Modeling of a spiral resonant sensor for biological process monitoring

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Elsherbiny, Omar Hesham
Major Professor
Neihart, Nathan M.
Zoughi, Reza
Reuel, Nigel F.
Committee Member
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Electrical and Computer Engineering
Bioprocess engineering is a field concerned with the design and development of products derived from biological resources such as food, agricultural feed, and pharmaceutical health products. In 2022, the global bioprocessing market was valued at over 1 trillion USD and is estimated to grow annually by 14% until at least 2030. This substantial market value, as well as its far-reaching impact on human life, necessitates high quality standards. A plethora of sensing techniques are utilized to attain high quality control; thus, safeguarding the safety of consumers. Spectroscopic sensing techniques are at the forefront of sensors used in bioprocess monitoring and analysis. A ubiquitous sensing technique is employing dielectric spectroscopy through the use of a dielectric probe. However, these probes present a contamination hazard as they require a sample to be extracted from a sterile environment and/or inserting the probe into a sterile bioreactor environment. Additionally, the use of these probes can be cost prohibitive for upstream development where multiple sensors are needed. Researchers have thus been drawn to LC resonant sensors (which exploit the same operating principle of dielectric spectroscopy) due to their passive non-invasive nature and low cost of fabrication. This thesis presents a lumped element circuit model for an Archimedean spiral LC resonant sensor which accurately detects and quantifies ionic concentrations of aqueous solutions, which are pervasive in bioprocesses. The model is informed by the observed change in electrical properties caused by changing an analyte’s aqueous concentration. The system’s response to different concentrations of aqueous potassium chloride is measured, and the model parameters are extracted from the measured data. The model parameters are then extrapolated to obtain expressions that accurately model the system’s response with respect to the analyte’s concentration. This model offers insight into the sensing system’s mechanism of transduction, which could be extended to other analytes, and be used to systemically optimize the sensor’s design.