Molecularly Imprinted Polymers (MIPs): Bibliometric Analysis

¹ Chemistry Department, Faculty of Mathematics and Natural Science, University of Indonesia, Depok, Indonesia
² Research Center for Chemistry, National Research and Inovation Agency (BRIN), Tangerang Selatan, Indonesia

^* Corresponding author: fitr040@brin.go.id

Abstract

Separation techniques can be applied to sample clean up and preconcentration processes which are key steps in analytical methods to improve the characteristic performance for the separation and detection of various analytes. Selection of the right sorbent with higher selectivity is the main objective of analysis proposed by Molecularly Imprinted Polymers (MIPs). MIPs is a separation technique are prepared by reaction sequences containing a template, one or two functional monomers, one/two crosslinking monomers, polymerization initiator in a solvent containing solution. Various studies on MIPs have been carried out because MIPs have many advantages over other methods, especially as a selective adsorbent for sample preconcentration. The aim of this study is to comprehensively and systematically examine and present bibliometric data analysis using VOSViewer using Scopus article data. it is possible to infer that VOSviewer may be used as a tool for bibliometric data analysis, whereas Publish or Perish can be utilized as a reference management tool for collecting research article data on "molecularly imprinted polymers." The data utilized in this study to analyze data with VOSviewer is research on "molecularly imprinted polymers" from the Scopus database. The total number of publications acquired relevant to the issue was 200 documents spanning the years 2018-2023. Between 2018 and 2023, the amount of research on molecularly imprinted polymers published in Scopus-indexed journals declined. The network visualization depicts the evolution map of molecularly imprinted polymers, which is separated into four clusters. Cluster 1 contains 128 topics, Cluster 2 contains 95 topics, Cluster 3 contains 90 topics, and Cluster 4 contains 73 items. So, the total number of terms in this study is 386 from four clusters.