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  1. Fouling at interfaces deteriorates the efficiency and hygiene of processes within numerous industrial sectors, including the oil and gas, biomedical device, and food industries. In the food industry, the fouling of a complex food matrix to a heated stainless steel surface reduces production efficiency by increasing heating resistance, pumping requirements, and the frequency of cleaning operations. In this work, quartz crystal microbalance with dissipation (QCM-D) was used to study the interface formed by the fouling of milk on a stainless steel surface at different flow rates and protein concentrations at high temperatures (135 °C). Subsequently, the QCM-D response was recordedmore »during the cleaning of the foulant. Two phases of fouling were identified. During phase-1, the fouling rate was dependent on the flow rate, while the fouling rate during phase-2 was dependent on the flow rate and protein concentration. During cleaning, foulants deposited at the higher flow rate swelled more than those deposited at the lower flow rate. The composition of the fouling deposits consisted of both protein and mineral species. Two crystalline phases of calcium phosphate, β-tricalcium phosphate and hydroxyapatite, were identified at both flow rates. Stratification in topography was observed across the surface of the QCM-D sensor with a brittle and cracked structure for deposits formed at 0.2 mL/min and a smooth and close-packed structure for deposits formed at 0.1 mL/min. These stratifications in the composition and topography were correlated to differences in the reaction time and flow dynamics at different flow rates. This high-temperature application of QCM-D to complex food systems illuminates the initial interaction between proteins and minerals and a stainless steel surface, which might otherwise be undetectable in low-temperature applications of QCM-D or at larger bench and industrial scales. The methods and results presented here have implications for optimizing processing scenarios that limit fouling formation while also enhancing removal during cleaning.« less
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  4. A large collection of element-wise planar densities for compounds obtained from the Materials Project is calculated using brute force computational geometry methods, where the planar density is given by the total fractional area of atoms intersecting a supercell's crystallographic plane divided by the area of the supercell's crystallographic plane. It is demonstrated that the element-wise maximum lattice plane densities can be useful as machine learning features. The methods described here are implemented in an open-source Mathematica package hosted at
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  6. We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR) (, a Python tool for identifying and assessing high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model. In this work, we create pairwise distance matrices between compounds via Element Mover's Distance (ElMD) and use these to create 2D density-aware embeddings for chemical compositions via Density-preserving Uniform Manifold Approximation and Projection (DensMAP). Because ElMD assigns distances between compounds that are more chemically intuitive than Euclidean-based distances, the compounds can then be clustered into chemically homogeneous clusters viamore »Hierarchical Density-based Spatial Clustering of Applications with Noise (HDBSCAN*). In combination with performance predictions via Compositionally-Restricted Attention-Based Network (CrabNet), we introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multi-objective Pareto front plots and assign a weighted score to each composition that encompasses the trade-off between performance and density-based chemical uniqueness. In addition to density-based metrics, we explore an additional uniqueness proxy related to property gradients in DensMAP space. As a validation study, we use DiSCoVeR to screen materials for both performance and uniqueness to extrapolate to new chemical spaces. Top-10 rankings are provided for the compound-wise density and property gradient uniqueness proxies. Top-ranked compounds can be further curated via literature searches, physics-based simulations, and/or experimental synthesis. Finally, we compare DiSCoVeR against the naive baseline of random search for several parameter combinations in an adaptive design scheme. To our knowledge, this is the first time automated screening has been performed with explicit emphasis on discovering high-performing, novel materials.« less
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