Evidence map›Paper›PMID 38690106›Full record

ArticleRSC advances2024

SERS detection of dopamine using metal-chelated Ag nanoshell.

Mingyeong Kim, Yun Sik Choi, Dae Hong Jeong

Open access · goldAbstract read
In one paragraph

Article in RSC advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

2 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. 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

3 authors at 1 institution in 1 country.

Mingyeong KimDepartment of Chemistry Education, Seoul National University Seoul 08826 Republic of Korea jeongdh@snu.ac.kr.
Yun Sik ChoiDepartment of Chemistry Education, Seoul National University Seoul 08826 Republic of Korea jeongdh@snu.ac.kr.
Dae Hong JeongDepartment of Chemistry Education, Seoul National University Seoul 08826 Republic of Korea jeongdh@snu.ac.kr.ORCID https://orcid.org/0000-0001-7869-5648
Seoul National University of Education · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the concentrations of different neurotransmitters can indicate the presence of certain disorders affecting brain functions, quantitative analyses of neurotransmitters have attracted increasing attention in various fields. Surface-enhanced Raman scattering (SERS) spectroscopy is an outstanding spectroscopic analytical tool that enables detection at the single molecule level with high specificity. As local field enhancement of surface plasmon is effective within nanometers, active interaction between SERS-active noble metals (gold and silver) and analyte molecules enhances the molecular detection capacity of SERS. However, neurotransmitters and noble metal nanoparticles are often not affinitive, because neurotransmitters generally have a hydroxyl group rather than a thiol group. As a result, the interaction between the two typically remains inactive, which makes detection more difficult. To overcome this limitation, in the present work we utilized metal-chelation to attract dopamine, a neurotransmitter molecule, close to the surface of silver nanoparticles. AgNS was capped with poly(vinyl alcohol) (PVA) and sequentially integrated with copper ion to bind dopamine in the form of chelate bonding between dopamine and copper. The PVA linked AgNS and metal ions through a coordinate bond between hydroxyl groups and metal ions. This metal-chelation-functionalized nanoprobe allowed us to stably detect dopamine in aqueous solution at a concentration of less than 10

Identifiers

PMID38690106
PMCPMC11060141
OpenAlexW4396533500

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

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.