The majority of binding models that have been applied to molecularly imprinted polymers (MIPs) have been homogeneous models. MIPs, on the other hand, are heterogeneous materials containing binding sites with a wide array of binding affinities and selectivities. Demonstrated is that the binding behavior of MIPs can be accurately modeled by the heterogeneous Langmuir-Freundlich (LF) isotherm. The applicability of the LF isotherm to MIPs was demonstrated using five representative MIPs from the literature, including both homogeneous and heterogeneous MIPs. Previously, such comparisons required the use of several different binding models and analyses, including the Langmuir model, the Freundlich model, and numerical approximation techniques. In contrast, the LF model enabled direct comparisons of the binding characteristics of MIPs that have very different underlying distributions and were measured under different conditions. The binding parameters can be calculated directly using the LF fitting coefficients that yield a measure of the total number of binding sites, mean binding affinity, and heterogeneity. Alternatively, solution of the Langmuir adsorption integral for the LF model enabled direct calculation of the corresponding affinity spectrum from the LF fitting coefficients from a simple algebraic expression, yielding a measure of the number of binding sites with respect to association constant Finally, the ability of the LF isotherm to model MIPs suggests that a unimodal heterogeneous distribution is an accurate approximation of the distribution found in homogeneous and heterogeneous MIPs.

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Molecularly imprinted polymers (MIPs) have been used in a wide range of analytical applications in particular in chromatography and sensing. However, the binding properties in MIPs are typically measured only in a narrow concentration range, which corresponds to only a subset of the sites in MIPs. This limited analytical window and binding site heterogeneity of MIPs leads to inaccuracies and inconsistencies in the estimation of their binding properties. This has hampered the characterization and optimization of MIPs for analytical applications. In this study, the origins of the molecular imprinting effect were studied using the newly developed Freundlich isotherm-affinity distribution (FIAD) analysis. The analysis is able to readily calculate an affinity distribution for MIPs from the limited analytical window. The FIAD analysis also yields an estimate of number, affinity, and heterogeneity for this subset of binding sites. Consistent with previous studies, MIPs were found to have higher capacities than the corresponding nonimprinted polymers (NIPs). Interestingly, MIPs were also found to be more heterogeneous than NIPs. Examination of variables in the imprinting process including temperature, template concentration, and cross-linking percentages further confirmed these trends. Based on these observations, a model for the imprinting effect was developed. The larger population of high-affinity sites in MIPs appears to arise from a broadening of the heterogeneous distribution. This suggests that noncovalent MIPs may be ill-suited for chromatographic applications and other applications that are detrimentally affected by binding site heterogeneity and better suited to applications that are less affected by heterogeneity such as sensing.

Reported is the first affinity spectrum (AS) [number of binding sites (N) vs. association constant (K)] for a non-covalently imprinted polymer. The AS method yields the distribution of sites over a continuous range of binding constants and characterizes the heterogeneity present in imprinted polymers better than current methodologies. To demonstrate the generality of the AS method, the distributions for three different imprinted polymers (two of which were taken from the literature) were calculated from their respective binding isotherms. The shapes of the distribution curves were different yet consistent with the respective covalent or non-covalent imprinting mechanisms. Finally, the binding parameters derived from the AS method were compared with those determined by the more common Scatchard analysis and were in general agreement.