Abstract
Molecularly imprinted polymer (MIP) computational design is expected to become a routine technique prior to synthesis to produce polymers with high affinity and selectivity towards target molecules. Furthermore, using these simulations reduces the cost of optimizing polymerization composition. There are several computational methods used in MIP fabrication and each requires a comprehensive study in order to select a process with results that are most similar to properties exhibited by polymers synthesized through laboratory experiments. Until now, no review has linked computational strategies with experimental results, which are needed to determine the method that is most appropriate for use in designing MIP with high molecular recognition. This review will present an update of the computational approaches started from 2016 until now on quantum mechanics, molecular mechanics and molecular dynamics that have been widely used. It will also discuss the linear correlation between computational results and the polymer performance tests through laboratory experiments to examine to what extent these methods can be relied upon to obtain polymers with high molecular recognition. Based on the literature search, density functional theory (DFT) with various hybrid functions and basis sets is most often used as a theoretical method to provide a shorter MIP manufacturing process as well as good analytical performance as recognition material.
Highlights
A molecularly imprinted polymer (MIP) is a synthetic material that has molecular recognition ability with high affinity and selectivity for a particular molecule through the formation of active sites with the shape, size and pattern of functional groups that are complementary to the template used during synthesis [1,2,3]
This review aims to determine the most efficient computational method in the design of MIPs to achieve a selective material with sensitive analytical performance
There has been excellent correlation between computations and experimental results associated with MIP analytical performance
Summary
A molecularly imprinted polymer (MIP) is a synthetic material that has molecular recognition ability with high affinity and selectivity for a particular molecule (template) through the formation of active sites with the shape, size and pattern of functional groups that are complementary to the template used during synthesis [1,2,3]. Using computers as research tools allows performing a large number of calculations and simulations in order to select the most suitable structure and composition of functional monomer, template, crosslinker, and porogen/solvent [21] The core of such calculations is based on input data related to the chemical composition, the structure at the atomic scale, the distribution of electronic charges leading to dipole formation as well as weak, long range dispersion interactions governed by hydrophobic van der Waals forces [22]. The most extensively applied technique for MIP design is the density functional theory (DFT) approach, denoted by the numerous MIP publications with optimizations that have used it with various hybrid functions and different basis sets [4,33,34,35] These studies that have succeeded in producing polymers with high specificity and selectivity to target compounds have shown a correlation between computational and experimental results. This review aims to determine the most efficient computational method in the design of MIPs to achieve a selective material with sensitive analytical performance
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