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1. Modelling segregation of bidisperse granular mixtures varying simultaneously in size and density for free surface flows

   https://doi.org/10.1017/jfm.2021.342  

   Duan, Yifei ; Umbanhowar, Paul B. ; Ottino, Julio M. ; Lueptow, Richard M. ( July 2021 , Journal of Fluid Mechanics)

   null (Ed.)

   Flowing granular materials segregate due to differences in particle size (driven by percolation) and density (driven by buoyancy). Modelling the segregation of mixtures of large/heavy particles and small/light particles is challenging due to the opposing effects of the two segregation mechanisms. Using discrete element method (DEM) simulations of combined size and density segregation we show that the segregation velocity is well described by a model that depends linearly on the local shear rate and quadratically on the species concentration for free surface flows. Concentration profiles predicted by incorporating this segregation velocity model into a continuum advection–diffusion–segregation transport model match DEM simulation results well for a wide range of particle size and density ratios. Most surprisingly, the DEM simulations and the segregation velocity model both show that the segregation direction for a range of size and density ratios depends on the local species concentration. This leads to a methodology to determine the combination of particle size ratio, density ratio and particle concentration for which a bidisperse mixture will not segregate. 

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2. Designing non-segregating granular mixtures

   https://doi.org/10.1051/epjconf/202124903011  

   Duan, Yifei ; Umbanhowar, Paul B. ; Lueptow, Richard M. ( January 2021 , EPJ Web of Conferences)

   Aguirre, M.A. ; Luding, S. ; Pugnaloni, L.A. ; Soto, R. (Ed.)

   In dense flowing bidisperse particle mixtures varying in size or density alone, large particles rise (driven by percolation) and heavy particles sink (driven by buoyancy). When the two particle species differ from each other in both size and density, the two segregation mechanisms either enhance (large/light and small/heavy) or oppose (large/heavy and small/light) each other. In the latter case, an equilibrium condition exists in which the two mechanisms balance and the particles no longer segregate. This leads to a methodology to design non-segregating particle mixtures by specifying particle size ratio, density ratio, and mixture concentration to achieve the equilibrium condition. Using DEM simulations of quasi-2D bounded heap flow, we show that segregation is significantly reduced for particle mixtures near the equilibrium condition. In addition, the rise-sink transition for a range of particle size and density ratios matches the predictions of the combined size and density segregation model. 

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3. Designing minimally segregating granular mixtures for gravity‐driven surface flows

   https://doi.org/10.1002/aic.18032  

   Duan, Yifei ; Peckham, Jack ; Umbanhowar, Paul B. ; Ottino, Julio M. ; Lueptow, Richard M. ( January 2023 , AIChE Journal)

   In dense flowing bidisperse particle mixtures varying in size or density alone, smaller particles sink (percolation‐driven) and lighter particles rise (buoyancy‐driven). But when particle species differ from each other in both size and density, percolation and buoyancy can either enhance (large/light and small/heavy) or oppose (large/heavy and small/light) each other. In the latter case, a local equilibrium can exist in which the two mechanisms balance and particles remain mixed: this allows the design of minimally segregating mixtures by specifying particle size ratio, density ratio, and mixture concentration. Using DEM simulations, we show that mixtures specified by the design methodology remain relatively well‐mixed in heap and tumbler flows. Furthermore, minimally segregating mixtures prepared in a fully segregated state in a tumbler mix over time and eventually reach a nearly uniform concentration. Tumbler experiments with large steel and small glass particles validate the DEM simulations and the potential for designing minimally segregating mixtures.

    

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4. Insights on phase formation from thermodynamic calculations and machine learning of 2436 experimentally measured high entropy alloys

   https://doi.org/10.1016/j.jallcom.2022.165173  

   Wang, Chuangye ; Zhong, Wei ; Zhao, Ji-Cheng ( September 2022 , Journal of Alloys and Compounds)

   Both CALPHAD (CALculation of PHAse Diagrams) and machine-learning (ML) approaches were employed to analyze the phase formation in 2,436 experimentally measured high entropy alloy (HEA) compositions consisting of various quinary mixtures of Al, Co, Cr, Cu, Fe, Mn, and Ni. CALPHAD was found to have good capabilities in predicting the BCC/B2 and FCC phase formation for the 1,761 solid-solution-only compositions, excluding HEAs containing an amorphous phase (AM) or/and intermetallic compound (IM). Phase selection rules were examined systematically using several parameters and it revealed that valency electron concentration (VEC) < 6.87 and VEC > 9.16 are the conditions for the formation of single-phase BCC/B2 and FCC, respectively; and CALPHAD could predict this with essentially 100% accuracy. Both CALPHAD predictions and experimental observations show that more BCC/B2 alloys are formed over FCC alloys as the atomic size difference between the elements increases. Four machine learning (ML) algorithms, decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN), were employed to study the phase selection rules for two different datasets, one consisting of 1,761 solid-solution (SS) HEAs without AM and/or IM phases, and the other set consisting of all the 2,436 HEA compositions. Cross validation (CV) was performed to optimize the ML models and the CV accuracies are found to be 91.4%, 93.1%, 90.2%, 89.1% for DT, KNN, SVM, and ANN respectively in predicting the formation of BCC/B2, BCC/B2 + FCC, and FCC; and 93.6%, 93.3%, 95.5%, 92.7% for DT, KNN, SVM, and ANN respectively in predicting SS, AM, SS + AM, and IM phases. Sixty-six experimental bulk alloys with SS structures are predicted with trained ANN model, and the accuracy reaches 81.8%. VEC is found to be most important parameter in phase prediction for BCC/B2, BCC/B2 + FCC, and FCC phases. Electronegativity difference and FCC-BCC-index (FBI) are the two additional dominating features in determining the formation of SS, AM, SS + AM, and IM. A separation line ΔH_mix=28.97×VEC-246.77 was found in the VEC-vs-ΔH_mix plot to predict the formation of single-phase BCC/B2 or FCC with a 96.2% accuracy (ΔH_mix = mixing enthalpy). These insights will be very valuable for designing HEAs with targeted crystal structures. 

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5. Particle size determines the accumulation of platinum nanoparticles in the estuarine amphipod, Leptocheirus plumulosus

   https://doi.org/10.1039/d1en00713k  

   Sikder, Mithun ; Eudy, Emily ; Cai, Bo ; Chandler, G. Thomas ; Baalousha, Mohammed ( February 2022 , Environmental Science: Nano)

   Nanoparticles (NPs) typically display a wide distribution of different sizes in aquatic environments, yet little information is available on the impact of particle size dispersity on organismal uptake and elimination. This study investigated uptake and elimination of polyvinylpyrrolidone-coated platinum nanoparticles (PVP-PtNPs) of different sizes ( e.g. , 20.0 ± 4.8 nm, 40.5 ± 4.1 nm, and 70.8 ± 4.2 nm) by the estuarine amphipod Leptocheirus plumulosus . Accumulation and elimination were determined by measuring total Pt body burden in amphipods exposed to PtNPs using inductively coupled plasma-mass spectroscopy, as well as the mass and number PtNP body burden using single particle-ICP-MS (sp-ICP-MS). L. plumulosus accumulated Pt from PtNP suspensions of different sizes from water exposure, mostly ( e.g. , >90%) as PtNPs rather than as dissolved Pt. Mass- and number-based uptake increased with decreases in PtNP size whereas mass- and number-based elimination increased with increasing PtNP size. The residual whole-animal body burden of PtNPs after 48 h elimination increased with decreases in PtNP size, with residual body burdens approximately two-fold higher for amphipods exposed to 20 nm PtNPs than amphipods exposed to 70 nm PtNPs. PtNP influx rate ( k uw ) increased with decreasing NP size, with k uw s of 1.07 ± 0.31, 0.82 ± 0.22, and 0.67 ± 0.10 μg g −1 d −1 for 20 nm, 40 nm, and 70 nm PtNPs, respectively. PtNP efflux rate ( k e ) increased with increasing PtNP size, with k e s of 0.31 ± 0.08, 0.66 ± 0.04, and 0.83 ± 0.07 d −1 for 20 nm, 40 nm, and 70 nm PtNP, respectively. When exposed to mixtures of 40 and 70 nm PtNPs with equal masses, surface areas, or number concentrations of 40 nm and 70 nm PtNPs, L. plumulosus accumulated higher numbers of the 40 nm PtNPs than 70 nm PtNPs from all mixtures. The increased exposure concentration of 70 nm PtNPs in the mixture did not affect the uptake of 40 nm PtNPs, suggesting that in a polydispersed NP suspension the uptake of a given size fraction is independent of other size fractions in the mixture. 

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