Editorial

Molecularly Imprinted Polymers (MIPs) are a form of tailor-made material that can recognise the target compounds/ selectively. Because of its unique qualities such as low cost, resilience, and great selectivity, MIPs have sparked tremendous research interest in solid-phase extraction, catalysis, and sensor applications. MIPs can also be made into composite nanomaterials by combining nanoparticles, multi-walled carbon nanotubes (MWCNTs), nano- rods, quantum dots (QDs), graphene, and clays. The purpose of this review paper is to demonstrate and highlight current advances in the applications of imprinted nanocomposite materials in analytical chemistry. MIPs (molecularly imprinted polymers) are strongly cross-linked, resilient materials with great affinity for target compounds. MIPs are created by polymerizing appropriate functional monomers and a cross-linker agent around the target molecule (template).

MIPs can be utilised effectively in a variety of applications such as separation, catalysis, and sensor systems due to their high affinity and selectivity for the required molecule. MIPs can be manufactured as composite nanomaterials using nanoparticles, in addition to particular molecular recognition abilities towards their target chemical. Solid-phase extraction (SPE) is an effective sample preparation procedure that is commonly used in analytical chemistry. SPE was initially used in the 1940s. Then, in the 1970s, the development of contemporary analytical applications began. Various traditional materials, including as silica-based, carbon- based, and clay-based resins, were frequently employed in SPE applications. Although it is a popular sample preparation technique for enriching or extracting desirable molecules from complex matrices, standard SPE materials employed in analytical applications demonstrate poorer selectivity towards the target molecules, resulting in binding of other compounds. The reaction in electrochemical detection often results in a change in current (amperometric), potential (potentiometric), or conductivity (conductometric). Electrochemical sensors rely heavily on selectivity and sensitivity.

Surface modification of electrodes in electrochemical sensors by immobilisation of recognition components is an effective method for achieving high target molecule binding with good selectivity and responsiveness. Li and colleagues created an electrochemical sensor for metronidazole detection using a nanoporous gold leaf (NPGL) electrode with a selective MIP layer (MNZ). There are three types of MIP-based spectroscopic sensors. MIP-based fluorescence sensors, MIP-based chemiluminescence sensors, and MIP-based SPR sensors are among them. Fluorescence functional monomers are chosen for the development of sensor platforms based on the molecular imprinting process in fluorescence-based molecular recognition of the target chemical. When the target chemical attaches to the sensor, the intensity of the fluorescence increases or decreases depending on the sensor design. Imprinted nanomaterials are also commonly used in biomimetic catalysis as enzyme-like catalysts. To build enzyme-like catalysts using the molecular imprinting approach, appropriate functional monomers are chosen and inserted into the polymeric network by selecting the enzyme substrate (as the template substance) or the transition state analogue (TSA) of the target reaction. After the template is removed from the polymeric network, the imprinted nanomaterial acts as an enzyme-like catalyst in the desired chemical or biological reaction.

The expanding number of published studies using nanostructured composite MIPs for various applications demonstrated that these are attractive materials for selective extraction, sensing, and catalysis. The experiments mentioned in this review highlight current advancements in SPE, sensors, and catalytic systems using nanostructured composite MIPs over the last few years. Composite MIPs at the nanoscale, as promising materials, open up a new avenue for selective SPE and sensors directed at target molecules in complicated matrices.