Evidence map›Paper›PMID 40363129›Full record

ArticleSensors (Basel, Switzerland)2025

Thickness-Tunable PDMS-Based SERS Sensing Substrates.

Diego P Pacherrez Gallardo, Shu Kawamura, Ryo Shoji, Lina Yoshida, Binbin Weng

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

5 authors.

Diego P Pacherrez GallardoSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.ORCID 0009-0007-7945-0651
Shu KawamuraDepartment of Chemical Science and Engineering, National Institute of Technology, Tokyo College, Tokyo 193-0997, Japan.
Ryo ShojiDepartment of Chemical Science and Engineering, National Institute of Technology, Tokyo College, Tokyo 193-0997, Japan.ORCID 0000-0003-4451-7872
Lina YoshidaDepartment of Chemical Science and Engineering, National Institute of Technology, Tokyo College, Tokyo 193-0997, Japan.ORCID 0000-0002-1909-8066
Binbin WengSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.ORCID 0000-0002-5706-5345

Funding

United States Geological Survey G23AP00039-00United States National Science Foundation 2230706
6 · The paper itself

Abstract

Surface-enhanced Raman scattering (SERS) spectroscopy is an ultra-sensitive analytical method with the powerful signal-molecule detection capability. Coupling with the polydimethylsiloxane (PDMS) material, SERS can be enabled on a polymeric substrate for fast-developing bio-compatible sensing applications. However, due to PDMS's high viscosity, conventional PDMS-SERS substrates are typically thick and stiff, limiting their freedom for engineering flexible micro/nano functioning devices. To address this issue, we propose to adopt a low viscosity decamethylcyclopentasiloxane (D5) solvent as a diluent solution. Via controlling the mixture ratio of D5 and PDMS and the spin-coating speed for deposition, this method resulted in a film of a well-defined thickness from sub-millimeter down to a 100 nm scale. Furthermore, thanks to the unsaturated Si-H chemical bonds in the PDMS curing agent, the PDMS film could effectively reduce the Ag+ ions to Ag nanoparticles (NPs) directly bonding onto the substrate surface uniformly. Via adjusting the size and density of the AgNPs through reaction temperature and time, strong SERS was achieved and verified using R6G with the detection limit down to 0.1 ppm, attributed to the AgNPs' plasmonic enhancement effect.

Indexed as

decamethylcyclopentasiloxane (D5)polydimethylsiloxane (PDMS)surface-enhanced Raman scattering (SERS)

Identifiers

PMID40363129
PMCPMC12074034

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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.