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Porous Hybrids Structure between Silver Nanoparticle and Layered Double Hydroxide for Surface-Enhanced Raman Spectroscopy.

ABSTRACT: Silver nanoparticle (AgNP), in terms of antibacterial, catalytic, electronic, and optical applications, is an attractive material. Especially, when prepared to furnish sharp edge and systematic particle orientation on the substrate, AgNPs can take advantage of surface-enhanced Raman spectroscopy (SERS). In this research, we suggested a synthetic method to immobilize the AgNP on metal oxide by utilizing Ag-thiolate and layered double hydroxide (LDH) as precursor and template, respectively. The layer-by-layer structure of LDH and Ag-thiolate transformed through reductive calcination to metal oxide and AgNP array. Physicochemical characterization, including powder X-ray diffraction, N₂ adsorption-desorption, microscopies, and X-ray photoelectron spectroscopy, revealed that the AgNP with sufficient crystallinity and particle gap was obtained at relatively high calcination temperature, ~600 °C. UV-vis diffusion reflectance spectroscopy showed that the calcination temperature affected particle size and electronic structure of AgNP. The prepared materials were subjected to SERS tests toward 4-nitrothiophenol (4-NTP). The sample obtained at 600 °C exhibited 50 times higher substrate enhancement factor (SEF) than the one obtained at 400 °C, suggesting that the calcination temperature was a determining parameter to enhance SERS activity in current synthetic condition.

SUBMITTER: Lee SB

PROVIDER: S-EPMC7916476 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Porous Hybrids Structure between Silver Nanoparticle and Layered Double Hydroxide for Surface-Enhanced Raman Spectroscopy.

Lee Su-Bin SB Paek Seung-Min SM Oh Jae-Min JM

Nanomaterials (Basel, Switzerland) 20210210 2

Silver nanoparticle (AgNP), in terms of antibacterial, catalytic, electronic, and optical applications, is an attractive material. Especially, when prepared to furnish sharp edge and systematic particle orientation on the substrate, AgNPs can take advantage of surface-enhanced Raman spectroscopy (SERS). In this research, we suggested a synthetic method to immobilize the AgNP on metal oxide by utilizing Ag-thiolate and layered double hydroxide (LDH) as precursor and template, respectively. The la ...[more]

PMID: 33578775

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Project description:Characterizing complex biofluids using surface-enhanced Raman spectroscopy (SERS) coupled with machine learning (ML) has been proposed as a powerful tool for point-of-care detection of clinical disease. ML is well-suited to categorizing otherwise uninterpretable, patient-derived SERS spectra that contain a multitude of low concentration, disease-specific molecular biomarkers among a dense spectral background of biological molecules. However, ML can generate false, non-generalizable models when data sets used for model training are inadequate. It is thus critical to determine how different SERS experimental methodologies and workflow parameters can potentially impact ML disease classification of clinical samples. In this study, a label-free, broadband, Ag nanoparticle-based SERS platform was coupled with ML to assess simulated clinical samples for cardiovascular disease (CVD), containing randomized combinations of five key CVD biomarkers at clinically relevant concentrations in serum. Raman spectra obtained at 532, 633, and 785 nm from up to 300 unique samples were classified into physiological and pathological categories using two standard ML models. Label-free SERS and ML could correctly classify randomized CVD samples with high accuracies of up to 90.0% at 532 nm using as few as 200 training samples. Spectra obtained at 532 nm produced the highest accuracies with no significant increase achieved using multiwavelength SERS. Sample preparation and measurement methodologies (e.g., different SERS substrate lots, sample volumes, sample sizes, and known variations in randomization and experimental handling) were shown to strongly influence the ML classification and could artificially increase classification accuracies by as much as 27%. This detailed investigation into the proper application of ML techniques for CVD classification can lead to improved data set acquisition required for the SERS community, such that ML on labeled and robust SERS data sets can be practically applied for future point-of-care testing in patients.

Dataset Information

Porous Hybrids Structure between Silver Nanoparticle and Layered Double Hydroxide for Surface-Enhanced Raman Spectroscopy.

Publications

Porous Hybrids Structure between Silver Nanoparticle and Layered Double Hydroxide for Surface-Enhanced Raman Spectroscopy.

Similar Datasets

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