Our senses form the basis by which we understand, observe, and interact with nature. Thus, our human experience is rooted in sensing (i.e., the detection, conversion, and interpretation of signals).
As engineers, sensing provides the basis for understanding (interpreting), monitoring (observing), and controlling (interacting with) processes and systems. Similar to our human experience, the quality of inductive reasoning, decision making (e.g., diagnostics and prognostics), and control outcomes in an engineering context is dependent on accurately interpreting signals (observations) based on a combination of experience (empiricism) and logic (rationalism).
The concept of a reaction is foundational to the modeling of many complex processes and systems that involve the generation or consumption of resources (e.g., materials, information, energy), such as biological processes and systems.
For example, consider the generation of products from available industrial waste or sustainable resources, which can be modeled as a chemical reaction:
A + B + … <---> P + Q + … (Equation 1)
where A, B, P, and Q represent arbitrary reactants and products, respectively. While Equation 1 is foundational to the modeling of traditional chemical processes, it is also central to the modeling of biological and environmental processes. And that's where the story gets interesting.
Ideally, we would like to continuously monitor and control complex biological processes and systems, such as bioprocesses associated with the manufacturing of products from industrial waste or the spread of infectious diseases, by measuring the concentration of the species involved in the reaction in real time (often these species are critical inputs or products themselves). However, it remains a challenge to create robust sensors capable of continuously monitoring the quality attributes of ‘biologics’ (e.g., biomacromolecules, cells, and tissues) due to present barriers in biosensing technology and methodology.
Specifically, measurement accuracy (i.e., reliability) and speed (i.e., mitigation of time delay) are critical technical challenges that must be overcome to create robust, high-performance biosensors capable of continuously monitoring and controlling bioprocesses and biomanufacturing processes (e.g., bioreactors).
Our lab is focused on addressing these challenges through interdisciplinary research in chemical, mechanical, industrial, and electrical engineering, data science, and physics.
So Who Cares?: The sensitive, selective, and real-time continuous identification and quantification of biologics, which include biomacromolecules, viruses, and cells, is central to critical components of the United States manufacturing industries, economy, and national security as well as advancing fundamental research and emerging technologies across various fields that benefit humanity.
Device-based sensing (detection, identification, quantification) of material composition using chemical sensors and biosensors is central to quality control in various critical industries, including medical diagnostics and biomanufacturing, and fundamental research in various fields, including materials science and tissue engineering. Quality assurance and control in such industries is reliant on robust 'process analytical technology (PAT).' While various PAT have been examined to date for measurement of material composition, the complexity of the physical, chemical, and biological systems that generate the data and desired measurement objectives, such as those found in modern bioprocessing and biomanufacturing environments, requires continued innovations in sensor-based PAT (e.g., sensor design, manufacturing, and analytics).
We are inspired by the sentiment "If you can't sense it, you can't control it." Our group is focused on making fundamental advancements in the fields of sensing, process monitoring, diagnostics, and prognostics, sensor-based PAT, and autonomous manufacturing/experimentation with present focus on applications in biosensing, biomanufacturing, and autonomous chemistry.
Many of our current research projects are inspired by fundamental research in analytical chemistry and signal processing as well as the Materials Genome Initiative and COVID-19 pandemic, which motivate the need for advances in smart sensing and smart biomanufacturing. Specifically, our lab is focused on establishing novel methods for smart sensing and smart biomanufacturing via science-informed data science.
Research on sensors and sensing, and applications thereof in the biosensing and biomanufacturing domain, is interdisciplinary and applies theory, principles, and methodology from various engineering disciplines as well as data and computer science, neuroscience, biology, chemistry, and physics. Students from all backgrounds are welcome to venture deeper into the fascinating world of a smart sensing and Biomanufacturing 4.0.