Accurate measurements of proteins are critical for modern biomedical research, particularly for early detection and monitoring of disease, systems biology, and new drug development. More specifically, sensitive, specific, fast, and low-cost detection of proteins biomarkers in patient samples is of importance for public health and a key component of future personalized diagnostics and therapy. To date, enzyme-linked immune-sorbent assays in all their variants are the most common sensing techniques employed to detect the presence of protein biomarkers. However, a number of limitations still exist that greatly diminish their broader applications due to the low-sensitivity, the requirement of a calibration plot to elucidate the results, and the need for advanced costly instruments for ultrasensitive measurement. This proposal aims to address the aforementioned drawbacks by developing a simple and low-cost, but potentially powerful sensor based on the integration of a two-level signal amplification sensing mechanism and machine-learning-enabled reliability. The developed sensing devices are particularly desirable for early disease detection, ideally capable of "digitally" (calibration-free) detecting the presence of biomarkers with excellent portability, adaptability, accuracy, and high throughput. The knowledge and technology gained from this research will be disseminated to the scientific community and industry. This project will impact the education of the graduate, undergraduate and high school students by integrating advanced biosensing knowledge into their educational and laboratory training, and also actively interacting with general public and industry companies.
The proposed sensing device will allow immunoassay to be performed as a low-cost and digital (calibration-free) detection technique with potential single copy sensitivity through integration of three key components: 1) a coupled resonance between a piezoelectric substrate and a replaceable nanoimprinted acoustic wave micropillar resonance film "sticker", 2) a micron-sized mass amplifier realized through uniform silver enhancement of mono-dispersed gold nanoparticles conjugated with detection antibody, and 3) machine learning algorithms to be developed and trained to analyze the biosensing data in a reliable way, thus allowing more accurate detection. The number of the particles can be directly correlated to the number of biomarkers, realizing "digital" sensing. The developed methodology is not restricted to sandwich-type immunoassays, nor is the assay limited to any particular protein biomarker and therefore the develop sensor can be generalized as a universal platform for monitoring any biomolecule of interest such as protein biomarkers, viruses, and pathogens for assuring public health and biosafety. The work will revolutionize the acoustic wave-based biosensing platform if successful.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
