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      Dynamic Radial Placement and Routing in Paper Microfluidics

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          Abstract

          The low cost, simplicity, and ease of use of paper microfluidic devices have made them valuable medical diagnostics for applications from pregnancy testing to COVID-19 screening. Meanwhile, the increasing complexity of paper-based microfluidic devices is driving the need to produce new tools and methodologies that enable more robust biological diagnostics and potential therapeutic applications. A new design framework is being used to facilitate both research and fabrication of paper-based microfluidic biological devices to accelerate the investigative process and reduce material utilization and manpower. In this work we present a methodology for this framework to dynamically place and route microfluidic components in a nondiscrete design space where fluid volume usage, surface area utilization, and the timing required to perform specified biological assays are accounted for and optimized while also accelerating the development of potentially lifesaving new devices.

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          Most cited references41

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          Monolithic microfabricated valves and pumps by multilayer soft lithography.

          Soft lithography is an alternative to silicon-based micromachining that uses replica molding of nontraditional elastomeric materials to fabricate stamps and microfluidic channels. We describe here an extension to the soft lithography paradigm, multilayer soft lithography, with which devices consisting of multiple layers may be fabricated from soft materials. We used this technique to build active microfluidic systems containing on-off valves, switching valves, and pumps entirely out of elastomer. The softness of these materials allows the device areas to be reduced by more than two orders of magnitude compared with silicon-based devices. The other advantages of soft lithography, such as rapid prototyping, ease of fabrication, and biocompatibility, are retained.
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            Miniaturized total chemical analysis systems: A novel concept for chemical sensing

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              Transmissibility of 1918 pandemic influenza

              The 1918 influenza pandemic killed 20–40 million people worldwide 1 , and is seen as a worst-case scenario for pandemic planning. Like other pandemic influenza strains, the 1918 A/H1N1 strain spread extremely rapidly. A measure of transmissibility and of the stringency of control measures required to stop an epidemic is the reproductive number, which is the number of secondary cases produced by each primary case 2 . Here we obtained an estimate of the reproductive number for 1918 influenza by fitting a deterministic SEIR (susceptible-exposed-infectious-recovered) model to pneumonia and influenza death epidemic curves from 45 US cities: the median value is less than three. The estimated proportion of the population with A/H1N1 immunity before September 1918 implies a median basic reproductive number of less than four. These results strongly suggest that the reproductive number for 1918 pandemic influenza is not large relative to many other infectious diseases 2 . In theory, a similar novel influenza subtype could be controlled. But because influenza is frequently transmitted before a specific diagnosis is possible and there is a dearth of global antiviral and vaccine stores, aggressive transmission reducing measures will probably be required. Supplementary information The online version of this article (doi:10.1038/nature03063) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Journal
                0045300
                TCAD
                ITCSDI
                Ieee Transactions on Computer-Aided Design of Integrated Circuits and Systems
                IEEE
                0278-0070
                1937-4151
                October 2021
                10 November 2020
                : 40
                : 10
                : 1971-1984
                Affiliations
                [1] departmentDepartment of Computer Science and Engineering, institutionUniversity of California at Riverside, institutionringgold 8790; Riverside CA 92521 USA
                [2] departmentDepartment of Bioengineering, institutionUniversity of California at Riverside, institutionringgold 8790; Riverside CA 92521 USA
                Article
                10.1109/TCAD.2020.3036836
                8545023
                8bd8a020-1287-4768-b7c6-95bb44811a41
                Copyright @ 2020

                This article is free to access and download, along with rights for full text and data mining, re-use and analysis.

                History
                : 27 May 2020
                : 03 September 2020
                : 17 October 2020
                : 20 September 2021
                Page count
                Figures: 26, Tables: 3, Equations: 464, References: 41, Pages: 14
                Funding
                Funded by: National Science Foundation Division of Computing and Communication Foundations, fundref 10.13039/100000143;
                Award ID: 1423414
                Award ID: 1536026
                Award ID: 1351115
                Funded by: National Science Foundation Division of Industrial Innovation and Partnerships, fundref 10.13039/100000151;
                Award ID: 1640757
                This work was supported in part by the National Science Foundation Division of Computing and Communication Foundations under Grant 1423414, Grant 1536026, and Grant 1351115, and in part by the National Science Foundation Division of Industrial Innovation and Partnerships under Grant 1640757.
                Categories
                Article

                continuous placement,paper microfluidics,placement and routing

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