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Designing lab-on-a-chip systems with attribute dependency graphs

Published online by Cambridge University Press:  16 May 2024

Johannes Soika*
Affiliation:
Laboratory for Product Development and Lightweight Design, TUM School of Engineering and Design, Technical University of Munich, Germany
Tobias Wanninger
Affiliation:
Laboratory for Product Development and Lightweight Design, TUM School of Engineering and Design, Technical University of Munich, Germany
Patrick Muschak
Affiliation:
Chair of Bioseparation Engineering, TUM School of Engineering and Design, Technical University of Munich, Germany
Sebastian Schwaminger
Affiliation:
Chair of Bioseparation Engineering, TUM School of Engineering and Design, Technical University of Munich, Germany Division of Medicinal Chemistry, Otto Loewi Research Center, Medical University of Graz, Austria
Sonja Berensmeier
Affiliation:
Chair of Bioseparation Engineering, TUM School of Engineering and Design, Technical University of Munich, Germany Munich Institute for Integrated Materials, Energy and Process Engineering, Technical University of Munich, Germany
Markus Zimmermann
Affiliation:
Laboratory for Product Development and Lightweight Design, TUM School of Engineering and Design, Technical University of Munich, Germany

Abstract

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Lab-on-a-Chip (LOC) products for point-of-care diagnostics have gained significant attention. However, there is a lack of systematic approaches for LOC product development. To address this, we introduce an Attribute Dependency Graph exemplary for a magnetophoretic LOC system for pathogen detection. This model organizes dependencies between the design variables and crucial quantities of interest, such as detectability, cost per test, and test duration. The obtained model helps to manage design complexity and can be adapted to other LOC approaches.

Type
Design Methods and Tools
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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