Evidence map›Paper›PMID 35567008›Full record

ArticlePolymers2022

Graphene Oxide Molecularly Imprinted Polymers as Novel Adsorbents for Solid-Phase Microextraction for Selective Determination of Norfloxacin in the Marine Environment.

Jianlei Chen, Liju Tan, Zhengguo Cui, Keming Qu, Jiangtao Wang

Abstract read
In one paragraph

Article in Polymers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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

5 authors.

Jianlei ChenYellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China.
Liju TanKey Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China.ORCID 0000-0002-7082-6356
Zhengguo CuiYellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China.
Keming QuYellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China.
Jiangtao WangKey Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China.

Funding

National Natural Science Foundation of China 41876078Shandong Provincial Natural Science Foundation of China ZR2018MD016The National Key R&D Program of China 2019YFC140027-02The Postdoctoral Innovative Talents Support Program of Shandong Province SDBX2020015
6 · The paper itself

Abstract

In this study, a novel sample pretreatment strategy of solid-phase microextraction using graphene oxide molecularly imprinted polymers as adsorbents coupled with high-performance liquid chromatography was developed to detect norfloxacin in the marine environment. As a carrier, the imprinted polymers were synthesized by precipitation polymerization with graphene oxide. Compared with graphene oxide non-imprinted polymers, the graphene oxide molecularly imprinted polymers exhibited higher adsorption capacity towards norfloxacin. The synthesized polymeric materials were packed into a molecularly imprinted solid-phase microextraction cartridge, and critical parameters affecting the extraction process were optimized. Under the optimized molecular imprinted solid-phase microextraction condition, the proposed method was applied to the analysis of norfloxacin for seawater and fish with satisfactory recovery (90.1-102.7%) and low relative standard deviation (2.06-5.29%, n = 3). The limit of detection was 0.15 μg L

Indexed as

graphene oxidemarine environmentmolecularly imprinted polymersnorfloxacinsolid-phase microextraction

Identifiers

PMID35567008
PMCPMC9101591

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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.