### Theory and analysis of generalized mixing and dilution of biochemical fluids using digital microfluidic biochipsTheory and analysis of generalized mixing and dilution of biochemical fluids using digital microfluidic biochips

Access Restriction
Subscribed

 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

#### Open content in new tab

Source: ACM Digital Library