Integrated dual electrochemical device for multiplexed detection of biomarkers

• Grant number: 22/03758-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Effective date (Start): November 01, 2022
• Effective date (End): October 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter Physics
• Principal Investigator: Osvaldo Novais de Oliveira Junior
• Grantee: Thiago Serafim Martins
• Host Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis, AP.TEM
• Associated scholarship(s): 22/15122-3 - Developing new breathable tattoo (bio)sensors for health monitoring, BE.EP.PD

Abstract

The combination of electroanalytical methods and machine learning holds promise for the early diagnosis of cancer and other diseases, particularly with sensitive devices operating at the point of care. Even more reliable and complete results can be obtained with devices that simultaneously detect multiple biomarkers. However, producing these sensors free of label and external redox mediators is a challenge. In this project, we will develop integrated dual electrochemical devices composed of graphene (or carbon nitride) materials and signaling molecules(electrochemically active) immobilized on the detection platform. The devices will be constructed using printed electrodes, and 2-3-diaminophenazine (DAP), toluidine blue (TB), and polydopamine (PDA) will be used as signaling molecules on the active layer. Devices will be developed for the multiplexed detection of the following biomarkers: i) spike protein (SARS-CoV-2), hemagglutinin (HA), and neuraminidase (NA) glycoproteins (H3N2 influenza); ii) interleukin-6 (IL-6) and vascular endothelial growth factor (VEGF) proteins (colon cancer); iii) and carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) (lung cancer). The molecular architectures of the sensors will be investigated by X-ray diffraction (XRD), transmission electron microscopy (TEM) and scanning electron microscopy (SEM), X-ray excited photoelectron spectroscopy (XPS), atomic force microscopy (AFM), cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), polarization modulated reflection and absorption infrared spectroscopy (PM-IRRAS), and sum frequency generation spectroscopy (SFG). Results will be analyzed with statistical and computational tools, including information visualization and machine learning.