplexed biosensing and future integration into real-time platforms for mental health monitoring.
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