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1. Quantitative Structure Retention Relationships to Identify Per-/Poly-Fluoroalkyl Substances and Other Contaminants of Emerging Concern

   Simpson, S.; Antle, J.l Halwatura; LaRock, M.; Dickman, R.; Aga, D. (May 2022, Annual meeting abstracts)

   Nontarget analysis using liquid chromatography–high resolution mass spectrometry (LC–HRMS) is a valuable approach in characterizing for contaminants of emerging concern (CECs) in the environment. However, identification of these analytes can be quite costly or taxing without proper analytical standards. To circumvent this problem we utilize Quantitative structure-retention relationships (QSRR) models to predict elution order and retention times. Properties calculated from density functional theory (DFT) and the conductor-like screening model for real solvents (COSMO-RS) theory are used to produce our QSRR models, which can be calculated for virtually any analyte. We show that this methodology has been successful in identification of per- /poly-fluoroalkyl substances (PFAS) and other contaminants. Nontarget analysis using liquid chromatography– high resolution mass spectrometry (LC–HRMS) is a valuable approach in characterizing for contaminants of emerging concern (CECs) in the environment. However, identification of these analytes can be quite costly or taxing without proper analytical standards. To circumvent this problem we utilize Quantitative structureretention relationships (QSRR) models to predict elution order and retention times. Properties calculated from density functional theory (DFT) and the conductor-like screening model for real solvents (COSMO-RS) theory are used to produce our QSRR models, which can be calculated for virtually any analyte. We show that this methodology has been 

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2. Sensing Gas Mixtures by Analyzing the Spatiotemporal Optical Responses of Liquid Crystals Using 3D Convolutional Neural Networks

   https://doi.org/10.1021/acssensors.2c00362  

   Bao, Nanqi; Jiang, Shengli; Smith, Alexander; Schauer, James J.; Mavrikakis, Manos; Van Lehn, Reid C.; Zavala, Victor M.; Abbott, Nicholas L. (August 2022, ACS Sensors)

   We report how analysis of the spatial and temporal optical responses of liquid crystal (LC) films to targeted gases, when per-formed using a machine learning methodology, can advance the sensing of gas mixtures and provide important insights into the physical processes that underlie the sensor response. We develop the methodology using O3 and Cl2 mixtures (representative of an important class of analytes) and LCs supported on metal perchlorate-decorated surfaces as a model system. Whereas O3 and Cl2¬ both diffuse through LC films and undergo redox reactions with the supporting metal perchlorate surfaces to generate similar ini-tial and final optical states of the LCs, we show that a 3-dimensional convolutional neural network (3D CNN) can extract feature information that is encoded in the spatiotemporal color patterns of the LCs to detect the presence of both O3 and Cl2 species in mixtures as well as to quantify their concentrations. Our analysis reveals that O3 detection is driven by the transition time over which the brightness of the LC changes, while Cl2 detection is driven by color fluctuations that develop late in the optical response of the LC. We also show that we can detect the presence of Cl2 even when the concentration of O3 is orders of magnitude greater than the Cl2 concentration. The proposed methodology is generalizable to a wide range of analytes, reactive surfaces and LCs, and has the potential to advance the design of portable LC monitoring devices (e.g., wearable devices) for analyzing gas mixtures us-ing spatiotemporal color fluctuations. 

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3. Benchmarking the accuracy of higher-order particle methods in geodynamic models of transient flow

   https://doi.org/10.5194/gmd-17-4115-2024  

   Gassmöller, Rene; Dannberg, Juliane; Bangerth, Wolfgang; Puckett, Elbridge Gerry; Thieulot, Cedric (January 2024, Geoscientific Model Development)

   Abstract. Numerical models are a powerful tool for investigating the dynamic processes in the interior of the Earth and other planets, but the reliability and predictive power of these discretized models depends on the numerical method as well as an accurate representation of material properties in space and time. In the specific context of geodynamic models, particle methods have been applied extensively because of their suitability for advection-dominated processes and have been used in applications such as tracking the composition of solid rock and melt in the Earth's mantle, fluids in lithospheric- and crustal-scale models, light elements in the liquid core, and deformation properties like accumulated finite strain or mineral grain size, along with many applications outside the Earth sciences. There have been significant benchmarking efforts to measure the accuracy and convergence behavior of particle methods, but these efforts have largely been limited to instantaneous solutions, or time-dependent models without analytical solutions. As a consequence, there is little understanding about the interplay of particle advection errors and errors introduced in the solution of the underlying transient, nonlinear flow equations. To address these limitations, we present two new dynamic benchmarks for transient Stokes flow with analytical solutions that allow us to quantify the accuracy of various advection methods in nonlinear flow. We use these benchmarks to measure the accuracy of our particle algorithm as implemented in the ASPECT geodynamic modeling software against commonly employed field methods and analytical solutions. In particular, we quantify if an algorithm that is higher-order accurate in time will allow for better overall model accuracy and verify that our algorithm reaches its intended optimal convergence rate. We then document that the observed increased accuracy of higher-order algorithms matters for geodynamic applications with an example of modeling small-scale convection underneath an oceanic plate and show that the predicted place and time of onset of small-scale convection depends significantly on the chosen particle advection method. Descriptions and implementations of our benchmarks are openly available and can be used to verify other advection algorithms. The availability of accurate, scalable, and efficient particle methods as part of the widely used open-source code ASPECT will allow geodynamicists to investigate complex time-dependent geodynamic processes such as elastic deformation, anisotropic fabric development, melt generation and migration, and grain damage. 

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4. Redefining Spectral Data Analysis with Immersive Analytics: Exploring Domain-Shifted Model Spaces for Optimal Model Selection

   https://doi.org/10.1177/00037028241280669  

   Peper, Jordan_MJ; Kalivas, John_H (September 2024, Applied Spectroscopy)

   Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human–computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems. 

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5. Efficient Low-Cost Procedure for Microextraction of Estrogen from Environmental Water Using Magnetic Ionic Liquids

   https://doi.org/10.3390/molecules26010032  

   Berton, Paula; Siraj, Noureen; Das, Susmita; de Rooy, Sergio; Wuilloud, Rodolfo G.; Warner, Isiah M. (January 2021, Molecules)

   null (Ed.)

   In this study, three magnetic ionic liquids (MILs) were investigated for extraction of four estrogens, i.e., estrone (E1), estradiol (E2), estriol (E3), and ethinylestradiol (EE2), from environmental water. The cation trihexyl(tetradecyl)phosphonium ([P66614]+), selected to confer hydrophobicity to the resulting MIL, was combined with tetrachloroferrate(III), ferricyanide, and dysprosium thiocyanate to yield ([P66614][FeCl4]), ([P66614]3[Fe(CN)6]), and ([P66614]5[Dy(SCN)8]), respectively. After evaluation of various strategies to develop a liquid–liquid microextraction technique based on synthesized MILs, we placed the MILs onto a magnetic stir bar and used them as extracting solvents. After extraction, the MIL-enriched phase was dissolved in methanol and injected into an HPLC–UV for qualitative and quantitative analysis. An experimental design was used to simultaneously evaluate the effect of select variables and optimization of extraction conditions to maximize the recovery of the analytes. Under optimum conditions, limits of detection were in the range of 0.2 (for E3 and E2) and 0.5 μg L−1 (for E1), and calibration curves exhibited linearity in the range of 1–1000 μg L−1 with correlation coefficients higher than 0.998. The percent relative standard deviation (RSD) was below 5.0%. Finally, this method was used to determine concentration of estrogens in real lake and sewage water samples. 

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