"Advancing SERS biosensing through adaptable sensor design and field-ready applications"

Pietro Strobbia, Assistant Professor
University of Cincinnati, Department of Chemistry 
Hosted by: Dr. Robbyn Anand

Abstract: Our research is largely directed towards bridging the gap between current state-of-the-art sensing technologies and the actual requirements for their application in clinical settings or in the field. Emerging infectious diseases and global food security are arising as major societal challenges due to climate change and other shifting conditions. We believe surface-enhanced Raman scattering (SERS)-based technology can play a key role in tackling global challenges, including early surveillance of infectious diseases and advancing precision agriculture. Our goal is to advance the state-of-the-art of SERS biosensors developing solutions designed to meet these global challenges. 

To ensure these biosensors meet the performance and readiness demands for point-of-care diagnostics, our lab has also been working on automation and optimization of sensor design, as a tool for rapid response to emerging threats. Being able to predict the figures-of-merit for a sensor given input genetic target sequence removes the requirements for validation steps and permits to optimize sensor performance within a given target genome. Our previous work has focused on designing a catalytic sensing mechanism for reagentless SERS sensors that can be used in point-of-need sensing platforms. The optimization of this sensor design was integrated in an automated design algorithm. Our current research focus is on establishing a protocol for inverse sensor design. We are using machine learning to analyze sensor performance as a function of the sequences used in our sensors to optimize our design algorithm. 

Within this effort, our lab has been working on the translation of SERS biosensors for real-world applications. Specifically, we have been focused on designing a SERS sensing platform for agricultural settings. We have developed SERS-sensing hydrogels for the detection of viral infections directly in plants. This sensing platform integrates reagentless sensors in a hydrogel matrix. The hydrogel materials are optimized for the extraction of viral and genetic material from the plant interstitial fluid. The hydrogels also function as an optimal matrix for the sensors to respond to target genetic material without additional processing steps. Our current research focus is on tuning the hydrogel material for the extraction of specific analytes and understand its relation to sensing performance.