Time scale analysis of enzymatic reduction of uric acid in a microfluidic biomedical device
Issued Date
2024-01-01
Resource Type
eISSN
26732718
Scopus ID
2-s2.0-85207010214
Journal Title
Frontiers in Chemical Engineering
Volume
6
Rights Holder(s)
SCOPUS
Bibliographic Citation
Frontiers in Chemical Engineering Vol.6 (2024)
Suggested Citation
Asgharpour Masouleh A., Pantakitcharoenkul J., Coblyn M., Plazl I., Jovanovic G.N. Time scale analysis of enzymatic reduction of uric acid in a microfluidic biomedical device. Frontiers in Chemical Engineering Vol.6 (2024). doi:10.3389/fceng.2024.1451222 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101813
Title
Time scale analysis of enzymatic reduction of uric acid in a microfluidic biomedical device
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Abstract
Time Scale Analysis (TSA) is an investigative tool used in engineering design to identify locations in processes that should be a focus of Process Intensification (PI). Furthermore, TSA points to process variables and parameters that could be used to advance and measure PI improvement. However, TSA cannot suggest any specific design solution to intensify process performance. Instead, design engineers should use their fundamental knowledge and creative intelligence to specify detailed design transformations. TSA will then provide a specific quantitative measure of the improvement. TSA implementation improves an explicitly defined process performance, thus helping achieve process intensification goals. TSA is based on first principles, and it utilizes Characteristic Times (CT) such as diffusion, mean residence, and reaction times to improve an existing process. In this study, we specifically consider microfluidic biomedical devices. To illustrate the genesis of CT and TSA, we start by developing a mathematical model of an enzymatic degradation process in a biomedical device called iCore based on mass, momentum, and kinetic equations. After introducing user-defined scaling parameters, we extract CTs pertinent to the enzymatic degradation of uric acid in this microfluidic biomedical device. Diffusion coefficients, microchannel architectural characteristics, enzyme loading, hydrogel thickness, and characteristic parameters of enzyme kinetics are the parameters and process variables incorporated in this analysis. Finally, we compared the extracted CTs with a COMSOL Multiphysics parametric study to demonstrate how time scale analysis as a design tool and adjusting design parameters, such as diffusion coefficient, hydrogel layer thickness, substrate concentration, and enzyme concentration, can enhance the enzymatic reaction process without a need for complex computational modeling. It is crucial to recognize that pertinent CTs can be determined by understanding the type and nature of the observed process, previous experience, published data, and other foundational engineering design work. There is no need for mathematical modeling and numerical simulations to identify and acknowledge the CTs relevant and essential to the observed process; in this work, we only illustrate the principal origin of CTs via a detailed mathematical model of the process, as previously reported by Jovanovic et al. Therefore, in a routine application of TSA, it is important to remember that mathematical modeling and detailed numerical simulations are not necessary. This is a very comforting fact when TSA is deployed as a tool in higher-level process design functions. The investigations on how best to apply TSA in these higher level design functions such as Process Intensification, scale-up/numbering-up, change of device architecture, change of operating conditions, change of process feed characteristics, change of material physical and chemical properties, parametric optimization of the system for various objective functions, and techno-economic analysis, are yet to be studied and reported.