Evidence map›Paper›PMID 36080014›Full record

ArticleNanomaterials (Basel, Switzerland)2022

Fast Synthesis of Au Nanoparticles on Metal-Phenolic Network for Sweat SERS Analysis.

Xiaoying Zhang, Xin Wang, Mengling Ning, Peng Wang, Wen Wang, Xiaozhou Zhang, Zhiming Liu, Yanjiao Zhang, Shaoxin Li

Open access · goldAbstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 26% of its field
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

4 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. 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

9 authors at 2 institutions in 1 country.

Xiaoying ZhangDepartment of Physical Education, Guangdong Medical University, Dongguan 523808, China.
Xin WangSchool of Medical Technology, Guangdong Medical University, Dongguan 523808, China.
Mengling NingGuangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China.
Peng WangSchool of Medical Technology, Guangdong Medical University, Dongguan 523808, China.
Wen WangSchool of Medical Technology, Guangdong Medical University, Dongguan 523808, China.
Xiaozhou ZhangSchool of Basic Medicine, Guangdong Medical University, Dongguan 523808, China.
Zhiming LiuGuangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China.ORCID 0000-0003-0879-9438
Yanjiao ZhangSchool of Basic Medicine, Guangdong Medical University, Dongguan 523808, China.
Shaoxin LiSchool of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China.
Guangdong Medical College · CNSouth China Normal University · CN

Funding

Natural Science Foundation of Guangdong Province 2019A1515012105 and 2021A1515011733
6 · The paper itself

Abstract

The biochemical composition of sweat is closely related to the human physiological state, which provides a favorable window for the monitoring of human health status, especially for the athlete. Herein, an ultra-simple strategy based on the surface-enhanced Raman scattering (SERS) technique for sweat analysis is established. Metal-phenolic network (MPN), an outstanding organic-inorganic hybrid material, is adopted as the reductant and platform for the in situ formation of Au-MPN, which displays excellent SERS activity with the limit of detection to 10

Indexed as

metal–phenolic networkmolecular fingerprintpHsurface-enhanced Raman scatteringsweat

Identifiers

PMID36080014
PMCPMC9458096
OpenAlexW4293734191

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.