Experimental Characterization Of Electrical Current Leakage In Poly(Dimethylsiloxane) Microfluidic Devices

Poly(dimethylsiloxane) (PDMS) is usually considered as a dielectric material and the PDMS microchannel wall can be treated as an electrically insulated boundary in an applied electric field. However, in certain layouts of microfluidic networks, electrical leakage through the PDMS microfluidic channel walls may not be negligible, which must be carefully considered in the microfluidic circuit design. In this paper, we report on the experimental characterization of the electrical leakage current through PDMS microfluidic channel walls of different configurations. Our numerical and experimental studies indicate that for tens of microns thick PDMS channel walls, electrical leakage through the PDMS wall could significantly alter the electrical field in the main channel. We further show that we can use the electrical leakage through the PDMS microfluidic channel wall to control the electrolyte flow inside the microfluidic channel and manipulate the particle motion inside the microfluidic channel. More specifically, we can trap individual particles at different locations inside the microfluidic channel by balancing the electroosmotic flow and the electrophoretic migration of the particle.

A stable PEO-tethered PDMS surface having controllable wetting property by a swelling-deswelling process

A PEO-tethered layer on a PDMS (polydimethylsiloxane) cross-linked network has been prepared by a swelling-deswelling process. During swelling, the PDMS block of a PDMS-b-PEO diblock copolymer penetrates into the PDMS substrate and interacts with PDMS chains because of the van der Waals force and hydrophobic interaction between them. Upon deswelling, the PDMS block is trapped in the PDMS matrix while the PEO, as a hydrophilic block, is tethered to the surface. The PEO-tethered layer showed stability when treated in water for 16 h. The surface fraction of PEO and the wetting property of the PEO-tethered PDMS surface can be controlled by the cross linking density of the PDMS matrix. A patterned PEO-tethered layer on a PDMS network was also created by microcontact printing and water condensation figures (CFs) were used to study the patterned surface with different wetting properties.

Parameters Inversion of Fluid-Saturated Porous Media Based on Neural Networks

The multi-layers feedforward neural network is used for inversion of material constants of fluid-saturated porous media. The direct analysis of fluid-saturated porous media is carried out with the boundary element method. The dynamic displacement responses obtained from direct analysis for prescribed material parameters constitute the sample sets training neural network. By virtue of the effective L-M training algorithm and the Tikhonov regularization method as well as the GCV method for an appropriate selection of regularization parameter, the inverse mapping from dynamic displacement responses to material constants is performed. Numerical examples demonstrate the validity of the neural network method.

A Label-Free Protein Microfluidic Array for Parallel Immunoassays

A label-free protein microfluidic array for immunoassays based on the combination of imaging ellipsometry and an integrated microfluidic system is presented. Proteins can be patterned homogeneously on substrate in array format by the microfluidic system simultaneously. After preparation, the protein array can be packed in the microfluidic system which is full of buffer so that proteins are not exposed to denaturing conditions. With simple microfluidic channel junction, the protein microfluidic array can be used in serial or parallel format to analyze single or multiple samples simultaneously. Imaging ellipsometry is used for the protein array reading with a label-free format. The biological and medical applications of the label-free protein microfluidic array are demonstrated by screening for antibody–antigen interactions, measuring the concentration of the protein solution and detecting five markers of hepatitis B.

Simultaneous Laser-Induced Fluorescence And Contactless-Conductivity Detection For Microfluidic Chip

A combined detection system involving simultaneous LIF and contactless-conductometric measurements at the same place of the microfluidic chip was described. The LIF measurement was designed according to the confocal principle and a moveable contactless-conductivity detector was used in (CD)-D-4. Both measurements were mutually independent and advantageous in analyses of mixtures. Various experimental parameters affecting the response were examined and optimized. The performances were demonstrated by simultaneous detection of Rhodamine B. And the results showed that the combined detection system could be used sensitively and reliably. (C) 2008 Yong Yu. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved.

Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

On the formation of well-aligned ZnO nanowall networks by catalyst-free thermal evaporation method

Two-dimensional ZnO nanowall networks were grown on ZnO-coated silicon by thermal evaporation at low temperature without catalysts or additives. All of the results from scanning electronic spectroscope, X-ray diffraction and Raman scattering confirmed that the ZnO nanowalls were vertically aligned and c-axis oriented. The room-temperature photoluminescence spectra showed a dominated UV peak at 378 nm, and a much suppressed orange emission centered at similar to 590 nm. This demonstrates fairly good crystal quality and optical properties of the product. A possible three-step, zinc vapor-controlled process was proposed to explain the growth of well-aligned ZnO nanowall networks. The pre-coated ZnO template layer plays a key role during the synthesis process, which guides the growth direction of the synthesized products. (C) 2007 Elsevier B.V. All rights reserved.

Predicting chaotic time-series with wavelet networks

A new technique, wavelet network, is introduced to predict chaotic time series. By using this technique, firstly, we make accurate short-term predictions of the time series from chaotic attractors. Secondly, we make accurate predictions of the values and bifurcation structures of the time series from dynamical systems whose parameter values are changing with time. Finally we predict chaotic attractors by making long-term predictions based on remarkably few data points, where the correlation dimensions of predicted attractors are calculated and are found to be almost identical to those of actual attractors.

Microfluidic Rheometry

The development and growth of microfluidics has stimulated interest in the behaviour of complex liquids in micro-scale geometries and provided a rich platform for rheometric investigations of non-Newtonian phenomena at small scales. Microfluidic techniques present the rheologist with new opportunities for material property measurement and this review discusses the use of microfluidic devices to measure bulk rheology in both shear and extensional flows. Capillary, stagnation and contraction flows are presented in this context and developments, limitations and future perspectives are examined. (C) 2008 Elsevier Ltd. All rights reserved.

Microfluidic effects of transporting signaling components in cell coculture chips

A theoretical model has been developed to investigate the microfluidic transport of the signaling chemicals in the cell coculture chips. Using an epidermal growth factor (EGF)-like growth factor as the sample chemical, the effects of velocities and channel geometry were studied for the continuous-flow microchannel bioreactors. It is found that different perfusion velocities must be applied in the parallel channels to facilitate the communication, i.e., transport of the signaling component, between the coculture channels. Such communication occurs in a unidirectional way because the signaling chemicals can only flow from the high velocity area to the low velocity area. Moreover, the effect of the transport of the signaling component between the coculture channels on the growth of the monolayer cells and the multicellular tumor spheroid (MTS) in the continuous-flow coculture environment were simulated using 3D models. The numerical results demonstrated that the concentration gradients will induce the heterogeneous growth of the cells and the MTSs, which should be taken into account in designing the continuous-flow perfusion bioreactor for the cell coculture research.

Developing A Microfluidic-Based System To Quantify Cell Capture Efficiency

Micro-fabrication technology has substantial potential for identifying molecular markers expressed on the surfaces of tissue cells and viruses. It has been found in several conceptual prototypes that cells with such markers are able to be captured by their antibodies immobilized on microchannel substrates and unbound cells are flushed out by a driven flow. The feasibility and reliability of such a microfluidic-based assay, however, remains to be further tested. In the current work, we developed a microfluidic-based system consisting of a microfluidic chip, an image grabbing unit, data acquisition and analysis software, as well as a supporting base. Specific binding of CD59-expressed or BSA-coupled human red blood cells (RBCs) to anti-CD59 or anti-BSA antibody-immobilized chip surfaces was quantified by capture efficiency and by the fraction of bound cells. Impacts of respective flow rate, cell concentration, antibody concentration and site density were tested systematically. The measured data indicated that the assay was robust. The robustness was further confirmed by capture efficiencies measured from an independent ELISA-based cell binding assay. These results demonstrated that the system developed provided a new platform to effectively quantify cellular surface markers effectively, which promoted the potential applications in both biological studies and clinical diagnoses.

Fabrication of microfluidic optical waveguides on glass chips with femtosecond laser pulses

We describe the fabrication of microfluidic channel structures on the surface of a borosilicate glass slide by femtosecond laser direct writing for optical waveguide application. Liquid with a variable refractive index is fed into the microchannel, serving as the core of the waveguide. We demonstrate that either a multimode or a single-mode waveguide can be achieved by controlling the refractive index of the liquid. (C) 2007 Optical Society of America

Optical interconnection for neural networks by use of a self-imaging function

A grating-lens combination unit is developed to form a scaling self-transform function that can self-image on scale. Then an array of many such grating-lens units is used for the optical interconnection of a two-dimensional neural network, and experiments are carried out. We find that our idea is feasible, the optical interconnection system is simple, and optical adjustment is easy. (C) 1998 Optical Society of America.