Surface Enhanced Raman Spectroscopy, also known as SERS, is a technology that has developed into an effective, rapid, accurate, and sensitive method for the detection and analysis of any analyte at the ultra-trace level. This is accomplished by the enhancement of the Raman signal. The SERS technique allows for the identification of very low quantities of Raman-active analytes by coupling electromagnetic radiation at optical frequency. There is a substantial amount of interest in the study of the design and development of SERS-active substrates due to the fact that these substrates are of crucial importance in the process of Raman signal amplification. In this study, we have demonstrated EBL fabricated, Au-gratings nanopattern as SERS-active substrate for detection of Rhodamine 6G and a bio-molecule urea up to 10-9 M concentration. NIR laser source (λ=785nm) was used to collect Raman spectra using Raman set up (Renishaw). As part of normal bodily functions, the liver releases urea into the blood, which is transported to the kidneys and expelled as urine, serving as a biomarker for kidney disease. Gas chromatography and calorimetry can detect low quantities of urea, but they are time-consuming and costly. As a low-cross-section molecule, urea can be adsorbed on noble-metal NPs and detected at trace amounts using SERS. There are a number of other possible applications for the SERS-active substrate that has been presented. These applications include in-situ detection of food and water adulterants, narco-analysis etc.
Surface enhanced Raman scattering (SERS), a variant of Raman spectroscopy, is one of the most powerful analytical techniques which can be used to obtain detailed chemical information of molecules or molecular assemblies, with the potential to reach single molecule detection. It is rapid, highly sensitive, accurate and non-destructive detection technique. It finds extensive application in various fields such as: environmental monitoring, biology, defense, forensics etc. It can be observed when target analytes are present in the vicinity of a metallic surface, especially noble metals like Au or Ag, since their plasmonic resonances lie in the visible and NIR regions. We have provided a numerical design of Ag-bullseye structure that function as reproducible SERS probe in the visible frequency band by using FDTD method. The proposed pattern is robust (with respect to different design parameters) with high sensitivity ~ 108, high uniformity and specificity, which assures that such substrate can be used for quantitative analysis, making SERS an indispensable tool for bio-diagnostics and bio-analysis. Surface enhanced Raman scattering (SERS), FDTD, Raman spectroscopy, nano-patterns, Raman enhancement factor (EF).
Detection of proteinuria in urine is an early sign of the progression of cardiovascular and chronic kidney disease. In general, colorimetry detection in urine dipsticks is carried out for a screening of proteinuria. Although low sensitivity and vision assessment has hindered its utility. Smartphone-based colorimetry detection using a urine dipstick plays an important role to estimate the concentration of albumin. Segmentation of albumin patches in urine dipstick is the first step for the automatic estimation of albumin concentration in the urine sample. Here, we present a deep learning-based object detection technique i.e. You Only Look Once (YOLOv5), for the segmentation of albumin patches from urine dipstick (Uristix Siemens) for the estimation of albumin concentration. A comparison between different segmentation techniques to accurately arrive at the region of interest has been demonstrated. Four different smartphones i.e. iPhone SE, Realme C11, Redmi 8A Dual, and Samsung Galaxy M01 were used to capture the images of the urine dipstick, at six different illumination conditions i. e. 500 Lux, 400 Lux, 300 Lux, 200 Lux, 100 Lux, and 50 Lux. The training was done using iPhone SE with eight different albumin concentrations: 10 mg/L, 20 mg/L, 40 mg/L, 80 mg/L, 160 mg/L, 320 mg/L, 640 mg/L, and 1280 mg/L. The proposed model is fast and robust and it helps minimize the effect of different light conditions and color variations across smartphone models.
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