Nano Science and Technology Institute
Nanotech 2006 Vol. 2
Nanotech 2006 Vol. 2
Technical Proceedings of the 2006 NSTI Nanotechnology Conference and Trade Show, Volume 2
 
Chapter 8: Micro & Nano Fluidics
 

Computationally Efficient Models of Flow-through Affinity-based Assays

Authors:D.K. Peterson, W.H. Wright and D.B. MacQueen
Affilation:SRI International, US
Pages:581 - 584
Keywords:lateral flow device, convex modeling, optimization
Abstract:Lateral flow “strips” are a common assay format used in a variety of applications, including home pregnancy testing, clinical point-of-care screening for HIV, agribusiness surety against pathogens, and military bioagent detection. Current lateral flow assays are fabricated with a porous wick material, such as nitrocellulose paper. The random microstructure of these substrates presents difficulties for assay optimization and causes undesirably large coefficients of variation in assay results.
We have developed computationally efficient models of a novel lateral flow substrate that enable simulation, analysis, and rational optimization of an assay’s performance. These models represent the phenomenology of micro- and nano-scale physico-chemical processes, including flow through the microstructures, surface binding of analytes and reporters, and optical transduction mechanisms, as well as manufacturing and cost constraints. The models are mathematically convex, allowing efficient optimization by convex programming algorithms.
We present our models for lateral flow substrates and an optimal design tradeoff study, based on these models. Presently, the models are derived from fundamental physical parameters (e.g., fluid viscosity). It is possible (and desirable) to augment the models with laboratory measurements (e.g., observed scaling laws); however, laboratory validation of the models, simulations, and optimization predictions is yet to be done.
Computationally Efficient Models of Flow-through Affinity-based AssaysView PDF of paper
ISBN:0-9767985-7-3
Pages:893
Hardcopy:$119.95
 
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