Evidence map›Paper›PMID 32429203›Full record

ArticleBiosensors2020

Silver SERS Adenine Sensors with a Very Low Detection Limit.

Yonhua Tzeng, Bo-Yi Lin

Open access · goldAbstract read
In one paragraph

Article in Biosensors, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.4field-weighted citation impact, top 20% 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

11 citing papers in PubMed, 38 citations in OpenAlex.

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  6. The Role of GaN in the Heterostructure WSMaterials (Basel, Switzerland) · 2023
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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

2 authors at 1 institution in 1 country.

Yonhua TzengDepartment of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan.ORCID 0000-0003-4763-1144
Bo-Yi LinDepartment of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
National Cheng Kung University · TW

Funding

Ministry of Science and Technology, Taiwan MOST-106-2221-E-006-173-MY3 and MOST-105-2221-E-006-057-MY3.
6 · The paper itself

Abstract

The detection of adenine molecules at very low concentrations is important for biological and medical research and applications. This paper reports a silver-based surface-enhanced Raman scattering (SERS) sensor with a very low detection limit for adenine molecules. Clusters of closely packed silver nanoparticles on surfaces of discrete ball-like copper bumps partially covered with graphene are deposited by immersion in silver nitrate. These clusters of silver nanoparticles exhibit abundant nanogaps between nanoparticles, where plasmonic coupling induces very high local electromagnetic fields. Silver nanoparticles growing perpendicularly on ball-like copper bumps exhibit surfaces of large curvature, where electromagnetic field enhancement is high. Between discrete ball-like copper bumps, the local electromagnetic field is low. Silver is not deposited on the low-field surface area. Adenine molecules interact with silver by both electrostatic and functional groups and exhibit low surface diffusivity on silver surface. Adenine molecules are less likely to adsorb on low-field sensor surface without silver. Therefore, adenine molecules have a high probability of adsorbing on silver surface of high local electric fields and contribute to the measured Raman scattering signal strength. We demonstrated SERS sensors made of clusters of silver nanoparticles deposited on discrete ball-like copper bumps with very a low detection limit for detecting adenine water solution of a concentration as low as 10

Indexed as

AdenineMetal NanoparticlesMolecular StructureSilverSpectrum Analysis, RamanSurface PropertiesAdenineSilveradeninecoppergrapheneplasmonR6GRaman scatteringSERSsilver

Identifiers

PMID32429203
PMCPMC7277772
OpenAlexW3024251262

What OpenQuestion holds

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LicenceCC BY
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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.