RESUMO
The resonant mirror sensor is a planar optical sensor platform that uses frustrated total internal reflection to couple light into and out of a leaky waveguiding layer. The evanescent wave associated with the dielectric structure is very sensitive to changes in surface refractive index caused by the binding of macromolecules to immobilised proteins or other biorecognition species such as antibodies. However, such variations can also be generated by variations in the bulk analyte solution, via changes in the composition or temperature. In the device described here, an additional buried resonant mirror layer is incorporated into the sensor structure generating an internal reference resonant mirror. The efficacy of this internal reference system is demonstrated in both chemical and immunological systems--as a pH sensor monitoring the absorption of an encapsulated sulfonephthalein dye, and as a refractive index sensor measuring the adsorption of anti-protein A and binding of its corresponding antigen. In both cases the internally referenced resonant mirror provides a means by which errors due to fluctuations in light intensity, temperature and bulk composition may be accounted for.
Assuntos
Ressonância de Plasmônio de Superfície/instrumentação , Adsorção , Anticorpos/análise , Anticorpos/imunologia , Reações Antígeno-Anticorpo , Técnicas Biossensoriais , Proteína Estafilocócica A/imunologia , Ressonância de Plasmônio de Superfície/métodosRESUMO
Anti-resonant reflecting optical waveguides (ARROW) are described which trap light in a low index layer between a lower, high-index confining layer and an upper total internal reflection boundary. In this configuration, most of the light (greater than 80%) travels in the low index porous polymer layer, the refractive index of which is monitored by examining the angle at which light is coupled out of the waveguide. It is shown that asymmetric ARROW sensors can be constructed using conventional chemical vapour deposition and spin-coating techniques and their sensitivity is as predicted by theoretical modelling.