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The creation of SERS‐active substrates is investigated by self‐assembling hierarchical structures of plasmonic‐assisted nanospheres (HSNs). The hierarchical architecture exploits the advantages of the well‐known ordered configuration of a hexagonally closed‐packed array (CPA) of nanospheres. A further layer of upper nanospheres is used to provide regular and intense surface hotspots, located at the nanogaps between neighboring nanospheres. Numerical analysis is carried out to predict the SERS performances and to identify the more promising configurations, by offering design criteria and a physical insight on the conditions affecting the SERS response of the self‐assembled substrates. Two alternative self‐assembly fabrication methods have been pursued to realize HSNs, namely co‐deposition and sequential deposition. Morphological analysis revealed the formation of well‐ordered hierarchical structures with different ratios between the diameters of the bottom and upper nanospheres. Experimental analysis of the SERS response demonstrates that HSNs can work as cost‐effective SERS substrates with superior performances with respect to the simpler single‐layer CPA configurations. [ABSTRACT FROM AUTHOR]

We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells' Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum. [ABSTRACT FROM AUTHOR]

Fast and efficient water quality monitoring is essential in the pursuit of reducing the impact of human activities on the environment. We address this issue by presenting a sensing system and method based on Raman spectroscopy in liquid-filled capillaries, that enables quantitative measurement of polyatomic anions in solution. We demonstrate quantitative measurement of nitrate concentrations in water via multivariate analysis with partial least squares regression. We achieve a limit of detection of 0.13 millimolar for a measurement time of 30 s. Our Raman method is compared with gravimetrically measured concentration with good agreement and reproducibility. The Raman monitoring method can be performed in a continuous manner, thus suitable for fast continuous monitoring of water and wastewater quality. [ABSTRACT FROM AUTHOR]