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Tunable Electrophoretic Separations Using a Scalable, Fabric-Based Platform
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DOI：10.1021/ac5045127.png)
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There is a rising need for low-cost and scalable platforms for sensitive medical diagnostic testing. Fabric weaving is a mature, scalable manufacturing technology and can be used as a platform to manufacture microfluidic diagnostic tests with controlled, tunable flow. Given its scalability, low manufacturing cost (<$0.25 per device), and potential for patterning multiplexed channel geometries, fabric is a viable platform for the development of analytical devices. In this paper, we describe a fabric-based electrophoretic platform for protein separation. Appropriate yarns were selected for each region of the device and weaved into straight channel electrophoretic chips in a single step. A wide dynamic range of analyte molecules ranging from small molecule dyes (<1 kDa) to macromolecule proteins (67-150 kDa) were separated in the device. Individual yarns behave as a chromatographic medium for electrophoresis. We therefore explored the effect of yarn and fabric parameters on separation resolution. Separation speed and resolution were enhanced by increasing the number of yarns per unit area of fabric and decreasing yarn hydrophilicity. However, for protein analytes that often require hydrophilic, passivated surfaces, these effects need to be properly tuned to achieve well-resolved separations. A fabric device tuned for protein separations was built and demonstrated. As an analytical output parameter for this device, the electrophoretic mobility of a sedimentation marker, Naphthol Blue Black bovine albumin in glycine-NaOH buffer, pH 8.58 was estimated and found to be -2.7 x 10(-8) m-(2) V-1 s(-1). The ability to tune separation may be used to predefine regions in the fabric for successive preconcentrations and separations. The device may then be applied for the multiplexed detection of low abundance proteins from complex biological samples such as serum and cell lysate.
Keyword:
GEL-ELECTROPHORESIS
SYSTEMS
PAPER
PROTEINS
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IF:
6.7
论文数:
4.7W
被引数:
15.9W
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引用论文
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