23 December 2003 Modeling of microscale processes as a tool to speed development and enhance performance of microanalytical products
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Proceedings Volume 5345, Microfluidics, BioMEMS, and Medical Microsystems II; (2003); doi: 10.1117/12.538592
Event: Micromachining and Microfabrication, 2004, San Jose, California, United States
With macroscopic chemical analysis devices, it is usually possible during the development phase to mount flow sensors, temperature probes, and optical detectors at various positions along the instrument pathway to experimentally determine the optimum operational parameters for the device. This approach usually fails for microdevices as standard sensors and probes are typically of the same scale as the microdevice. These relatively large sensors interfere so much with the experiments that any results generated do not represent the actual performance of the system. Fortunately, modeling of microscale processes provides a uniquely useful tool to develop microanalytical devices and optimize their operational parameters, since the chemical and physical processes in the microscale generally follow deterministic physical laws that can be accurately represented in mathematical models. We will discuss some of the methods used to model and design microanalytical assays based on these principles, as well as several ongoing development projects that MicroPlumbers is currently involved in (including a clinical microsensor, a microvolume blood collection device, and a novel clinical assay), and show how modeling and rational design is used during the development processes and yield devices with optimized performance.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bernhard H. Weigl, Ron L. Bardell, "Modeling of microscale processes as a tool to speed development and enhance performance of microanalytical products", Proc. SPIE 5345, Microfluidics, BioMEMS, and Medical Microsystems II, (23 December 2003); doi: 10.1117/12.538592; https://doi.org/10.1117/12.538592



Mathematical modeling

Process modeling




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