Dendrimer-based magneto-plasmonic nanosorbents for water quality monitoring using surface-enhanced Raman spectroscopy

Surface-enhanced Raman scattering detection of thiram and ciprofloxacin using chitosan-silver coated paper substrates

Fast detection of contaminants of emerging concern (CECs) in water resources is of great environmental interest. Ideally, sustainable materials should be used in water quality monitoring technologies implemented for such purposes. In this regard, the application of bio-based materials aimed at the fabrication of analytical platforms has become of great importance. This research merges both endeavors by exploring the application of chitosan-coated paper, decorated with silver nanoparticles (AgNPs), on surface-enhanced Raman scattering (SERS) spectroscopy studies of two distinct types of CECs dissolved in aqueous samples: an antibiotic (ciprofloxacin) and a pesticide (thiram). Our results indicate the superior SERS performance of biocoated substrates compared to their non-coated paper counterparts. The detection limits achieved for thiram and ciprofloxacin using the biocoated substrates were 0.024 ppm and 7.7 ppm, respectively. The efficient detection of both analytes is interpreted in terms of the role of the biopolymer in promoting AgNPs assemblies that result in local regions of enhanced SERS activity. Taking advantage of these observations, we use confocal Raman microscopy to obtain Raman images of the substrates using ciprofloxacin and thiram as molecular probes. We also demonstrate that these biobased substrates can be promising for on-site analysis when used in conjunction with portable Raman instruments.

Deep metric learning framework combined with Gramian angular difference field image generation for Raman spectra classification based on a handheld Raman spectrometer

Rapid identification of unknown material samples using portable or handheld Raman spectroscopy detection equipment is becoming a common analytical tool. However, the design and implementation of a set of Raman spectroscopy-based devices for substance identification must include spectral sampling of standard reference substance samples, resolution matching between different devices, and the training process of the corresponding classification models. The process of selecting a suitable classification model is frequently time-consuming, and when the number of classes of substances to be recognised increases dramatically, recognition accuracy decreases dramatically. In this paper, we propose a fast classification method for Raman spectra based on deep metric learning networks combined with the Gramian angular difference field (GADF) image generation approach. First, we uniformly convert Raman spectra acquired at different resolutions into GADF images of the same resolution, addressing spectral dimension disparities induced by resolution differences in different Raman spectroscopy detection devices. Second, a network capable of implementing nonlinear distance measurements between GADF images of different classes of substances is designed based on a deep metric learning approach. The Raman spectra of 450 different mineral classes obtained from the RRUFF database were converted into GADF images and used to train this deep metric learning network. Finally, the trained network can be installed on an embedded computing platform and used in conjunction with portable or handheld Raman spectroscopic detection sensors to perform material identification tasks at various scales. A series of experiments demonstrate that our trained deep metric learning network outperforms existing mainstream machine learning models on classification tasks of different sizes. For the two tasks of Raman spectral classification of natural minerals of 260 classes and Raman spectral classification of pathogenic bacteria of 8 classes with significant noise, our suggested model achieved 98.05% and 90.13% classification accuracy, respectively. Finally, we also deployed the model in a handheld Raman spectrometer and conducted identification experiments on 350 samples of chemical substances attributed to 32 classes, achieving a classification accuracy of 99.14%. These results demonstrate that our method can greatly improve the efficiency of developing Raman spectroscopy-based substance detection devices and can be widely used in tasks of unknown substance identification.

Online detection of concentrate grade in the antimony flotation process based on in situ Raman spectroscopy combined with a CNN-GRU hybrid model

Froth flotation is the most critical process for separating stibnite from raw ore. Concentrate grade is a vital production indicator in the antimony flotation process. It is a direct reflection of the product quality of the flotation process and an essential basis for the dynamic adjustment of its operating parameters. Existing methods of measuring concentrate grades suffer from expensive measurement equipment, difficult maintenance of complex sampling systems, and extended testing times. This paper presents a nondestructive and fast methodology to quantify the concentrate grade in the antimony flotation process based on in situ Raman spectroscopy. A particular Raman spectroscopic measuring system is designed for on-line measurement of the Raman spectra of the mixed minerals from the froth layer during the antimony flotation process. To obtain representative Raman spectra that better characterize the concentrate grades, a traditional Raman spectroscopic system has been redesigned to account for the different interferences during actual flotation field acquisition. A one-dimensional convolutional neural network (1D-CNN) is combined with a gated recurrent unit (GRU) and applied to construct a model for online prediction of concentrate grades based on continuously collected Raman spectra of mixed minerals in the froth layer. With an average prediction error of 4.37% and a maximum prediction deviation of 10.56%, the quantitative analysis of concentrate grade by the model demonstrates that our method is distinguished by high accuracy, low deviation, and in situ analysis, and it essentially satisfies the requirements for online quantitative determination of concentrate grade in the antimony flotation site.

Chemical Strategies for Dendritic Magneto-plasmonic Nanostructures Applied to Surface-Enhanced Raman Spectroscopy

Chemical analyses in the field using surface-enhanced Raman scattering (SERS) protocols are expected to be part of several analytical procedures applied to water quality monitoring. To date, these endeavors have been supported by developments in SERS substrate nanofabrication, instrumentation portability, and the internet of things. Here, we report distinct chemical strategies for preparing magneto-plasmonic (Fe₃ O₄  : Au) colloids, which are relevant in the context of trace-level detection of water contaminants due to their inherent multifunctionality. The main objective of this research is to investigate the role of poly(amidoamine) dendrimers (PAMAMs) in the preparation of SERS substrates integrating both functionalities into single nanostructures. Three chemical routes were investigated to design magneto-plasmonic nanostructures that translate into different ways for assessing SERS detection by using distinct interfaces. Hence, a series of magneto-plasmonic colloids have been characterized and then assessed for their SERS activity by using a model pesticide (thiram) dissolved in aqueous samples.