Improved phase-field models of melting and dissolution in multi-component flows.
Ontology highlight
ABSTRACT:
SUBMITTER: Hester EW
PROVIDER: S-EPMC7655769 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and weather prediction, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However, simulations of turbulent flows remain hindered by the inability of heuristics and supervised learning to model the near-wall dynamics. We address this challenge by introducing scientific multi-agent reinforcement learning (SciMARL) for the discovery of wall models for large-eddy simulations (LES). In SciMARL, discretization points act also as cooperating agents that learn to supply the LES closure model. The agents self-learn using limited data and generalize to extreme Reynolds numbers and previously unseen geometries. The present simulations reduce by several orders of magnitude the computational cost over fully-resolved simulations while reproducing key flow quantities. We believe that SciMARL creates unprecedented capabilities for the simulation of turbulent flows.
| S-EPMC8931082 | biostudies-literature
Project description:As an extremely popular natural product, berberine (BER) is mainly used for gastroenteritis and diarrhoea caused by bacteria. Research has also revealed the potent and extensive pharmacological properties of BER including its anti-arrhythmic, anti-tumour, anti-inflammatory and hypoglycemic activities and so on; therefore, BER is a promising drug for further development. However, its commercial form with hydrochloride exhibits poor stability and solubility, which are detrimental to its clinical therapeutic effects. For these purposes, the salt form was regulated via the reactive crystallization of 8-hydroxy-7,8-dihydroberberine (8H-HBER) with five pharmaceutically suitable organic acids including malonic acid (MA), L-tartaric acid (LTA), D-tartaric acid (DTA), DL-tartaric acid (DLTA) and citric acid (CA), resulting in the six novel solid forms 1BER-1LTA-1W, 1BER-1DTA-1W, 1BER-1DLTA and 2BER-2CA as well as two rare multi-stoichiometric solid forms 1BER-1MA and 1BER-2MA-2W. The preparation of the multi-stoichiometric products was greatly influenced by both the crystallization solvent type and the molar ratio of reactants. The structures of these multi-component solid forms were determined using single-crystal X-ray diffraction and further characterized by powder X-ray diffraction, thermal analysis and Fourier transform infrared spectroscopy. Stability experiments showed that all samples prepared had superior physical stability under high temperature and high humidity. Furthermore, dissolution experiments demonstrated that the maximum apparent solubilities (MAS) of all the products were significantly improved compared with the commercial form of BER in dilute hydrochloric solution (pH = 1.2). In particular, the MAS of 1BER-1MA in dilute hydrochloric solution is as high as 34 times that of the commercial form. In addition, it is preliminarily confirmed that the MAS of the samples prepared in pure water and dilute hydrochloric solution is primarily influenced by a combination of factors including the packing index, intermolecular interactions, affinity of the counter-ion to the solvent, the molar ratio of the drug to counter-ion in the product and the common ion effect. These novel solids are potential candidates for BER solid forms with improved oral dosage design and may prompt further development.
| S-EPMC9812220 | biostudies-literature
Project description:An advection-diffusion-limited dissolution model of an object being eroded by a two-dimensional potential flow is presented. By taking advantage of the conformal invariance of the model, a numerical method is introduced that tracks the evolution of the object boundary in terms of a time-dependent Laurent series. Simulations of a variety of dissolving objects are shown, which shrink and collapse to a single point in finite time. The simulations reveal a surprising exact relationship, whereby the collapse point is the root of a non-analytic function given in terms of the flow velocity and the Laurent series coefficients describing the initial shape. This result is subsequently derived using residue calculus. The structure of the non-analytic function is examined for three different test cases, and a practical approach to determine the collapse point using a generalized Newton-Raphson root-finding algorithm is outlined. These examples also illustrate the possibility that the model breaks down in finite time prior to complete collapse, due to a topological singularity, as the dissolving boundary overlaps itself rather than breaking up into multiple domains (analogous to droplet pinch-off in fluid mechanics). The model raises fundamental mathematical questions about broken symmetries in finite-time singularities of both continuous and stochastic dynamical systems.
| S-EPMC4786035 | biostudies-literature
Project description:Spergualin is a natural product that exhibits immunosuppressive, anti-tumor and anti-bacterial activities. Its derivatives, such as 15-deoxyspergualin (15-DSG), have been clinically approved for acute allograft rejection. However, the reported syntheses are cumbersome (>10 steps) and they suffer from low overall yields (?0.3% to 18%). Moreover, spergualin and its derivatives are chemically unstable and rapidly hydrolyzed in aqueous buffer. Here, we have re-explored these issues and report a modified synthetic route with significantly improved overall yield (?31% to 47%). The key transformation is a microwave-accelerated Ugi multi-component reaction that is used to generate the peptoid core in a single step. Using the products of this route, we found that modifications of the hemiaminal significantly increased chemical stability. Thus, we anticipate that this synthetic route will improve access to biologically active 15-DSG derivatives.
| S-EPMC3088103 | biostudies-literature
Project description:Mitochondria contain an autonomous and spatially segregated genome. The organizational unit of their genome is the nucleoid, which consists of mitochondrial DNA (mtDNA) and associated architectural proteins. Here, we show that phase separation is the primary physical mechanism for assembly and size control of the mitochondrial nucleoid (mt-nucleoid). The major mtDNA-binding protein TFAM spontaneously phase separates in vitro via weak, multivalent interactions into droplets with slow internal dynamics. TFAM and mtDNA form heterogenous, viscoelastic structures in vitro, which recapitulate the dynamics and behavior of mt-nucleoids in vivo. Mt-nucleoids coalesce into larger droplets in response to various forms of cellular stress, as evidenced by the enlarged and transcriptionally active nucleoids in mitochondria from patients with the premature aging disorder Hutchinson-Gilford Progeria Syndrome (HGPS). Our results point to phase separation as an evolutionarily conserved mechanism of genome organization.
| S-EPMC7957436 | biostudies-literature
Project description:MFC is an open-source tool for solving multi-component, multi-phase, and bubbly compressible flows. It is capable of efficiently solving a wide range of flows, including droplet atomization, shock-bubble interaction, and bubble dynamics. We present the 5- and 6-equation thermodynamically-consistent diffuse-interface models we use to handle such flows, which are coupled to high-order interface-capturing methods, HLL-type Riemann solvers, and TVD time-integration schemes that are capable of simulating unsteady flows with strong shocks. The numerical methods are implemented in a flexible, modular framework that is amenable to future development. The methods we employ are validated via comparisons to experimental results for shock-bubble, shock-droplet, and shock-water-cylinder interaction problems and verified to be free of spurious oscillations for material-interface advection and gas-liquid Riemann problems. For smooth solutions, such as the advection of an isentropic vortex, the methods are verified to be high-order accurate. Illustrative examples involving shock-bubble-vessel-wall and acoustic-bubble-net interactions are used to demonstrate the full capabilities of MFC.
| S-EPMC8218895 | biostudies-literature
Project description:The MTT assay, based on the enzymatic reduction of the water-soluble, yellowish tetrazolium salt 3-(4,5-dimethylthiazol)-2,5-diphenyl-tetrazolium bromide (MTT) to purple formazan, is commonly used for assessment of cell viability and proliferation. Accurate performance by the MTT assay depends on complete solubilization of cells and formazan and stability of the colored solution. Comparison of different solubilization solutions revealed that dimethylformamide (DMF) and dimethyl sulfoxide (DMSO), buffered with ammonia buffer, pH 10, and containing 5% SDS, produced the best results. These two solvents provided rapid and complete solubilization of formazan and cells, with minimal background absorbance at 700 nm, good reproducibility (low interassay coefficient of variation), high sensitivity, and color stability for at least 24 h. A linear relationship between viable-cell number and formazan absorbance was preserved for cell densities up to ∼1 × 109 cells/mL for Gram-negative and Gram-positive microorganisms. Since MTT can be reduced by medium components in the absence of cells, blanks containing all medium components but no cells should be run simultaneously. Measurements at two wavelengths, one corresponding to absorption peak of formazan (570 nm) and a background absorbance far from the peak (700 nm), are necessary to avoid artifacts due to incomplete solubilization and turbidity. IMPORTANCE Reduction of the water-soluble tetrazolium salt 3-(4,5-dimethylthiazol)-2,5 diphenyl-tetrazolium bromide (MTT) to purple, water-insoluble formazan is commonly used for assessment of cell viability and proliferation. Spectrophotometric detection of formazan requires its solubilization. The solubilization solvent has a strong influence on data acquisition and often introduces artifacts, leading to misreading of results. This study offers a choice of solvents that minimize solubilization artifacts when the MTT test is applied to microbiological cultures.
| S-EPMC8694201 | biostudies-literature
Project description:Functional magnetic resonance imaging (fMRI) is increasingly used to characterize functional connectivity between brain regions. Given the vast number of between-voxel interactions in high-dimensional fMRI data, it is an ongoing challenge to detect stable and generalizable functional connectivity in the brain among groups of subjects. Component models can be used to define subspace representations of functional connectivity that are more interpretable. It is, however, unclear which component model provides the optimal representation of functional networks for multi-subject fMRI datasets. A flexible cross-validation approach that assesses the ability of the models to predict voxel-wise covariance in new data, using three different measures of generalization was proposed. This framework is used to compare a range of component models with varying degrees of flexibility in their representation of functional connectivity, evaluated on both simulated and experimental resting-state fMRI data. It was demonstrated that highly flexible subject-specific component subspaces, as well as very constrained average models, are poor predictors of whole-brain functional connectivity, whereas the best-generalizing models account for subject variability within a common spatial subspace. Within this set of models, spatial Independent Component Analysis (sICA) on concatenated data provides more interpretable brain patterns, whereas a consistent-covariance model that accounts for subject-specific network scaling (PARAFAC2) provides greater stability in functional connectivity relationships between components and their spatial representations. The proposed evaluation framework is a promising quantitative approach to evaluating component models, and reveals important differences between subspace models in terms of predictability, robustness, characterization of subject variability, and interpretability of the model parameters. Hum Brain Mapp 38:882-899, 2017. © 2016 Wiley Periodicals, Inc.
| S-EPMC6866892 | biostudies-literature
Project description:Abstract Herein, we demonstrate how to predict and experimentally validate phase diagrams for multi?component systems from a high?dimensional virtual space of all possible phase diagrams involving several elements based on small existing experimental data. The experimental data for bulk phases for known systems represents a sampling from this space, and screening the space allows multi?component phase diagrams with given design criteria to be built. This approach uses machine learning methods to predict phase diagrams and Bayesian experimental design to minimize experiments for refinement and validation, all within an active learning loop. The approach is proven by predicting and synthesizing the ferroelectric ceramic system (1??)(Ba0.61Ca0.28Sr0.11TiO3)??(BaTi0.888Zr0.0616Sn0.0028Hf0.0476O3) with a relatively high transition temperature and triple point, as well as the NiTi?based pseudo?binary phase diagram (1??)(Ti0.309Ni0.485Hf0.20Zr0.006)??(Ti0.309Ni0.485Hf0.07Zr0.068Nb0.068) designed for high transition temperature (? ? 1). Each phase diagram is validated and optimized through only three new experiments. The complexity of these compounds is beyond the reach of today's computational methods. A machine learning?based approach is proposed to predict all possible phase diagrams of a given multi?component system from a high?dimensional virtual space. By quickly screening the space, a specific phase diagram with given design criteria can be constructed. Bayesian experimental design is then employed to refine the phase diagram with as few experiments as possible.
| S-EPMC7788591 | biostudies-literature
Project description:Spontaneous C4-symmetry breaking phases are ubiquitous in layered quantum materials, and often compete with other phases such as superconductivity. Preferential suppression of the symmetry broken phases by light has been used to explain non-equilibrium light induced superconductivity, metallicity, and the creation of metastable states. Key to understanding how these phases emerge is understanding how C4 symmetry is restored. A leading approach is based on time-dependent Ginzburg-Landau theory, which explains the coherence response seen in many systems. However, we show that, for the case of the single layered manganite La0.5Sr1.5MnO4, the theory fails. Instead, we find an ultrafast inhomogeneous disordering transition in which the mean-field order parameter no longer reflects the atomic-scale state of the system. Our results suggest that disorder may be common to light-induced phase transitions, and methods beyond the mean-field are necessary for understanding and manipulating photoinduced phases.
| S-EPMC8752725 | biostudies-literature