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Author Roy, Sudip ♦ Bhattacharya, Bhargab B. ♦ Ghoshal, Sarmishtha ♦ Chakrabarty, Krishnendu
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Biochips ♦ Design automation ♦ Digital microfluidics ♦ Dilution and mixing ♦ Sample preparation
Abstract Digital microfluidic (DMF) biochips are recently being advocated for fast on-chip implementation of biochemical laboratory assays or protocols, and several algorithms for diluting and mixing of reagents have been reported. However, all methods for such automatic sample preparation suffer from a drawback that they assume the availability of input fluids in pure form, that is, each with an extreme concentration factor $(\textit{CF})$ of 100%. In many real-life scenarios, the stock solutions consist of samples/reagents with multiple $\textit{CF}s.$ No algorithm is yet known for preparing a target mixture of fluids with a given ratio when its constituents are supplied with random concentrations. An intriguing question is whether or not a given target ratio is feasible to produce from such a general input condition. In this article, we first study the feasibility properties for the generalized mixing problem under the (1:1) mix-split model with an allowable error in the target $\textit{CF}s$ not exceeding 1 2d, where the integer $\textit{d}$ is user specified and denotes the desired accuracy level of $\textit{CF}.$ Next, an algorithm is proposed which produces the desired target ratio of $\textit{N}$ reagents in ONd mix-split steps, where $\textit{N}$ ( ≥ 3) denotes the number of constituent fluids in the mixture. The feasibility analysis also leads to the characterization of the total space of input stock solutions from which a given target mixture can be derived, and conversely, the space of all target ratios, which are derivable from a given set of input reagents with arbitrary $\textit{CF}s.$ Finally, we present a generalized algorithm for diluting a sample $\textit{S}$ in minimum (1:1) mix-split steps when two or more arbitrary concentrations of $\textit{S}$ (diluted with the same buffer) are supplied as inputs. These results settle several open questions in droplet-based algorithmic microfluidics and offer efficient solutions for a wider class of on-chip sample preparation problems.
ISSN 15504832
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-10-06
Publisher Place New York
e-ISSN 15504840
Journal ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number 11
Issue Number 1
Page Count 33
Starting Page 1
Ending Page 33

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Source: ACM Digital Library