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My current work is related to the design and development of biophotonic and bioelectronic systems that have primary applications in healthcare and fluid quality monitoring.
Scalable design of electrochemical impedance-based sensor for E.coli in water
Scalable design of electrochemical impedance-based sensor for E.coli in water
I was part of a project that involved the development of a low-cost sensor architecture for detecting bacterial colonies in water samples. In particular, we analyzed the electrochemical impedance of suspensions containing different concentrations of E.coli using a scalably fabricated fluidic chamber containing screen-printed interdigitated electrodes. Our findings indicated that it is possible to develop scalable sensors and associated algorithmic approaches to infer the presence and concentration of bacterial colonies in consumer and pharmaceutical waters
Biomedical Microdevices, Volume 23, Article number: 26 (2021)
Time-resolved autofluorescence for rapid pathogen assessment in a portable form factor
Time-resolved autofluorescence for rapid pathogen assessment in a portable form factor
Our team at Adiuvo developed a portable probe for time-resolved autofluorescence measurements for rapid pathogen assessment in a range of contexts including water quality monitoring, cancer tissue detection, food quality monitoring, biofilm detection, cell growth tracking and so on. In a first-of-its-kind portable form factor, our group was able to demonstrate the use of autofluorescence lifetimes as a reliable indicator of various microbiological parameters of interest. This work was featured as a keynote talk at SPIE-PW, 2023, San Francisco, CA.
Proceedings Volume 12391, Label-free Biomedical Imaging and Sensing (LBIS) 2023; 1239102 (2023)
Label-free AI-powered platform based detect bacterial concentrations <100 CFU/ml
Label-free AI-powered platform based detect bacterial concentrations <100 CFU/ml
This interesting piece of work involves the demonstration of >90% accuracy in detecting a range of bacteria and fungi in water samples using label-free autofluorecence lifetime-based methods and advanced detection models based on machine learning.
Our current field validations also show >90% accuracy in bioburden detection in raw water samples.
Proceedings Volume 12854, Label-free Biomedical Imaging and Sensing (LBIS) 2024; 1285402 (2024) https://doi.org/10.1117/12.3003634
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