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Accurate detection and analysis of traces of persistent organic pollutants in water is important in many areas, including environmental monitoring and food quality control, due to their long environmental stability and potential bioaccumulation. While conventional analysis of organic pollutants requires expensive equipment, surface enhanced Raman spectroscopy (SERS) has demonstrated great potential for accurate detection of these contaminants. However, SERS analytical difficulties, such as spectral preprocessing, denoising, and substrate-based spectral variation, have hindered widespread use of the technique. Here, we demonstrate an approach for predicting the concentration of sample pollutants from messy, unprocessed Raman data using machine learning. Frequency domain transform methods, including the Fourier and Walsh Hadamard transforms, are applied to sets of Raman spectra of three model micropollutants in water (rhodamine 6G, chlorpyrifos, and triclosan), which are then used to train machine learning algorithms. Using standard machine learning models, the concentration of sample pollutants are predicted with more than 80 percent cross-validation accuracy from raw Raman data. cross-validation accuracy of 85 percent was achieved using deep learning for a moderately sized dataset (100 spectra), and 70 to 80 percent cross-validation accuracy was achieved even for very small datasets (50 spectra). Additionally, standard models were shown to accurately identify characteristic peaks via analysis of their importance scores. The approach shown here has the potential to be applied to facilitate accurate detection and analysis of persistent organic pollutants by surface-enhanced Raman spectroscopy.

Liquid-liquid extraction based on surface nanodroplets, namely nanoextraction, can continuously extract and enrich target analytes from the flow of a sample solution. This sample preconcentration technique is easy to operate in a continuous flow system with low organic solvent consumption and a high enrichment factor. In this review, the evolution from single drop microextraction to advanced nanoextraction will be briefly introduced. Also, the formation principle and key features of surface nanodroplets will be summarized. Further, the major findings of nanoextraction combined with in-droplet chemistry toward sensitive and quantitative detection will be discussed. Finally, we will give our perspectives on the future trend of nanoextraction., Comment: 9 pages, 5 figures, review article

Solvent exchange is a controlled process for dilution-induced phase separation. This work utilizes the solvent exchange method to reveal the effect of the mixing dynamics on the asphaltene precipitation process under 20 different mixing conditions using a model system of n-heptane and asphaltene in toluene. The external mixing between the asphaltene solution and the paraffinic solvent is strictly controlled. We employed a high-spatial-resolution total internal reflection fluorescence microscope to detect asphaltene precipitates with a resolution up to 200 nm. A multiphysics model is used to simulate the evolution of the oversaturation pulse in the solvent exchange process. Based on the simulation results, we predicted the effect of the flow rate, dimension, the orientation of the microfluidic chamber, and temperature on the surface coverage and size distribution of asphaltene precipitates. The model predictions of all factors corroborate with the experimental observations. Local concentration of the solvent and shear forces are found to be the two main reasons for the change of asphaltene precipitation caused by mixing dynamics. However, the influence of thermodynamics is more critical than the mixing dynamics as temperature changes. Through a combination of experimental and simulation studies, this work illuminates the significance of the transportation process for the final morphology of asphaltene precipitates and provides an in-depth insight into the mechanism of mixing dynamics on the asphaltene precipitation. A smart mixing may be to boost new phase formation without excessive solvent consumption., Comment: 34 pages, 13 figures