Detection and analysis of rotavirus in clinical stool samples using silver nanoparticle functionalized paper as SERS substrate

Biswas, Sritam; Devi, Yengkhom Damayanti; Sarma, Dipjyoti; Hatiboruah, Diganta; Chamuah, Nabadweep; Namsa, Nima D.; Nath, Pabitra

doi:10.1016/j.saa.2023.122610

Cited by 6 publications

(1 citation statement)

References 52 publications

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“…Though these SERS substrates are sensitive and highly reproducible, they suffer from a high fabrication costs thus, restricting their use to research applications only. Some of the low-cost SERS platforms, like printing grade paper [13][14][15], filter paper [16], Blu-ray DVD [17,18], electrospun nanofibers [19,20], and polymer films [21] offer a reasonably high sensitivity and reproducibility characteristics, and have emerged as alternative platforms to their commercially counterparts. Different plasmonic nanostructures of noble metals like multi-spiked star-shaped silver structures [22], silver nano dendrites [23], T-shaped dimers [24] have also been explored for the development of various low-cost SERS substrates that were used to detect chemical and bio-chemical analytes in complex matrices.…”

Section: Introductionmentioning

confidence: 99%

AuNP decorated aegle marmelos leaf as SERS substrate for trace detection of antibiotics and machine learning based classification

Sarma,

Marak,

Chetia

et al. 2024

Phys. Scr.

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Surface-enhanced Raman spectroscopy (SERS) has emerged as a reliable molecular spectroscopic technique for trace detection of chemical and biological samples. Present study illustrates a new SERS platform which has been obtained through surface adsorption of gold nanoparticles (AuNP) on a microscopically roughened surface of aegle marmelos (AM) leaf. The micro-structured patterns of the AM leaves promote the generation of hotspot regions for the surface deposited AuNPs thus, aids in electromagnetic enhancement for the scattered Raman signals from the sample. For the proposed SERS platform, with rhodamine6G (R6G) as an analyte, the limit of detection (LoD) was found to be 0.88 nM. The applicability of the designed SERS was realized through detection and quantification of two commonly used antibiotics- Ceftriaxone (CEFTR) and Ceftiofur sodium (CEF-Na) residues from cow milk samples. Furthermore, a dimensionality reduction method known as principal component analysis (PCA) and an optimal machine learning-based model were built to categorize the analytes in the milk samples. The suggested machine learning model's classification accuracy was found to be 94%.Keywords: AuNPs, LSPR, Raman scattering, SERS, antibiotics, Ceftiofur sodium, Ceftriaxone, Machine learning

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