Evidence map›Paper›PMID 39335883›Full record

ArticleFoods (Basel, Switzerland)2024

Green Synthesis of Molecularly Imprinted Polymers for Selective Extraction of Protocatechuic Acid from Mango Juice.

Liping Zhang, Xin Song, Yuxiao Dong, Xiyan Zhao

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Liping ZhangSchool of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471000, China.
Xin SongSchool of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471000, China.
Yuxiao DongSchool of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471000, China.
Xiyan ZhaoSchool of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471000, China.

Funding

Scientific and Technological Project of Henan Province; Doctoral Startup Foundation of Henan University of Science and Technology;National Key Laboratory of Cotton Bio-breeding and Integrated Utilization Open Fund 242102310569;13480055;CB2024A28
6 · The paper itself

Abstract

A novel and environmentally friendly molecularly imprinted polymer (PCA-MIP) was successfully synthesized in an aqueous solution for the selective extraction of protocatechuic acid (PCA). In this study, a deep eutectic solvent (DES, choline chloride/methacrylic acid, 1:2, mol/mol) and chitosan were employed as the eco-friendly functional monomers. These two components interacted with PCA through hydrogen bonding, integrating a multitude of recognition sites within the PCA-MIP. Thus, the resulting PCA-MIP exhibited outstanding adsorption performance, rapid adsorption rate, and better selectivity, with a maximum binding capacity of 30.56 mg/g and an equilibrium time of 30 min. The scanning electron microscope (SEM) and Brunauer-Emmett-Teller (BET) analyses revealed that the synthesized polymers possessed a uniform morphology and substantial surface areas, which were conducive to their adsorption properties. Moreover, the PCA-MIP integrated with HPLC demonstrated its efficacy as an adsorbent for the selective extraction of PCA from mango juice. The PCA-MIP presented itself as an exemplary adsorbent, offering a highly effective and eco-friendly method for the enrichment of PCA from complex matrices.

Indexed as

chitosandeep eutectic solventgreen synthesismolecularly imprinted polymerprotocatechuic acid

Identifiers

PMID39335883
PMCPMC11431359

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.