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3. Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra

Author

Jayaprakash, Vishnu, You, Jae Bem, Kanike, Chiranjeevi, Liu, Jinfeng, McCallum, Christopher, and Zhang, Xuehua

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

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.

Published
2024
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6. Evaporation of a sessile colloidal water-glycerol droplet: Marangoni ring formation

Author

Raju, Lijun Thayyil, Diddens, Christian, Li, Yaxing, Marin, Alvaro, van der Linden, Marjolein N., Zhang, Xuehua, and Lohse, Detlef

Subjects
Physics - Fluid Dynamics
Abstract

The transport and aggregation of particles in suspensions is an important process in many physicochemical and industrial processes. In this work, we study the transport of particles in an evaporating binary droplet. Surprisingly, the accumulation of particles occurs not only at the contact line (due to the coffee-stain effect) or at the solid substrate (due to sedimentation), but also at a particular radial position near the liquid-air interface, forming a "ring", which we term as the Marangoni ring. The formation of this ring is primarily attributed to the solutal Marangoni flow triggered by the evaporation dynamics of the water-glycerol droplet. Experiments and simulations show fair agreement in the volume evolution and the general structure of the solutal Marangoni flow, that is, the Marangoni vortex. Experiments show that the location of the Marangoni ring is strongly correlated with the Marangoni vortex. However, finite element numerical simulations fail to describe the particle distribution seen in the experiments. Interestingly, the particles not only accumulate to form the Marangoni ring but also assemble as colloidal crystals close to the liquid-air interface, yielding iridescence. The formation of the colloidal crystals in the experiments is strong evidence that non-hydrodynamic interactions, which are not represented in the simulations, also play a significant role in our system.

Published
2022
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