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  • Association for Computing Machinery (ACM)  (4)
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  • Association for Computing Machinery (ACM)  (4)
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  • 1
    Online Resource
    Online Resource
    Demand-Driven Single- and Multitarget Mixture Preparation Using Digital Microfluidic Biochips
    Association for Computing Machinery (ACM) ; 2018
    In:  ACM Transactions on Design Automation of Electronic Systems Vol. 23, No. 4 ( 2018-07-31), p. 1-26
    Details
    In: ACM Transactions on Design Automation of Electronic Systems, Association for Computing Machinery (ACM), Vol. 23, No. 4 ( 2018-07-31), p. 1-26
    Abstract: Recent studies in algorithmic microfluidics have led to the development of several techniques for automated solution preparation using droplet-based digital microfluidic (DMF) biochips. A major challenge in this direction is to produce a mixture of several reactants with a desired ratio while optimizing reactant cost and preparation time. The sequence of mix-split operations that are to be performed on the droplets is usually represented as a mixing tree (or graph). In this article, we present an efficient mixing algorithm, namely, Mixing Tree with Common Subtrees ( MTCS ), for preparing single-target mixtures. MTCS attempts to best utilize intermediate droplets, which were otherwise wasted, and uses morphing based on permutation of leaf nodes to further reduce the graph size. The technique can be generalized to produce multitarget ratios, and we present another algorithm, namely, Multiple Target Ratios ( MTR ). Additionally, in order to enhance the output load, we also propose an algorithm for droplet streaming called Multitarget Multidemand ( MTMD ). Simulation results on a large set of target ratios show that MTCS can reduce the mean values of the total number of mix-split steps ( T ms ) and waste droplets ( W ) by 16% and 29% over Min-Mix (Thies et al. 2008) and by 22% and 34% over RMA (Roy et al. 2015), respectively. Experimental results also suggest that MTR can reduce the average values of T ms and W by 23% and 44% over the repeated version of Min-Mix , by 30% and 49% over the repeated version of RMA , and by 9% and 22% over the repeated-version of MTCS , respectively. It is observed that MTMD can reduce the mean values of T ms and W by 64% and 85%, respectively, over MTR . Thus, the proposed multitarget techniques MTR and MTMD provide efficient solutions to multidemand, multitarget mixture preparationon a DMF platform.
    Type of Medium: Online Resource
    ISSN: 1084-4309 , 1557-7309
    URL: Article
    DOI: 10.1145/3200903
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2018
    detail.hit.zdb_id: 1501152-5
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  • 2
    Online Resource
    Online Resource
    Theory and analysis of generalized mixing and dilution of biochemical fluids using digital microfluidic biochips
    Association for Computing Machinery (ACM) ; 2014
    In:  ACM Journal on Emerging Technologies in Computing Systems Vol. 11, No. 1 ( 2014-10-06), p. 1-33
    Details
    In: ACM Journal on Emerging Technologies in Computing Systems, Association for Computing Machinery (ACM), Vol. 11, No. 1 ( 2014-10-06), p. 1-33
    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 ( CF ) of 100%. In many real-life scenarios, the stock solutions consist of samples/reagents with multiple 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 CF s not exceeding 1 2d, where the integer d is user specified and denotes the desired accuracy level of CF . Next, an algorithm is proposed which produces the desired target ratio of N reagents in ONd mix-split steps, where 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 CF s. Finally, we present a generalized algorithm for diluting a sample S in minimum (1:1) mix-split steps when two or more arbitrary concentrations of 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.
    Type of Medium: Online Resource
    ISSN: 1550-4832 , 1550-4840
    URL: Article
    DOI: 10.1145/2629578
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2014
    detail.hit.zdb_id: 2193538-5
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  • 3
    Online Resource
    Online Resource
    Congestion-aware layout design for high-throughput digital microfluidic biochips
    Association for Computing Machinery (ACM) ; 2012
    In:  ACM Journal on Emerging Technologies in Computing Systems Vol. 8, No. 3 ( 2012-08), p. 1-23
    Details
    In: ACM Journal on Emerging Technologies in Computing Systems, Association for Computing Machinery (ACM), Vol. 8, No. 3 ( 2012-08), p. 1-23
    Abstract: Potential applications of digital microfluidic (DMF) biochips now include several areas of real-life applications like environmental monitoring, water and air pollutant detection, and food processing to name a few. In order to achieve sufficiently high throughput for these applications, several instances of the same bioassay may be required to be executed concurrently on different samples. As a straightforward implementation, several identical biochips can be integrated on a single substrate as a multichip to execute the assay for various samples concurrently. Controlling individual electrodes of such a chip by independent pins may not be acceptable since it increases the cost of fabrication. Thus, in order to keep the overall pin-count within an acceptable bound, all the respective electrodes of these individual pieces are connected internally underneath the chip so that they can be controlled with a single external control pin. In this article, we present an orientation strategy for layout of a multichip that reduces routing congestion and consequently facilitates wire routing for the electrode array. The electrode structure of the individual pieces of the multichip may be either direct-addressable or pin-constrained. The method also supports a hierarchical approach to wire routing that ensures scalability. In this scheme, the size of the biochip in terms of the total number of electrodes may be increased by a factor of four by increasing the number of routing layers by only one. In general, for a multichip with 4 n identical blocks, ( n + 1) layers are sufficient for wire routing.
    Type of Medium: Online Resource
    ISSN: 1550-4832 , 1550-4840
    URL: Article
    DOI: 10.1145/2287696.2287700
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2012
    detail.hit.zdb_id: 2193538-5
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  • 4
    Online Resource
    Online Resource
    Layout-Aware Mixture Preparation of Biochemical Fluids on Application-Specific Digital Microfluidic Biochips
    Association for Computing Machinery (ACM) ; 2015
    In:  ACM Transactions on Design Automation of Electronic Systems Vol. 20, No. 3 ( 2015-06-24), p. 1-34
    Details
    In: ACM Transactions on Design Automation of Electronic Systems, Association for Computing Machinery (ACM), Vol. 20, No. 3 ( 2015-06-24), p. 1-34
    Abstract: The recent proliferation of digital microfluidic (DMF) biochips has enabled rapid on-chip implementation of many biochemical laboratory assays or protocols. Sample preprocessing, which includes dilution and mixing of reagents, plays an important role in the preparation of assays. The automation of sample preparation on a digital microfluidic platform often mandates the execution of a mixing algorithm, which determines a sequence of droplet mix-split steps (usually represented as a mixing graph). However, the overall cost and performance of on-chip mixture preparation not only depends on the mixing graph but also on the resource allocation and scheduling strategy, for instance, the placement of boundary reservoirs or dispensers, mixer modules, storage units, and physical design of droplet-routing pathways. In this article, we first present a new mixing algorithm based on a number-partitioning technique that determines a layout-aware mixing tree corresponding to a given target ratio of a number of fluids. The mixing graph produced by the proposed method can be implemented on a chip with a fewer number of crossovers among droplet-routing paths as well as with a reduced reservoir-to-mixer transportation distance. Second, we propose a routing-aware resource-allocation scheme that can be used to improve the performance of a given mixing algorithm on a chip layout. The design methodology is evaluated on various test cases to demonstrate its effectiveness in mixture preparation with the help of two representative mixing algorithms. Simulation results show that on average, the proposed scheme can reduce the number of crossovers among droplet-routing paths by 89.7% when used in conjunction with the new mixing algorithm, and by 75.4% when an earlier algorithm [Thies et al. 2008] is used.
    Type of Medium: Online Resource
    ISSN: 1084-4309 , 1557-7309
    URL: Article
    DOI: 10.1145/2714562
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2015
    detail.hit.zdb_id: 1501152-5
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