| Author: | Ma, Yingjin |
| Title: | Fabrication and characterization of nanomaterial-based biosensors for sensitive and rapid virus detection |
| Advisors: | Hao, Jianhua (AP) |
| Degree: | Ph.D. |
| Year: | 2025 |
| Department: | Department of Applied Physics |
| Pages: | xx, 159 pages : color illustrations |
| Language: | English |
| Abstract: | Emerging and re-emerging infectious respiratory illnesses caused by pathogens significantly threaten human life. Generally, reverse transcription polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA) are the gold standards for viral detection. Nevertheless, they need long testing durations, high costs, and skilled personnel, developing point-of-care and sensitive biosensors is essential. Upconversion nanoparticles (UCNPs) are competitive nanoprobes in optical biosensors for detecting various molecules. Their chemo-physical characteristics enable the removal of autofluorescent signal interference and differentiate the signal from other background noise. The NIR excitation source can also avoid photodamage to biomolecules, such as DNA/RNA. However, the current limitation of detection (LOD) based on UCNPs is merely at the femtomole level, making it essential to enhance detection sensitivity for effective viral detection. Here, we developed an ultrasensitive and rapid biosensor for detecting SARS-CoV-2 N-gene long-chain oligos utilizing UCNPs and Au-Au dimer through the fluorescence resonance energy transfer (FRET) effect. Our design can greatly enhance the LOD to attomolar levels compared to traditional single AuNPs to UCNPs, which is considerably more sensitive than most biosensors. The calculations and simulation results reveal the synergetic effect of plasmonic resonance and FRET, resulting in greater quenching efficiency (QE) between Au-Au dimers and UCNPs. Additionally, the clinical detection of inactivated SARS-CoV-2 is successfully performed with outstanding selectivity within 30 min. This fast and ultra-sensitive biosensor establishes a novel approach for clinical diagnosis without target amplification. Although UCNP-based biosensors show exceptional sensitivity and high specificity for viral detection, conducting simultaneous detection of several viruses inside a system poses significant challenges. Most technologies, such as RT-PCR and ELISA, are limited by primers and can only detect one type of virus at a time. Consequently, the rapid and accurate classification of various viruses is essential for the risk assessment of public health or epidemiological investigations. Taking advantage of the unique molecular "fingerprints" of different analytes, a 3D plasmonic Au-PS SERS chip was developed for the multiple, rapid and sensitive detection of infectious respiratory viruses using a label-free method. In contrast to typical Au substrate, our fabricated Au-PS chip significantly amplifies the SERS signal over 6.4×10³ fold, facilitating the capture of tiny vibrations, which is the result of synergistic coupling between local surface and gap-surface plasmon resonance. The SERS spectra of SARS-CoV-2, RSV, Influenza A and B exhibit similarities with subtle differences among them, but they are distinct from those of the healthy group. Meanwhile, the SERS spectra of viral mixtures with two random viruses exhibit the distinctive peak features of both viruses. These results demonstrate that our device can simultaneously identify multiple viruses with excellent sensitivity, rapidity, and simplicity. Outbreaks of SARS-CoV-2 make us realize the severity of pathogen X to public health and society security. Considering the possibility of false negatives from RT-PCR tests for novel or mutated pathogens, there are significant challenges in the rapid identification and containment of known or unknown respiratory infections. Based on the advancement of artificial intelligence (AI), we developed an AI-assisted SERS platform for infectious respiratory viruses and pathogen X screening. An ensemble model was designed to analyze SERS spectra from SARS-CoV-2, RSV, influenza A and B, and a healthy group, achieving a prediction accuracy of 100%. Moreover, the 100% classification accuracy for viral mixtures effectively avoids the interference of SERS signals from co-infectious patients. Notably, the prediction of nasal samples and simulated models of pathogen X show excellent results by our models, indicating the practicality and feasibility of our biosensing platform for clinical samples and emerging pathogens prediction. Our strategy is also capable of assessing the viral concentrations from 5×10² to 106 PFU/mL. The detection approach combining SERS and AI in this work shows great promise in the early screening and fight against infectious respiratory diseases, including pathogen X. |
| Rights: | All rights reserved |
| Access: | open access |
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