Evidence map›Paper›PMID 39316462›Full record

ArticleACS applied materials & interfaces2024

Tuning Nanographene-Enhanced Raman Scattering for Rapid Label-Free Detection of Amino Acids.

Neha Sharma, Muhammad Hussnain Akmal, Ryoto Yura, Seyyed Mojtaba Mousavi, Darwin Kurniawan, Yoshiyuki Nonoguchi, Wei-Hung Chiang

Abstract read
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In one paragraph

Article in ACS applied materials & interfaces, 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
–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

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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.

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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

7 authors.

Neha SharmaDepartment of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
Muhammad Hussnain AkmalDepartment of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
Ryoto YuraFaculty of Materials Science and Engineering, Kyoto Institute of Technology, Kyoto 606-8585, Japan.
Seyyed Mojtaba MousaviDepartment of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
Darwin KurniawanDepartment of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.ORCID 0000-0003-4198-7641
Yoshiyuki NonoguchiFaculty of Materials Science and Engineering, Kyoto Institute of Technology, Kyoto 606-8585, Japan.ORCID 0000-0002-4246-284X
Wei-Hung ChiangDepartment of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.ORCID 0000-0002-6350-6696

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid and sensitive detection of amino acids is important not only for fundamental studies but also for the establishment of a healthy society. However, conventional detection methods have been hampered by the difficulties of low sensitivity, long sampling and detection times, and expensive operation and instruments. Here, we report the plasma engineering of bioresource-derived graphene quantum dots (GQDs) as surface-enhanced Raman scattering (SERS)-active materials for the rapid and sensitive detection of amino acids. Surface-functionalized GQDs with tuned structures and band gaps were synthesized from earth-abundant bioresources by using reactive microplasmas under ambient conditions. Detailed microscopy and spectroscopy studies indicate that the SERS properties of the synthesized GQDs can be tuned by controlling the band gaps of synthesized GQDs. The plasma-synthesized metal-free GQDs with surface functionalities showed improved SERS properties for rapid amino acid detection with low detection limits of 10

Indexed as

Amino AcidsGraphiteQuantum DotsSpectrum Analysis, RamanLimit of DetectionAmino AcidsGraphiteamino acidsband gap tuningbiosensorsgraphene quantum dotsplasmasRaman scattering

Identifiers

PMID39316462
PMCPMC11472263

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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.