Low-cost Surface Enhanced Raman Spectroscopy Research Articles

TinyML-Raman: A novel IoT based field-deployable spectra analysis for accurate identification of pharmaceuticals and trace dye-pesticide mixtures from facile SERS method

BackgroundUpcoming inexpensive, compact Internet of Things (IoT) microcontrollers i.e., tiny-machine learning (TinyML) takes the ML driven Raman spectroscopy one step ahead for realization of more affordable and highly compact field deployable instruments. Further, lack of large spectral datasets and need for numerous specialized SERS substrates impede the development of various ML-based surface enhanced Raman spectroscopy (SERS) applications. The aim is to introduce TinyML analysis on wide range of spectra classes using customized dataset obtained with low-cost SERS. In this regard, it is vital to establish an optimum ML model and efficient data handling methodology for low memory TinyML units. ResultsWe introduce a novel TinyML methodology for accurate classification of large spectra classes with smartphone assistance for data communication and results visualization. To generate large customized spectral dataset, we present a facile, micro-drop SERS using Au colloids and reusable grooved Al substrates. The results demonstrated that memory efficient 8-bit data quantization based convolutional neural network is effective for accurate identification of 22 different spectra classes of trace dye-pesticide mixtures and pharmaceuticals. In this novel quantized data analysis on significantly varied intensity and complex variation spectra classes i.e., many individual, binary-mixtures and some with varied compositions, data normalization is shown to be powerful for improving ML classification accuracy from about 55 % to >99.5 %. Its robustness is demonstrated using inter-instrument driven data variations such as spectral shifts, high noise, and abscissa-flip, with five-fold cross validation of model performance. In addition, on-site quantification of analyte through spectral intensity is also demonstrated. SignificanceThis study opens up a new approach of ML analysis towards realization of next generation field deployable analytical instruments maintaining data privacy. It presents a detailed procedure of quantized spectral data analysis and its implementation in TinyML, attractive for various users and instrument manufacturers. The presented innovative computer-free ML analysis can be employed in all types of spectrometers, meeting the common goal of Raman spectroscopy i.e., accurate identification of complex spectra classes.

FEM investigation of Cu@SiO2 core-shell nanoparticle for a sensitive and low-cost SERS and SEF substrate

We theoretically investigate the plasmon coupling properties of Cu@SiO2 core-shell system that supports the measurement of surface enhanced Raman spectroscopy (SERS) and surface enhanced fluorescence (SEF) technology. The roles of Cu core and SiO2 shell in tuning LSPR have been quantitatively discussed by calculating the localized electric field enhancement factor (EF), fluorescent quantum yield, SERS and SEF EF in the nanogap based on finite element simulations. In this work, we demonstrate that the SERS and SEF effect strongly depend on the core size, shell thickness and dimer distance. The larger size of Cu core, ultrathin thickness of SiO2 shell and shorter dimer distance could be considered as the ideal substrate. Meanwhile, the strong localized excitation enhancement is a vital contribution to fluorescence spectral. By comparison, it is found that the enhancement effect of the Cu, Au and Ag cores are very close in order of magnitude in the red and near infrared regions, and thus the low-cost Cu core may act as an active substrate in these regions. At the excitation wavelength of 630 nm, the optimized core size of 60 nm and shell thickness of 1 nm provide the maximum Raman and fluorescence EF as high as 9 and 4 orders of magnitude. Our theoretical results not only help understanding the underlying enhancement mechanism for the core-shell SERS and SEF effect, but also contribute to develop the low-cost and sensitive detection technology.

Theoretical predictions and experimental verifications of SERS detection in colorants.

Synthetic colorants added during food processing not only fail to provide nutrients, but also can be harmful to human health when used in excess. To establish a simple, convenient, rapid and low-cost surface-enhanced Raman spectroscopy (SERS) detection method for colorants, an active surface-enhanced substrate of colloidal gold nanoparticles (AuNPs) was prepared in this study. The density functional theory (DFT) method of B3LYP with 6-31G(d) was applied to determine the theoretical Raman spectra of erythrosine, basic orange 2, 21 and 22, and to attribute their characteristic spectral peaks. The SERS spectra of the four colorants were pre-processed using local least squares (LLS) and morphological weighted penalized least squares (MWPLS), and multiple linear regression (MLR) models were established to quantify the four colorants in beverages. The results showed that the prepared AuNPs with a particle size of about 50 nm were reproducible and stable, with a good enhancement of the SERS spectrum of rhodamine 6G at 10-8 mol L-1. The theoretical Raman frequencies were in good agreement with the experimental Raman frequencies, and the peak position differences of the main characteristic peaks of the four colorants were within 20 cm-1. The MLR calibration models for the concentrations of the four colorants showed relative errors of prediction (REP) of 2.97-8.96%, root mean square errors of prediction (RMSEP) of 0.03-0.94, R2 of 0.973-0.999, and limits of detection of 0.06 μg mL-1. The present method could be used to quantify erythrosine, basic orange 2, 21, and 22, revealing its wide range of applications in food safety.

Cellulose nanofibre textured SERS substrate

Poor reproducibility limits the wide uptake of low-cost surface enhanced Raman spectroscopy (SERS) substrates. This study reports a relatively low-cost and reproducible cellulose nanofibre (CNF) textured SERS substrate. Utilizing a layer of CNFs deposited onto glass slides, nanoscale roughness was achieved, which facilitated effective aggregation of gold nanoparticles (AuNPs) to form a novel CNF textured SERS substrate. This substrate meets the critical roughness requirements to control the distribution of AuNPs to provide ‘hot spots’ for SERS detection, offering significant signal enhancement. The reproducibility and accuracy of low-cost cellulosic SERS substrates were significantly improved on a model SERS molecule of 4-aminothiophenol.