Evidence map›Paper›PMID 31611473›Full record

ArticleAnalytical sciences : the international journal of the Japan Society for Analytical Chemistry2020

Sensitive Detection of Caffeic Acid and Rutin via the Enhanced Anodic Electrochemiluminescence Signal of Luminol.

Tianlai Xia, Yuan Gao, Ling Zhang, Xinyu Wang, Guangxing Pan, Zhenyuan Wang, Shuang Han, Xing Ma, Weiwei Zhao, Jiaheng Zhang

Abstract read
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In one paragraph

Article in Analytical sciences : the international journal of the Japan Society for Analytical Chemistry, 2020. 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. 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

10 authors.

Tianlai XiaFlexible Printed Electronics Technology Center, Harbin Institute of Technology, Shenzhen, 518055, China.
Yuan GaoFlexible Printed Electronics Technology Center, Harbin Institute of Technology, Shenzhen, 518055, China.
Ling ZhangSchool of Science, Harbin Institute of Technology, Shenzhen, 518055, China. zhangling2018@hit.edu.cn.
Xinyu WangFlexible Printed Electronics Technology Center, Harbin Institute of Technology, Shenzhen, 518055, China.
Guangxing PanFlexible Printed Electronics Technology Center, Harbin Institute of Technology, Shenzhen, 518055, China.
Zhenyuan WangFlexible Printed Electronics Technology Center, Harbin Institute of Technology, Shenzhen, 518055, China.
Shuang HanCollege of Applied Chemistry, Shenyang University of Chemical Technology, Shenyang, 110142, China.
Xing MaFlexible Printed Electronics Technology Center, Harbin Institute of Technology, Shenzhen, 518055, China.
Weiwei ZhaoFlexible Printed Electronics Technology Center, Harbin Institute of Technology, Shenzhen, 518055, China.
Jiaheng ZhangFlexible Printed Electronics Technology Center, Harbin Institute of Technology, Shenzhen, 518055, China. zhangjiaheng@hit.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The electrooxidation of phenolic groups of caffeic acid and rutin promote anodic electrochemiluminescence (ECL) luminol substantially. A sensitive, and cost-effective ECL method has thus been developed to detect caffeic acid, ranging from 0.1 to 5.0 μM, with a detection limit of 0.1 μM and rutin ranging from 0.2 to 25 μM with a detection limit of 0.12 μM. Contrarily, phenolic compounds quench the weak cathodic ECL of luminol. Both of anodic and cathodic ECL mechanisms of luminol in the presence of phenolic compounds are analyzed. The method based on the boomed anodic ECL of luminol is comparable to those based on Ru(bpy)

Indexed as

caffeic acidECLluminolphenolic compoundrutinsensitive detection

Identifiers

PMID31611473

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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.