The Rational Design of Recognitive Polymeric Networks for Sensing Applications
Type of Degreedissertation
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Testosterone recognitive networks were synthesized with varying feed crosslinking percentages and length of the bi-functional crosslinking agent to analyze the effect of changing structural parameters on template binding properties such as affinity, selectivity, capacity, and diffusional transport. The crosslinking percentage of the crosslinking monomer ethylene glycol dimethacrylate was varied from 50% to 90% and associated networks experienced a 2 fold increase in capacity and a 4 fold increase in affinity with the equilibrium association constants, Ka, ranging from 0.32 ± 0.02 x 104 M-1 to 1.3 ± 0.1 x 104 M-1, respectively. The higher concentration of crosslinking monomer increased the crosslinking points available for inter-chain stabilization creating an increased number of stable cavities for template association. However, by increasing the length of the crosslinking agent and increasing the feed crosslinking percentage from 77% crosslinked poly(methacrylic acid-co-ethylene glycol dimethacrylate) (poly(MAA-co-EGDMA)) to 50% crosslinked poly(methacrylic acid-co-poly(ethylene glycol)200 dimethacrylate) (poly(MAA-co-PEG200DMA)), the mesh size of the network increased resulting in an increased template diffusion coefficient from (2.83 ± 0.06) x 109 cm2/s to (4.3 ± 0.06) x 109 cm2/s, respectively, which is approximately a 40% faster template diffussional transport. A 77% crosslinked poly (MAA-co-PEG200DMA) recognitive network had an association constant of (0.20 ± 0.05) x 104 M-1 and bound (0.72 ± 0.04) x 10-2 mmol testosterone/g dry polymer, which was less by 6 and 3 fold, respectively, compared to a similarly crosslinked poly(MAA-co-EGDMA) recognitive network. Structural manipulation of the macromolecular architecture illustrates the programmability of recognitive networks for specific template binding parameters and diffusional transport, which may lead to enhanced imprinted sensor materials and successful integration onto sensor platforms.