Design and fabrication of solution-processed nano/micro-structures and their applications for biosensing

Abstract

Solution processing is a technology that can manufacture high-quality nano/microstructure-based thin films through liquid-to-solid phase transition at room pressure and room temperature without a vacuum environment. In particular, through the 4th industrial revolution and COVID-19 pandemic, electrical signal-based biosensors that can be applied to the point-of-care diagnostics are required. Essential in related research is the development of functional nano/microcomposite with high performance and high uniformity, and solution processing that can precisely control the characteristics of the composites play an important role in commercialization of POC diagnostics. For example, various printing technologies such as 3D printing, inkjet printing, and blade coating can fabricate the high-quality composites by optimizing process parameters and expand their scope to develop the integrated electronic devices. However, in-depth understanding of complex physicochemical phenomena on the surface of the substrate, including electrodes, is still very insufficient. This is due to the fact that the hydrodynamic behavior in the liquid affects the mass transport, and in addition, the evaporation of the solvent increases the complexity of the system.

To address this issue, I investigate the mechanism of mass transport occurred during the printing via computational technologies such as machine learning and numerical simulation. Through the Bayesian optimization-based Gaussian Process Regression (GPR) algorithm, the phase separation between small molecule and polymer was analyzed, and the effect of process parameters on transistor performance was investigated. In this process, information on grazing incident X-ray diffraction (GIXD) and transistor characteristics was used to demonstrate the predictive accuracy of machine learning. In addition, the sensitivity of carbon nanotube-based biosensors for detection of SARS-CoV-2 was optimized through machine learning. Carbon nanotubes (CNTs) are 1D nanomaterials, and their electrical properties vary depending on the characteristics of CNT array. Machine learning was used to analyze the correlation between array characteristics (e.g., alignment, thickness, surface roughness, and surface coverage, etc) and sensitivity, and consequently alignment and surface roughness are closely related to the binding of bioreceptors and tube-to-tube junction. Furthermore, the sensitivities to the IgG and nucleocapsid protein of SARS-CoV-2 was optimized based on in-depth understanding of physicochemical reaction away from the iterative experimental-based optimization approach.

Finally, albumin-based antifouling coating that can prevent physical adsorption of biomaterials was studied. Based on the insights from previous studies, I work on achieve a porous nanocomposite that can minimize the influence of non-target materials and maximize the sensitivity to the target molecules. To this end, I studied the functional emulsion and developed a nanocomposites that can maintain biosensor performance in clinical specimens (e.g., nasopharyngeal, saliva, plasma, etc.) containing various biomolecules. In addition, peptide nucleic acid (PNA) was immobilized to the nanocomposite surface, which is utilized as a CRISPR-Cas based molecular diagnostics. This novel diagnostic technology can simultaneously amplify the targets and electrochemical signals, demonstrating the sensing performance that is applicabile to the POC diagnostics with high sensitivity and high durability.

In conclusion, the research achievements of experimental and theoretical studies can contribute to the development of nanostructures-based biosensors.