“…At present, the separation and mixing models can only be used independently of each other as the mixing occurs in continuous flow of samples, and the separation exploits the transient behavior. To combine them for practical integrated LoC simulation requires the use of an injector [35]. Additionally, to achieve the canonical assay described in Fig.…”
Section: Future Workmentioning
confidence: 99%
“…2, a reactor model is needed. A simple reactor model has been assembled with the separation and mixing models described above and injector models from [35] to simulate an integrated immunoassay microchip [25] showing the path to our envisioned LoC design methodology.…”
This paper presents composable behavioral models and a schematic-based simulation methodology to enable topdown design of electrokinetic (EK) lab-on-a-chip (LoC). Complex EK LoCs are shown to be decomposable into a system of elements with simple geometry and specific function. Parameterized and analytical models are developed to describe the electric and biofluidic behavior within each element. Electric and biofluidic pins at element terminals support the communication between adjacent elements in a simulation schematic. An analog hardware description language implementation of the models is used to simulate LoC subsystems for micromixing and electrophoretic separation. Both direct current (dc) and transient analysis can be performed to capture the influence of system topology, element sizes, material properties, and operational parameters on LoC system performance. Accuracy (relative error generally less than 5%) and speedup (> 100×) of the schematic-based simulation methodology are demonstrated by comparison to experimental measurements and continuum numerical simulation. Index Terms-Behavioral model, dispersion, lab-on-a-chip, micromixing, schematic-based simulation, top-down design. NOMENCLATURE Variables A Maximum concentration of species band. c Concentration of species band. c m Channel width-averaged concentration of species band. c p pth moment of the concentration in a longitudinal filament of the species band. C e Electric conductivity of buffer, S/m. d n Mixing concentration coefficients. d m Mixing concentration coefficients at inlet of converging/diverging mixing intersection. D Molecular diffusivity of species, m 2 /s. E Electric field strength, V/cm. h Depth of microchannels, µm. I Electric current through buffer, A. L Length of microchannels, µm. L det Detector path length, µm.
“…At present, the separation and mixing models can only be used independently of each other as the mixing occurs in continuous flow of samples, and the separation exploits the transient behavior. To combine them for practical integrated LoC simulation requires the use of an injector [35]. Additionally, to achieve the canonical assay described in Fig.…”
Section: Future Workmentioning
confidence: 99%
“…2, a reactor model is needed. A simple reactor model has been assembled with the separation and mixing models described above and injector models from [35] to simulate an integrated immunoassay microchip [25] showing the path to our envisioned LoC design methodology.…”
This paper presents composable behavioral models and a schematic-based simulation methodology to enable topdown design of electrokinetic (EK) lab-on-a-chip (LoC). Complex EK LoCs are shown to be decomposable into a system of elements with simple geometry and specific function. Parameterized and analytical models are developed to describe the electric and biofluidic behavior within each element. Electric and biofluidic pins at element terminals support the communication between adjacent elements in a simulation schematic. An analog hardware description language implementation of the models is used to simulate LoC subsystems for micromixing and electrophoretic separation. Both direct current (dc) and transient analysis can be performed to capture the influence of system topology, element sizes, material properties, and operational parameters on LoC system performance. Accuracy (relative error generally less than 5%) and speedup (> 100×) of the schematic-based simulation methodology are demonstrated by comparison to experimental measurements and continuum numerical simulation. Index Terms-Behavioral model, dispersion, lab-on-a-chip, micromixing, schematic-based simulation, top-down design. NOMENCLATURE Variables A Maximum concentration of species band. c Concentration of species band. c m Channel width-averaged concentration of species band. c p pth moment of the concentration in a longitudinal filament of the species band. C e Electric conductivity of buffer, S/m. d n Mixing concentration coefficients. d m Mixing concentration coefficients at inlet of converging/diverging mixing intersection. D Molecular diffusivity of species, m 2 /s. E Electric field strength, V/cm. h Depth of microchannels, µm. I Electric current through buffer, A. L Length of microchannels, µm. L det Detector path length, µm.
“…Skew canceling by complimentary turn pairs is especially noticeable when diffusion can be considered negligible. However, electric field effects within the turn will typically cause incomplete skew canceling and can cause the exiting band to take on a parabolic concentration distribution. 5 Serpentine and spiral topologies. 6 Complimentary turn pair schematic.…”
The ability to manipulate and synthesize chemical species on microchip devices provides access
to a new and exciting field. These technologies have several advantages throughout the industrial
and scientific communities, especially in the emerging areas of biomedical engineering and the
life sciences. Recent advances in microscale mixing, reaction, separation, and fluid handling
have opened new areas in which process systems engineering techniques can be applied. Here
we discuss our initial efforts at creating automatic synthesis methods for the design of microchip-based electrophoretic separation systems that occupy minimal areas. We use piecewise algebraic
and logic models to compare the conflicting design goals of maximum system performance and
minimum device area. We have implemented both heuristic and numerical optimization design
techniques. The long-term goal of our work is the development of methodologies for the design
of complete lab-on-a-chip devices.
Abstract. The simulation of complex LoC (Lab-on-a-Chip) devices is a process that requires solving computationally expensive partial differential equations. An interesting alternative uses artificial neural networks for creating computationally feasible models based on MOR techniques. This paper proposes an approach that uses artificial neural networks for designing LoC components considering the artificial neural network topology as an isomorphism of the LoC device topology. The parameters of the trained neural networks are based on equations for modeling microfluidic circuits, analogous to electronic circuits. The neural networks have been trained to behave like AND, OR, Inverter gates. The parameters of the trained neural networks represent the features of LoC devices that behave as the aforementioned gates. This would mean that LoC devices universally compute.
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