Evidence map›Paper›PMID 39404289›Full record

ArticleNanomaterials (Basel, Switzerland)2024

Fabrication of Three-Dimensional Dendritic Ag Nanostructures: A SERS Substrate for Non-Invasive Detection.

Chia-Ling Sung, Tzung-Ta Kao, Yu-Cheng Lin

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chia-Ling SungDepartment of Engineering Science, National Cheng Kung University, Tainan 70101, Taiwan.ORCID 0009-0008-6264-3913
Tzung-Ta KaoInstitute of Photonics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan.
Yu-Cheng LinDepartment of Engineering Science, National Cheng Kung University, Tainan 70101, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper discusses the fabrication of three-dimensional dendritic Ag nanostructures, showcasing pronounced Localized Surface Plasmon Resonance (LSPR) effects. These nanostructures, employed in surface-enhanced Raman scattering (SERS), function as sensors for lactic acid in artificial sweat. The dendritic structures of the silver nanoparticles (AgNPs) create an effective SERS substrate, with additional hotspots at branch junctures enhancing LSPR. We achieve differential LSPR effects by varying the distribution and spacing of branches and the overall morphology. Adjustments to electrodeposition parameters, such as current and plating solution protective agents on an anodized aluminum oxide (AAO) base, allow for precise control over LSPR intensities. By pre-depositing AgNPs, the electron transmission paths during electrodeposition are modified, which leads to optimized dendritic morphology and enhanced LSPR effects. Parameter optimization produces elongated rods with main and secondary branches, covered with uniformly sized, densely packed, non-overlapping spherical AgNPs. This configuration enhances the LSPR effect by generating additional hotspots beyond the branch tips. Fine-tuning the electrodeposition parameters improved the AgNPs' morphology, achieving uniform particle distribution and optimal spacing. Compared to non-SERS substrates, our structure amplified the Raman signal for lactic acid detection by five orders of magnitude. This method can effectively tailor SERS substrates for specific analytes and laser-based detection.

Indexed as

Ag nanodendriteslactic acidlocalized surface plasmon resonance (LSPR)SERS substratessurface-enhanced Raman scattering (SERS)

Identifiers

PMID39404289
PMCPMC11477888

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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.