Synthetic intelligent polymeric materials that can recognize biomolecules have a tremendous potential in micro/nano scale applications such as sensors, biomolecular valves, actuators and point of care diagnostics. Non-covalent complexation between template or 慻uest' biomolecules and functional monomers during polymerization can create networks with selective binding sites. The concept of macromolecular recognition manifests itself from two major synergistic effects, (i) shape specific cavities that match the template biomolecule, which provide stabilization of the chemistry in a crosslinked matrix, and (ii) chemical groups oriented to form multiple complexation points with the template . Highly crosslinking imprinted polymers are extremely stable in a wide range of temperatures and pHs making them great candidates for incorporation onto sensor based platforms, such as surface plasmon resonance (SPR), microcantilevers, or quartz crystal microbalances to function as a biomolecular sensor. However, in order to maximize sensor affinity, selectivity and response time, analysis of parameters such as the crosslinking density of the network, the length of the crosslinking monomer, functional monomer to template ratio, polymerization reaction and kinetics, double bond conversion and the reaction rate versus time from the bulk material are needed. In this work, biorecognitive polymeric networks that are selective to testosterone were prepared and analyzed using crosslinking molecules differing in rank, size, and concentration to optimize the binding characteristics of the network for sensing applications. The crosslinking percentage of the testosterone recognitive gel was varied from 30%- 90% and experienced not only an increase of affinity ranging from 0 x 104 M-1 to 7 x 104 M-1 but also a decrease in the final double bond conversion ranging from 73% to 22%, respectively. Selectivity studies indicated that at a crosslinking percentage of 77%, the testosterone recognitive polymer had a higher affinity for testosterone (5 x 104 M-1) than that of progesterone (1 x104 M-1).
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