Conceptual Development of an Ultra-Sensitive Biosensor Based on a Quantum Dot Coated Microresonator for Exosome Characterization and Specification

• Applicant: Dr. Jalali Mandana
• Subject Area: Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics ; Biophysics ; Optics, Quantum Optics and Physics of Atoms, Molecules and Plasmas
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 494809387

The main objective of the proposed project is the exploration and design of ultra-sensitive excitonic-photonic biosensor capable of studying the physiology of exosomes on a single particle level. Through investigating the physiology of one single exosome, a benchmark for an on-chip method for early stage cancer diagnosis based on non-invasive liquid biopsy will be set. The sensor consists of a quantum dot (QD) coated microresonator capable of sustaining whispering gallery modes coupled to the excitonic mode within the QD layer. Adding a shell to the microresonator through reducing the radiation loss together with decreasing the mode volume improves the sensing limit. Additionally, the potential of electromagnetic induced transparency (EIT) due to a excitonic-photonic mode coupling, will substantially enhance the resonator’s quality factor. In the immediate vicinity of the resonator will be a plasmonic nanoantenna, which enhances the electric field strength at the sensing location. Such sensor has the potential of reaching unprecedented sensitivity which results in physiological selectivity for any given bio-recognition element. An electromagnetic model based on tensorial representation of the effective medium theory (EMT, using a perturbative Maxwell-Garnett approach) will be developed for the exosomes to reliably connect their physiological properties such as mass, density, shape as well as biological constituents to their characteristic dielectric function. Subsequently such model is capable to relate any frequency shift within the biosensor (via a machine-learning-based classification) to the exosome’s proper physiology and provides thus a highly-sensitive measure for the classification of the healthy and cancerous exosome's physiological characteristics.

• DFG Programme: Research Grants