Journal articles on the topic 'Crystal size prediction'
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Zhang, Yongchun, and Michael F. Doherty. "Simultaneous prediction of crystal shape and size for solution crystallization." AIChE Journal 50, no. 9 (2004): 2101–12. http://dx.doi.org/10.1002/aic.10182.
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Соколовский, А. С., М. Н. Лубов, Н. А. Беседина, Ю. В. Трушин, and М. В. Дубина. "Кинетическая модель формирования кристаллов белка в капиллярах методом контрдиффузии." Письма в журнал технической физики 44, no. 11 (2018): 105. http://dx.doi.org/10.21883/pjtf.2018.11.46203.17068.
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AbstractA kinetic model of crystallization of lysozyme is proposed, and computer calculations of this process are carried out. The conditions for the formation of such crystals are determined. Under these conditions, individual crystals were grown that were suitable for X-ray examination. The developed model enables prediction of the quantity, size, and place of crystal nucleation inside the capillary.
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Mahmoud, Hanan Ahmed Hosni. "Transfer Learning in Inorganic Compounds’ Crystal Structure Classification." Crystals 13, no. 1 (January 2, 2023): 87. http://dx.doi.org/10.3390/cryst13010087.
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Deep learning consists of deep convolutional layers and an unsupervised feature selection phase. The feature selection of deep learning on a large size dataset can be employed in correlated prediction models with small size datasets. This methodology is titled deep transfer learning model and enhances prediction model generalization. In this research, we proposed a prediction model for the crystal structure classification of inorganic compounds. Deep learning models in structure classification are usually trained using a large size dataset of 300 K compounds from different quantum compounds dataset (DS1). The feature selection of the deep learning models is reused for selecting features in a small size dataset (with 30 K inorganic compounds and containing 150 different crystal structures) and three alloy classes. The selected features are then fed into a random decision forest prediction model as input. The proposed convolutional neural network (CNN) with transfer learning realizes an accuracy of 98.5%. The experiment results display the CPU time consumed by our model, comparing the time required by similar models. The CPU classification time of the proposed model is 21 s on average.
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Hashino, Tempei, and Gregory J. Tripoli. "The Spectral Ice Habit Prediction System (SHIPS). Part IV: Box Model Simulations of the Habit-Dependent Aggregation Process." Journal of the Atmospheric Sciences 68, no. 6 (June 1, 2011): 1142–61. http://dx.doi.org/10.1175/2011jas3667.1.
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Abstract The purpose of this paper is to assess the prediction of particle properties of aggregates and particle size distributions with the Spectral Ice Habit Prediction System (SHIPS) and to investigate the effects of crystal habits on aggregation process. Aggregation processes of ice particles are critical to the understanding of precipitation and the radiative signatures of cloud systems. Conventional approaches taken in cloud-resolving models (CRMs) are not ideal to study the effects of crystal habits on aggregation processes because the properties of aggregates have to be assumed beforehand. As described in Part III, SHIPS solves the stochastic collection equation along with particle property variables that contain information about crystal habits and maximum dimensions of aggregates. This approach makes it possible to simulate properties of aggregates explicitly and continuously in CRMs according to the crystal habits. The aggregation simulations were implemented in a simple model setup, assuming seven crystal habits and several initial particle size distributions (PSDs). The predicted PSDs showed good agreement with observations after rescaling except for the large-size end. The ice particle properties predicted by the model, such as the mass–dimensional (m-D) relationship and the relationship between diameter of aggregates and number of component crystals in an aggregate, were found to be quantitatively similar to those observed. Furthermore, these predictions were dependent on the initial PSDs and habits. A simple model for the growth of a particle’s maximum dimension was able to simulate the typically observed fractal dimension of aggregates when an observed value of the separation ratio of two particles was used. A detailed analysis of the collection kernel indicates that the m-D relationship unique to each crystal habit has a large impact on the growth rate of aggregates through the cross-sectional area or terminal velocity difference, depending on the initial equivalent particle distribution. A significant decrease in terminal velocity differences was found in the inertial flow regime for all the habits but the constant-density sphere. It led to formation of a local maximum in the collection kernel and, in turn, formed an identifiable mode in the PSDs. Remaining issues that must be addressed in order to improve the aggregation simulation with the quasi-stochastic model are discussed.
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Chen, Dongju, Shupei Li, and Jinwei Fan. "Effect of KDP-Crystal Material Properties on Surface Morphology in Ultra-Precision Fly Cutting." Micromachines 11, no. 9 (August 25, 2020): 802. http://dx.doi.org/10.3390/mi11090802.
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To study the effect of material properties on the surface morphology of potassium dihydrogen phosphate (KDP) crystals, an ultra-precision fly cutting machine tool with a single-point diamond tool was used to perform a cutting experiment on (100) crystal plane of the KDP crystal. The elastic modulus, shear modulus, hardness, and dislocation of KDP crystals are taken into the cutting force model by introducing the strain gradient plasticity theory. Since the size effect and dynamic response will affect the surface roughness during ultra-precision machining, the surface roughness of workpieces in ultra-precision fly cutting is hard to predict. Based on the previously established strain gradient plasticity theoretical model, cutting force model, and the dynamic characteristics of the ultra-precision fly cutting system, a surface morphology prediction model under the influence of KDP crystal material properties was established. Finally, the accuracy of the surface morphology prediction model was verified by ultra-precision fly cutting experiments, and identified the frequency range of the characteristic signal caused by the anisotropy of the KDP crystal from the frequency, thereby verifying the KDP crystal material properties has a significant effect on the surface of the machined workpiece roughness.
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Dixon, Anthony G., and Robert W. Thompson. "Prediction of the zeolite crystal size distribution in batchwise hydrothermal synthesis." Zeolites 6, no. 3 (May 1986): 154–60. http://dx.doi.org/10.1016/0144-2449(86)90041-2.
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Arsiccio, Andrea, Antonello A. Barresi, and Roberto Pisano. "Prediction of Ice Crystal Size Distribution after Freezing of Pharmaceutical Solutions." Crystal Growth & Design 17, no. 9 (August 15, 2017): 4573–81. http://dx.doi.org/10.1021/acs.cgd.7b00319.
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McDonald, Matthew A., Andreas S. Bommarius, Martha A. Grover, and Ronald W. Rousseau. "Direct Observation of Growth Rate Dispersion in the Enzymatic Reactive Crystallization of Ampicillin." Processes 7, no. 6 (June 22, 2019): 390. http://dx.doi.org/10.3390/pr7060390.
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Prediction and control of crystal size distributions, a prerequisite for production of consistent crystalline material in the pharmaceutical industry, requires knowledge of potential non-idealities of crystal growth. Ampicillin is one such medicine consumed in crystal form (ampicillin trihydrate). Typically it is assumed that all crystals of the same chemical and geometric type grow at the same rate, however a distribution of growth rates is often observed experimentally. In this study, ampicillin produced enzymatically is crystallized and a distribution of growth rates is observed as individual crystals are monitored by microscopy. Most studies of growth rate dispersion use complex flow apparatuses to maintain a constant supersaturation or imprecise measurements of size distributions to reconstruct growth rate dispersions. In this study, the controllable enzyme reaction enables the same information to be gathered from fewer, less complicated experiments. The growth rates of individual ampicillin trihydrate crystals were found to be normally distributed, with each crystal having an intrinsic growth rate that is constant in time. Differences in the individual crystals, such as different number and arrangement of dislocations and surface morphology, best explain the observed growth rates. There is a critical supersaturation below which growth is not observed, thought to be caused by reactants adsorbing to the crystal surface and pinning advancing growth steps. The distribution of critical supersaturation also suggests that individual crystals’ surface morphologies cause a distribution of growth rates.
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Xia, Deyu, Ning Li, Pengju Ren, and Xiaodong Wen. "Prediction Of Material Properties By Neural Network Fusing The Atomic Local Environment And Global Description: Applied To Organic Molecules And Crystals." E3S Web of Conferences 267 (2021): 02059. http://dx.doi.org/10.1051/e3sconf/202126702059.
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Machine learning has brought great convenience to material property prediction. However, most existing models can only predict properties of molecules or crystals with specific size, and usually only local atomic environment or molecular global descriptor representation be used as the characteristics of the model, resulting in poor model versatility and cannot be applied to multiple systems. We propose a method that combines the description of the local atomic environment and the overall structure of the molecule, a fusion model consisting of a graph convolutional neural network and a fully connected neural network is used to predict the properties of molecules or crystals, and successfully applied to QM9 organic molecules and semiconductor crystal materials. Our method is not limited to a specific size of a molecule or a crystal structure. According to the calculation principle of the properties of the material molecules, the influences of the local atomic environment and the overall structure of the molecules on the properties are respectively considered, an appropriate weighting ratio is selected to predict the properties. As a result, the prediction performance has been greatly improved. In fact, the proposed method is not limited to organic molecules and crystals and is also applicable to other structures, such as clusters.
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Tu, Yuhui, Seán B. Leen, and Noel M. Harrison. "A high-fidelity crystal-plasticity finite element methodology for low-cycle fatigue using automatic electron backscatter diffraction scan conversion: Application to hot-rolled cobalt–chromium alloy." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 235, no. 8 (May 11, 2021): 1901–24. http://dx.doi.org/10.1177/14644207211010836.
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The common approach to crystal-plasticity finite element modeling for load-bearing prediction of metallic structures involves the simulation of simplified grain morphology and substructure detail. This paper details a methodology for predicting the structure–property effect of as-manufactured microstructure, including true grain morphology and orientation, on cyclic plasticity, and fatigue crack initiation in biomedical-grade CoCr alloy. The methodology generates high-fidelity crystal-plasticity finite element models, by directly converting measured electron backscatter diffraction metal microstructure grain maps into finite element microstructural models, and thus captures essential grain definition for improved microstructure–property analyses. This electron backscatter diffraction-based method for crystal-plasticity finite element model generation is shown to give approximately 10% improved agreement for fatigue life prediction, compared with the more commonly used Voronoi tessellation method. However, the added microstructural detail available in electron backscatter diffraction–crystal-plasticity finite element did not significantly alter the bulk stress–strain response prediction, compared to Voronoi tessellation–crystal-plasticity finite element. The new electron backscatter diffraction-based method within a strain-gradient crystal-plasticity finite element model is also applied to predict measured grain size effects for cyclic plasticity and fatigue crack initiation, and shows the concentration of geometrically necessary dislocations around true grain boundaries, with smaller grain samples exhibiting higher overall geometrically necessary dislocations concentrations. In addition, minimum model sizes for Voronoi tessellation–crystal-plasticity finite element and electron backscatter diffraction–crystal-plasticity finite element models are proposed for cyclic hysteresis and fatigue crack initiation prediction.
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Harrington, Jerry Y., Kara Sulia, and Hugh Morrison. "A Method for Adaptive Habit Prediction in Bulk Microphysical Models. Part I: Theoretical Development." Journal of the Atmospheric Sciences 70, no. 2 (February 1, 2013): 349–64. http://dx.doi.org/10.1175/jas-d-12-040.1.
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Abstract Bulk microphysical schemes use the capacitance model for ice vapor growth in combination with mass–size relationships to determine the evolution of ice water content (IWC) and ice particle maximum dimension in time. These approaches are limited since a single axis length is used, the aspect ratio is usually held constant and mass–size relations have many available coefficients for similar ice types. Fixing the crystal aspect ratio severs the nonlinear link between aspect ratio changes and increased growth rates that occur during crystal growth. A method is presented here for predicting two crystal axes and the crystal aspect ratio in bulk models. Evolution of the ice mass mixing ratio is tied to the evolution of two axis length mixing ratios through the use of a historical axis ratio parameter containing memory of crystal shape. This parameter links the distributions of the two axes, allowing characterization of particle lengths using a single distribution. The method uses four prognostic variables: the mass and number mixing ratios, and two axis length mixing ratios. Development of the method is presented, with testing described in Part II.
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Molthan, Andrew L., and Walter A. Petersen. "Incorporating Ice Crystal Scattering Databases in the Simulation of Millimeter-Wavelength Radar Reflectivity." Journal of Atmospheric and Oceanic Technology 28, no. 3 (March 1, 2011): 337–51. http://dx.doi.org/10.1175/2010jtecha1511.1.
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Abstract The Canadian CloudSat/Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Validation Project (C3VP) was designed to acquire aircraft, surface, and satellite observations of particle size distributions during cold season precipitation events for the purposes of validating and improving upon satellite-based retrievals of precipitation and the representation of cloud and precipitation processes within numerical weather prediction schemes. During an intensive observation period on 22 January 2007, an instrumented aircraft measured ice crystal size distributions, ice and liquid water contents, and atmospheric state parameters within a broad shield of precipitation generated by a passing midlatitude cyclone. The 94-GHz CloudSat radar acquired vertical profiles of radar reflectivity within light to moderate snowfall, coincident with C3VP surface and aircraft instrumentation. Satellite-based retrievals of cold season precipitation require relationships between remotely sensed quantities, such as radar reflectivity or brightness temperature, and the ice water content present within the sampled profile. In this study, three methods for simulating CloudSat radar reflectivity are investigated by comparing Mie spheres, single dendrites, and fractal aggregates represented within scattering databases or parameterizations. It is demonstrated that calculations of radar backscatter from nonspherical crystal shapes are required to represent the vertical trend in CloudSat radar reflectivity for this particular event, as Mie resonance effects reduce the radar backscatter from precipitation-sized particles larger than 1 mm. Remaining differences between reflectivity from nonspherical shapes and observations are attributed to uncertainty in the mass–diameter relationships for observed crystals and disparities between naturally occurring crystals and shapes assumed in the development of ice crystal scattering databases and parameterizations.
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Shirai, Y., M. Louhi, S. Palosaari, K. Nakanishi, and R. Matsuno. "Comments on the prediction of ice crystal size distribution in a continuous crystallizer." Chemical Engineering Science 45, no. 4 (1990): 1147–48. http://dx.doi.org/10.1016/0009-2509(90)85039-g.
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Gultepe, I., A. J. Heymsfield, P. R. Field, and D. Axisa. "Ice-Phase Precipitation." Meteorological Monographs 58 (January 1, 2017): 6.1–6.36. http://dx.doi.org/10.1175/amsmonographs-d-16-0013.1.
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AbstractIce-phase precipitation occurs at Earth’s surface and may include various types of pristine crystals, rimed crystals, freezing droplets, secondary crystals, aggregates, graupel, hail, or combinations of any of these. Formation of ice-phase precipitation is directly related to environmental and cloud meteorological parameters that include available moisture, temperature, and three-dimensional wind speed and turbulence, as well as processes related to nucleation, cooling rate, and microphysics. Cloud microphysical parameters in the numerical models are resolved based on various processes such as nucleation, mixing, collision and coalescence, accretion, riming, secondary ice particle generation, turbulence, and cooling processes. These processes are usually parameterized based on assumed particle size distributions and ice crystal microphysical parameters such as mass, size, and number and mass density. Microphysical algorithms in the numerical models are developed based on their need for applications. Observations of ice-phase precipitation are performed using in situ and remote sensing platforms, including radars and satellite-based systems. Because of the low density of snow particles with small ice water content, their measurements and predictions at the surface can include large uncertainties. Wind and turbulence affecting collection efficiency of the sensors, calibration issues, and sensitivity of ground-based in situ observations of snow are important challenges to assessing the snow precipitation. This chapter’s goals are to provide an overview for accurately measuring and predicting ice-phase precipitation. The processes within and below cloud that affect falling snow, as well as the known sources of error that affect understanding and prediction of these processes, are discussed.
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Aleksovska, S., S. C. Nyburg, Lj Pejov, and V. M. Petrusevski. "β-K2SO4-Type Isomorphs: Prediction of Structures and Refinement of Rb2CrO4." Acta Crystallographica Section B Structural Science 54, no. 2 (April 1, 1998): 115–20. http://dx.doi.org/10.1107/s010876819701152x.
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The crystal structures of members within a group of isostructural compounds may be successfully predicted. This is demonstrated for the β-K2SO4 group isomorphs with the general formula M 2 XO4, which were chosen as a family of very closely related compounds nearly all with accurately refined crystal structures. The unit-cell parameters and the fractional atomic coordinates are shown to exhibit systematic variations with both cation and anion size, as well as the Mulliken charge on the O atom in the tetrahedral anion. This allows the prediction of the crystal structures of members in the series, with only the chemical composition of the compound being known. The agreement is good, except for an early structure determination of Rb2CrO4. The now refined structure gives excellent agreement with that predicted.
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WANG, LIFENG, and HAIYAN HU. "SIZE EFFECTS ON EFFECTIVE YOUNG'S MODULUS OF NANO CRYSTAL COPPER WIRES." International Journal of Computational Methods 02, no. 03 (September 2005): 315–26. http://dx.doi.org/10.1142/s0219876205000508.
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In this paper, a study is made for the size effects on the effective Young's modulus of nano crystal copper wires. On the basis of numerical results of molecular dynamics simulation, the inhomogeneous property of the nano wires is taken into account so that the continuum model of either a rod or a beam is constructed to predict the size dependence of the effective Young's modulus. The comparison with molecular dynamics simulation based on embedded atom method shows that the new rod model enables one to predict the effective Young's modulus as accurately as existing models for the nano wires of different sizes of cross sections under axial load. Furthermore, the beam model gives better prediction than the current model for the nano wires subject to pure bending. The size effect on the elastic property can also be observed from the longitudinal and transverse natural vibration of the nano wires. In this case, the effective Young's modulus is nearly the same as that obtained through axial deformation and pure bending respectively.
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Rahmani, Hamayoon, Mohammad Jawad Hamta, Ibrahim Tawana, and Hussain Aziz. "Studying The Effect of The Manufacturing Process of Heusler Compounds Co2MnZ (Z=Ga, Ge, Si) on its Crystal Order and Magnetic Properties." Jurnal Pendidikan Fisika dan Teknologi 8, no. 2 (November 12, 2022): 136–45. http://dx.doi.org/10.29303/jpft.v8i2.4084.
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The effect of different manufacturing processes, such as arc melting, mechanical alloying, and baking, on the crystalline and magnetic behavior of Co2MnSi, Co2MnGa, and Co2MnGe compounds was investigated. Samples of Co2MnSi, Co2MnGe, and Co2MnGa compounds were produced using the arc melting method and the effect of mechanical alloying and annealing processes on the manufactured products was investigated. The results showed that the use of different processes during manufacturing leads to different crystalline and magnetic behaviors of the sample. One of these cases is the correlation of the crystal order with the lattice parameter size in the produced samples and its effect on reducing the saturation magnetization compared to Slater and Pauling's prediction. Also, the change of order induced by the mechanical alloying process in the production of Co2MnSi composition has led to a drop of about 14% in saturation magnetization. The coercivity in the sample produced by arc melting and mechanical alloying in Co2MnGe composition is lower than the expected value, which was attributed to the low magnetic anisotropy of the sample due to the small size of the crystals in this sample, which is compensated in the cooking process. For example, performing the grinding process before baking leads to a change in the crystal order and, consequently, to a decrease in the saturation magnetization of the sample. The final baking increases the size of the crystals and reduces the strain. The sample obtained from grinding after arc melting had more coercivity than the other two samples due to having smaller crystals.
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Asakuma, Yusuke, Tomonobu Terashima, Takahiro Honda, Kouji Maeda, Hideo Miki, and Keisuke Fukui. "Prediction of Attrited and Fragmented Crystal Size by Micro-hardness Parameters in Suspension-crystallization Processes." Journal of the Society of Powder Technology, Japan 46, no. 10 (2009): 750–55. http://dx.doi.org/10.4164/sptj.46.750.
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Morrison, Hugh, and Wojciech W. Grabowski. "An Improved Representation of Rimed Snow and Conversion to Graupel in a Multicomponent Bin Microphysics Scheme." Journal of the Atmospheric Sciences 67, no. 5 (May 1, 2010): 1337–60. http://dx.doi.org/10.1175/2010jas3250.1.
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Abstract This paper describes the development of a new multicomponent detailed bin ice microphysics scheme that predicts the number concentration of ice as well as the rime mass mixing ratio in each mass bin. This allows for local prediction of the rime mass fraction. In this approach, the ice particle mass size, projected area size, and terminal velocity–size relationships vary as a function of particle mass and rimed mass fraction, based on a simple conceptual model of rime accumulation in the crystal interstices that leads to an increase in particle mass, but not in its maximum size, until a complete “filling in” with rime and conversion to graupel occurs. This approach allows a natural representation of the gradual transition from unrimed crystals to rimed crystals and graupel during riming. The new ice scheme is coupled with a detailed bin representation of the liquid hydrometeors and applied in an idealized 2D kinematic flow model representing the evolution of a mixed-phase precipitating cumulus. Results using the bin scheme are compared with simulations using a two-moment bulk scheme employing the same approach (i.e., separate prediction of bulk ice mixing ratio from vapor deposition and riming, allowing for local prediction of bulk rime mass fraction). The bin and bulk schemes produce similar results in terms of ice and liquid water paths and optical depths, as well as the timing of the onset and peak surface precipitation rate. However, the peak domain-average surface precipitation rate produced by the bulk scheme is about 4 times that in the bin simulation. The bin scheme is also compared with simulations that assume the ice particles consist entirely of either unrimed snow or graupel. While overall results are fairly similar, the onset and timing of the peak domain-average surface precipitation rate are substantially delayed in the simulations that treat the ice particles as either unrimed snow or graupel. These results suggest the importance of representing different ice types, including partially rimed crystals, for this case.
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Hashino, Tempei, and Gregory J. Tripoli. "The Spectral Ice Habit Prediction System (SHIPS). Part III: Description of the Ice Particle Model and the Habit-Dependent Aggregation Model." Journal of the Atmospheric Sciences 68, no. 6 (June 1, 2011): 1125–41. http://dx.doi.org/10.1175/2011jas3666.1.
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Abstract The purpose of this paper is to describe a numerical scheme of the Spectral Ice Habit Prediction System (SHIPS) that simulates the dependency of aggregation process explicitly on crystal habit and size in cloud-resolving models (CRMs). The sizes and shapes of ice crystals are known to modulate the aggregation process, which is a critical part of physical processes leading to precipitation in addition to vapor deposition and riming processes. A problem with conventional formulation of aggregation process in CRMs is that it is not designed to predict the aggregates’ properties based on the information on crystals. To simulate such dependency, SHIPS solves a quasi-stochastic model that describes growth tendency for a group of particles, together with particle property variables (PPVs) that carry information on habit and types of ice particles. SHIPS diagnoses the ice particle properties based on the PPVs for each mass bin at given a time and space, which are used to calculate the collision cross-sectional area and terminal velocity differences based on crystal habits explicitly. To achieve prediction of properties of aggregates and rimed particles, SHIPS introduces 1) the use of a conceptually based, circumscribing shape, called the ice particle model, and 2) the explicit prediction of the circumscribing sphere volume based on a simple growth model of the maximum dimension. Based on these properties, SHIPS is able to predict the mass–dimensional relationships of aggregates so that they are physically consistent with the growth history of the particles. In addition, the information about the crystals making up the aggregates is predicted. Part IV of this series will present the results and evaluation of the application of this formulation to idealized tests in a Lagrangian “box model” setup.
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Wong-Ng, Winnie, Boris Paretzkin, and Edwin R. Fuller. "Crystal Chemistry and Phase Equilibria of the BaO-R2O3-CuO Systems." Advances in X-ray Analysis 33 (1989): 453–65. http://dx.doi.org/10.1154/s0376030800019881.
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AbstractTwo important factors, the progressively decreasing size of the lanthanides, which is known as the lanthanide contraction, as well as the stability of different oxidation states of these elements influence the prediction of compound formation in the Ba-R-Cu-O systems. A systematic investigation of these lanthanide systems and comparison with the Y system has revealed a correlation of the effect of the above factors, in particular the size factor, on the trend of phase formation, solid solution formation and phase compatibility diagrams of the Ba-R-Cu-O systems. For example, it has been found that the smaller the size of R3+ or the greater the mismatch betwaen Ba2+ and R3+ in the solid solution series Ba2-ZR1+lCu3O6+x, the smaller the extent of solid solution formation. This differing extent of solid solution formation influences the ternary phase relationships.
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Li, T., and Zhu Feng Yue. "A Life Prediction Model for Nickel-Base Single Crystal Superalloy DD3." Key Engineering Materials 261-263 (April 2004): 1123–28. http://dx.doi.org/10.4028/www.scientific.net/kem.261-263.1123.
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The possibility of the life prediction model for nickel-base single crystal blades has been studied. The fatigue-creep (FC) and thermal fatigue-creep (TMFC) as well as creep experiments have been carried out with different hold time of DD3. The hold time and the frequency as well as the temperature range are the main factors influencing on the life. An emphasis has been put on the micro mechanism of the rupture of creep, FC and TMFC. Two main factors are the voiding and degeneration of the material for the creep, FC and TMFC experiments. There are voids in the fracture surfaces, and size of the voids is dependent on the loading condition. Generally, the rupture mechanism is the same for creep, FC and TMFC. If the loading can be simplified to the working conditions of the turbine blades, i.e. the hold time is at the top temperature and maximum stress, a linear life model is satisfactory to the life prediction of nickel-base single crystal superalloy from the experimental study in this paper. The temperature and the stress level of the nickel-base single crystal (SC)blades are not uniform. To predict the life of SC blades, one should consider the cycles of the temperature and stress as well as the oxidation simultaneously. In the past 30 years, there are many works on the mechanical behavior and description, such as the inelastic constitutive relationships, plastic, fracture, isothermal creep and fatigue and thermal fatigue as well as oxidation[1-3]. There are also special software (program) to analyze the deformation and life of nickel-base single crystal structures, such as blades. In order to apply to the engineering more conveniently, there should be a life prediction model for the blades. The model should not be too complex, but take more influential factors as possible into consideration.
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Choudhury, Amitava, Tanmay Konnur, P. P. Chattopadhyay, and Snehanshu Pal. "Structure prediction of multi-principal element alloys using ensemble learning." Engineering Computations 37, no. 3 (November 21, 2019): 1003–22. http://dx.doi.org/10.1108/ec-04-2019-0151.
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Purpose The purpose of this paper, is to predict the various phases and crystal structure from multi-component alloys. Nowadays, the concept and strategies of the development of multi-principal element alloys (MPEAs) significantly increase the count of the potential candidate of alloy systems, which demand proper screening of large number of alloy systems based on the nature of their phase and structure. Experimentally obtained data linking elemental properties and their resulting phases for MPEAs is profused; hence, there is a strong scope for categorization/classification of MPEAs based on structural features of the resultant phase along with distinctive connections between elemental properties and phases. Design/methodology/approach In this paper, several machine-learning algorithms have been used to recognize the underlying data pattern using data sets to design MPEAs and classify them based on structural features of their resultant phase such as single-phase solid solution, amorphous and intermetallic compounds. Further classification of MPEAs having single-phase solid solution is performed based on crystal structure using an ensemble-based machine-learning algorithm known as random-forest algorithm. Findings The model developed by implementing random-forest algorithm has resulted in an accuracy of 91 per cent for phase prediction and 93 per cent for crystal structure prediction for single-phase solid solution class of MPEAs. Five input parameters are used in the prediction model namely, valence electron concentration, difference in the pauling negativeness, atomic size difference, mixing enthalpy and mixing entropy. It has been found that the valence electron concentration is the most important feature with respect to prediction of phases. To avoid overfitting problem, fivefold cross-validation has been performed. To understand the comparative performance, different algorithms such as K-nearest Neighbor, support vector machine, logistic regression, naïve-based approach, decision tree and neural network have been used in the data set. Originality/value In this paper, the authors described the phase selection and crystal structure prediction mechanism in MPEA data set and have achieved better accuracy using machine learning.
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Zhang, Han, Yumei Chen, and Ji Wang. "Bechmann’s Number for Harmonic Overtones of Thickness-Shear Vibrations of Rotated Y-Cut Quartz Crystal Plates." Coatings 10, no. 7 (July 13, 2020): 667. http://dx.doi.org/10.3390/coatings10070667.
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A procedure based on approximate solutions of three-dimensional equations of wave propagation is utilized for calculating Bechmann’s number for the harmonic overtones of thickness-shear modes in the rotated Y-cut quartz crystal plates. Bechmann’s number is used for the optimization and improvement of electrodes to yield superior performance in the design of quartz crystal resonators. Originally, Bechmann’s number is found through practical experiences, and analytical results were provided afterward to enable optimal design of novel resonator structures. The outcomes in this study are from a simplified theoretical prediction and they are consistent with known empirical results, making it is possible to design optimal quartz crystal resonators for cases without adequate experimental data for a higher frequency and smaller size.
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Huo, Yan, and Diyuan Guan. "Size measurement and prediction for L-glutamic acid crystal growth during stirred crystallization based on imaging analysis." Mathematical Biosciences and Engineering 18, no. 2 (2021): 1864–78. http://dx.doi.org/10.3934/mbe.2021097.
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Zhang, Wei, Fengzhen Zhang, Liping Ma, Jie Yang, Jing Yang, and Huaping Xiang. "Prediction of the Crystal Size Distribution for Reactive Crystallization of Barium Carbonate under Growth and Nucleation Mechanisms." Crystal Growth & Design 19, no. 7 (June 7, 2019): 3616–25. http://dx.doi.org/10.1021/acs.cgd.8b01067.
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Peng, Xiang-Long, Gan-Yun Huang, and Swantje Bargmann. "Gradient Crystal Plasticity: A Grain Boundary Model for Slip Transmission." Materials 12, no. 22 (November 15, 2019): 3761. http://dx.doi.org/10.3390/ma12223761.
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Interaction between dislocations and grain boundaries (GBs) in the forms of dislocation absorption, emission, and slip transmission at GBs significantly affects size-dependent plasticity in fine-grained polycrystals. Thus, it is vital to consider those GB mechanisms in continuum plasticity theories. In the present paper, a new GB model is proposed by considering slip transmission at GBs within the framework of gradient polycrystal plasticity. The GB model consists of the GB kinematic relations and governing equations for slip transmission, by which the influence of geometric factors including the misorientation between the incoming and outgoing slip systems and GB orientation, GB defects, and stress state at GBs are captured. The model is numerically implemented to study a benchmark problem of a bicrystal thin film under plane constrained shear. It is found that GB parameters, grain size, grain misorientation, and GB orientation significantly affect slip transmission and plastic behaviors in fine-grained polycrystals. Model prediction qualitatively agrees with experimental observations and results of discrete dislocation dynamics simulations.
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Dolinar, Erica K., James R. Campbell, Jared W. Marquis, Anne E. Garnier, and Bryan M. Karpowicz. "Novel Parameterization of Ice Cloud Effective Diameter from Collocated CALIOP-IIR and CloudSat Retrievals." Journal of Applied Meteorology and Climatology 61, no. 7 (July 2022): 891–907. http://dx.doi.org/10.1175/jamc-d-21-0163.1.
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Abstract Satellite-based measurements of global ice cloud microphysical properties are sampled to develop a novel set of physical parameterizations, relating to cloud layer temperature and effective diameter De, that can be implemented for two separate applications: in numerical weather prediction models and lidar-based cloud radiative forcing studies. Ice cloud optical properties (i.e., spectral scattering and absorption) are estimated based on the effective size and habit mixture of the cloud particles. Historically, the ice cloud De has been parameterized from aircraft in situ measurements. However, aircraft-based parameterizations are opportunistic in that they only represent specific types of clouds (e.g., convective anvil, tropopause-topped cirrus) in the regions in which they were sampled and, in some cases, are limited in fully resolving the entire vertical cloud layer. Breaking away from the aircraft-based parameterization paradigm, this study is the first of its kind to attempt a parameterization of De as a function of temperature, ice water content (IWC), and lidar-derived extinction from satellite-based global oceanic measurements of ice clouds. Data from both active and passive remote sensing sensors from two of NASA’s A-Train satellites, CloudSat and CALIPSO, are collected to guide development of globally robust parameterizations of all ice cloud types and one exclusively for cirrus clouds. Significance Statement We derived unique parameterizations of ice crystal effective size from global satellite measurements in an effort to more robustly and consistently represent ice clouds in numerical models for weather forecasting and climate energy balance studies. Based on our results, effective ice crystal size is easily solved based on temperature and visible cloud translucence. By knowing the size of the ice crystals, we can then estimate cloud scattering and absorption. In comparison with aircraft-based parameterizations, the satellite data reveal that ice crystal effective sizes are much smaller, on global average, for ice clouds occurring in relatively warm layers (>230 K), indicating that many ice clouds are more reflective than previously believed.
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Hashino, T., and G. J. Tripoli. "The Spectral Ice Habit Prediction System (SHIPS). Part I: Model Description and Simulation of the Vapor Deposition Process." Journal of the Atmospheric Sciences 64, no. 7 (July 1, 2007): 2210–37. http://dx.doi.org/10.1175/jas3963.1.
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Abstract This paper describes the Spectral Ice Habit Prediction System (SHIPS), which represents a continuous-property approach to microphysics simulation in an Eulerian cloud-resolving model (CRM). A two-moment hybrid-bin method is adopted to predict the solid hydrometeor distribution, where the distribution is divided into the mass bins with a simple mass distribution inside each bin. Each bin is characterized by a single representative ice crystal habit and the type of solid hydrometeor. These characteristics are diagnosed based on a series of particle property variables (PPVs) of solid hydrometeors that reflect the history of microphysical processes and the mixing between bins and air parcels in space. Thus, SHIPS allows solid hydrometeors to evolve characteristics and size distribution based on their movement through a cloud. SHIPS was installed into the University of Wisconsin-Nonhydrostatic Modeling System (UW-NMS) and tested for ice nucleation and vapor deposition processes. Two-dimensional idealized simulations were employed to simulate a winter orographic storm observed during the second Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) campaign. The simulated vertical distributions of ice crystal habits showed that the dynamic advection of dendrites produces wider dendritic growth region than local atmospheric conditions suggest. SHIPS showed the sensitivities of the habit distribution in the low- and midlevel to the upper-level growth mode (T < −20°C) of ice crystals through the sedimentation. Comparison of the results to aircraft observations casts doubt on the role of the columnar growth mode (T < −20°C) traditionally thought to be dominant in the literature. The results demonstrated how the complexity of the vapor deposition growth of ice crystals, including dendrites and capped columns, in varying temperature and moisture lead to particular observed habits.
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Li, Qing Yun, and Xue Zhong Wang. "Population Balance and CFD Simulation of Particle Aggregation and Growth in a Continuous Confined Jet Mixer for Hydrothermal Synthesis of Nanocrystals." Crystals 11, no. 2 (January 29, 2021): 144. http://dx.doi.org/10.3390/cryst11020144.
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Population balance and computational fluid dynamics models are built and integrated to carry out a simulation study of the reactive crystallisation process in a confined jet mixer (CJM) for the continuous flow synthesis of TiO2 nanoparticles at a supercritical water condition. In the population balance model, the crystal growth in size is modelled as being due to combined nanocrystal aggregation as well as surface growth. A free molecular model is used to predict the particle aggregation. The performance of the combined aggregation and surface growth models is compared with models that only consider surface growth as the only mechanism for particle size enlargement. It was found that the combined model gives a more accurate prediction of particle size distribution.
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van de Streek, Jacco, Kristoffer Johansson, and Xiaozhou Li. "Computational Pharmaceutical Materials Science: Beyond Static Structures." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1541. http://dx.doi.org/10.1107/s2053273314084587.
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The five Crystal-Structure Prediction (CSP) Blind Tests have shown that molecular-mechanics force fields are not accurate enough for crystal structure prediction[1]. The first--and only--method to successfully predict all four target crystal structures of one of the CSP Blind Tests was dispersion-corrected Density Functional Theory (DFT-D), and this is what we use for our work. However, quantum-mechanical methods (such as DFT-D), are too slow to allow simulations that include the effects of time and temperature, certainly for the size of molecules that are common in pharmaceutical industry. Including the effects of time and temperature therefore still requires molecular dynamics (MD) with less accurate force fields. In order to combine the accuracy of the successful DFT-D method with the speed of a force field to enable molecular dynamics, our group uses Tailor-Made Force Fields (TMFFs) as described by Neumann[2]. In Neumann's TMFF approach, the force field for each chemical compound of interest is parameterised from scratch against reference data from DFT-D calculations; in other words, the TMFF is fitted to mimic the DFT-D energy potential. Parameterising a dedicated force field for each individual compound requires an investment of several weeks, but has the advantage that the resulting force field is more accurate than a transferable force field. Combining crystal-structure prediction with DFT-D followed by molecular dynamics with a tailor-made force field allows us to calculate e.g. the temperature-dependent unit-cell expansion of each predicted polymorph, as well as possible temperature-dependent disorder. This is relevant for example when comparing the calculated X-ray powder diffraction patterns of the predicted crystal structures against experimental data.
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Wei, Qinyan, Bingqian Shi, Fei Wang, Shuoshuo Shao, Liang Zhu, and Xiaoyu Zhao. "Simple and Rapid Preparation of MIL-121 with Small Particles for Lithium Adsorption from Brine." Coatings 11, no. 7 (July 16, 2021): 854. http://dx.doi.org/10.3390/coatings11070854.
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A novel method to generate an aluminum-based MOF material named as MIL-121 was investigated. MIL-121, [Al(OH)(H2BTEC)·(H2O)]n is a prototypal aluminum MOF with 1,2,4,5-benzenetetracarboxylic acid (BTEC) linkers, which was normally produced by the hydrothermal method. Different from the hydrothermal method, the developed novel method does not involve high temperature and high pressure, instead the MOF material was produced by the traditional cooling crystallization method at ambient pressure and low temperature below 100 °C. The MIL-121 obtained by the novel method possesses the same lithium adsorption performance as that obtained by hydrothermal method, but with lower energy consumption and more environmentally friendly. Compared with hydrothermal method, this method has more advantage to be scaled up to industrialized production. The formation mechanism of MIL-121 in the novel method including nucleation and growth process of MOF crystal was studied. The results indicated that the size and morphology of MIL-121 crystals were influenced by the temperature and additives, respectively. As the reaction temperature increased to 100 °C, the operation time can be shortened to 2–5 h. The crystal habit that was predicted by Material studio software using BFDH, which is a model for crystal habit prediction proposed by Bravais, Friedel, Donnay, and Harker based on the crystal lattice parameters and crystal symmetry in the Morphology module, the simulated morphology of MIL-121 was in accord with that of the products obtained by cooling crystallization. The thermal stability of MIL-121 obtained by cooling crystallization is better than that obtained by the hydrothermal method.
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Chen, Mingyang, Changyou Yu, Menghui Yao, Xinyu Liu, Shijie Xu, Weiwei Tang, Weibing Dong, and Junbo Gong. "The time and location dependent prediction of crystal caking by a modified crystal bridge growth model and DEM simulation considering particle size and shape." Chemical Engineering Science 214 (March 2020): 115419. http://dx.doi.org/10.1016/j.ces.2019.115419.
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Li, Ling, Lu Ming Shen, and Gwénaëlle Proust. "Crystal Plasticity Finite Element Simulations of Polycrystalline Aluminium Alloy under Cyclic Loading." Advanced Materials Research 891-892 (March 2014): 1609–14. http://dx.doi.org/10.4028/www.scientific.net/amr.891-892.1609.
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A three-dimensional crystal plasticity (CP) finite element model is developed to reproduce the grain level stress concentration and deformation of polycrystalline aluminium alloy 7075 (AA7075) during fatigue experiments. The grains contained in the model possess the same size and crystallographic orientations obtained from electron back-scatter diffraction experiments. A modified CP constitutive model, which considers the backstress evolution, is employed to describe the mechanical behaviour of AA7075 under cyclic loading. A round-notched specimen from a fatigue test is simulated using the proposed CP model. Convergence studies in terms of mesh density and plastic deformation zone size are carried out to determine the appropriate conditions for the simulation. The simulation results are compared with those obtained using the elasto-plastic model and the CP model without grain morphology. The comparison indicates that with the embedded grain morphology, the proposed model can capture very well the local response induced by the microstructure features, which is vital to the accurate fatigue life prediction of aluminium alloys.
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Yue, Qing, K. N. Liou, S. C. Ou, B. H. Kahn, P. Yang, and G. G. Mace. "Interpretation of AIRS Data in Thin Cirrus Atmospheres Based on a Fast Radiative Transfer Model." Journal of the Atmospheric Sciences 64, no. 11 (November 1, 2007): 3827–42. http://dx.doi.org/10.1175/2007jas2043.1.
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Abstract A thin cirrus cloud thermal infrared radiative transfer model has been developed for application to cloudy satellite data assimilation. This radiation model was constructed by combining the Optical Path Transmittance (OPTRAN) model, developed for the speedy calculation of transmittances in clear atmospheres, and a thin cirrus cloud parameterization using a number of observed ice crystal size and shape distributions. Numerical simulations show that cirrus cloudy radiances in the 800–1130-cm−1 thermal infrared window are sufficiently sensitive to variations in cirrus optical depth and ice crystal size as well as in ice crystal shape if appropriate habit distribution models are selected a priori for analysis. The parameterization model has been applied to the Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite to interpret clear and thin cirrus spectra observed in the thermal infrared window. Five clear and 29 thin cirrus cases at nighttime over and near the Atmospheric Radiation Measurement program (ARM) tropical western Pacific (TWP) Manus Island and Nauru Island sites have been chosen for this study. A χ2-minimization program was employed to infer the cirrus optical depth and ice crystal size and shape from the observed AIRS spectra. Independent validation shows that the AIRS-inferred cloud parameters are consistent with those determined from collocated ground-based millimeter-wave cloud radar measurements. The coupled thin cirrus radiative transfer parameterization and OPTRAN, if combined with a reliable thin cirrus detection scheme, can be effectively used to enhance the AIRS data volume for data assimilation in numerical weather prediction models.
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Liu, Li, Ramesh Paudel, Yong Liu, Xiao-Liang Zhao, and Jing-Chuan Zhu. "Theoretical and Experimental Studies of the Structural, Phase Stability and Elastic Properties of AlCrTiFeNi Multi-Principle Element Alloy." Materials 13, no. 19 (September 30, 2020): 4353. http://dx.doi.org/10.3390/ma13194353.
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The fundamental challenge for creating the crystal structure model used in a multi-principle element design is the ideal combination of atom components, structural stability, and deformation behavior. However, most of the multi-principle element alloys contain expensive metallic and rare earth elements, which could limit their applicability. Here, a novel design of low-cost AlCrTiFeNi multi-principle element alloy is presented to study the relationship of structure, deformation behavior, and micro-mechanism. This structured prediction of single-phase AlCrTiFeNi by the atomic-size difference, mixing enthalpy ΔHmix and valence electron concentration (VEC), indicate that we can choose the bcc-structured solid solution to design the AlCrTiFeNi multi-principle element alloy. Structural stability prediction by density functional theory calculations (DFT) of single phases has verified that the most advantageous atom occupancy position is (FeCrNi)(AlFeTi). The experimental results showed that the structure of AlCrTiFeNi multi-principle element alloy is bcc1 + bcc2 + L12 phases, which we propose as the fundamental reason for the high strength. Our findings provide a new route by which to design and obtain multi-principle element alloys with targeted properties based on the theoretical predictions, first-principles calculations, and experimental verification.
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Price, Sarah. "Interpreting computed crystal energy landscapes for pharmaceutical molecules." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1615. http://dx.doi.org/10.1107/s2053273314083843.
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Crystal Structure Prediction (CSP) algorithms aim to generate the thermodynamically feasible crystal structures of a molecule from the chemical diagram, ranking their relative stability by a necessarily approximate estimate of the crystal energy. Such calculations are becoming feasible for molecules of a size and flexibility of small molecule pharmaceuticals. Contrasting the crystal energy landscape, the computer generated structures that are thermodynamically plausible as polymorphs, with the results of experimental polymorph screening, shows that CSP studies are not limited to being a search for the most thermodynamically stable crystal structure but can play a valuable role in understanding polymorphism and the potential complexity of crystallisation behaviour.[1] This presentation will illustrate the use of CSP as a complement to industrial-type solid form screening activities. Examples will include olanzapine, [2] tazofelone, two closely related 5-HT2a agonists and 6-[(5-chloro-2-([(4-chloro-2-fluorophenyl)methyl]oxy)phenyl)methyl]-2-pyridinecarboxylic acid (GSK269984B).[3] This illustrates the use of the crystal energy landscape to understand disorder, help structurally characterise metastable polymorphs and suggest whether there are additional polymorphs to be targeted. Since crystal energy landscapes usually include a wider range of crystal structures than known polymorphs, it raises the scientific question as to what determines which structures can be observed as metastable polymorphs. Thus both scientific as well as technological challenges need to be overcome before we can predict polymorphs.
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Ward, Michael D. "Directing the Assembly of Molecular Crystals." MRS Bulletin 30, no. 10 (October 2005): 705–12. http://dx.doi.org/10.1557/mrs2005.206.
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AbstractCrystalline materials made from molecular components can possess useful properties that can be tailored through judicious selection of their molecular building blocks.The utility of these materials, however, depends on molecular packing in the crystal lattice as well as the properties of the individual molecules themselves. Consequently, further advances hinge on our ability to manipulate solid-state structure in a rational and systematic manner. Although computational prediction of crystal structure remains elusive, empirical guidelines for assembling molecules into preordained crystal architectures are emerging rapidly. This article briefly describes the current state of the field, emphasizing the design of crystalline materials with structures reinforced by a twodimensional hydrogen-bonded network, which serves as a platform for the synthesis of a diverse collection of compounds. These include host frameworks with cavities supported by organic “pillars” that can be interchanged to manipulate the size, shape, and character of the inclusion cavities as well as the overall lattice architecture and metrics. This research has revealed some principles for crystal design that may prove useful in general while enabling exploration of the utility of these compounds.
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Mansmann, Ulrich Robert, Ruediger Paul Laubender, Ute Sartorius, Clemens Albrecht Giessen, Anno Graser, and Volker Heinemann. "Improved early prediction of individual prognosis for patients with mCRC: Joint modeling of tumor shrinkage with volume data for PFS and OS." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 3603. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.3603.
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3603 Background: Piessevaux et al. (ESMO 2010) proposed a decrease in RECIST-based tumor size of 20% at week 8 following 1st-line therapy for mCRC as a predictor of clinical outcome (PFS and OS). We expanded upon this idea (ASCO GI 2012). A tumor volume algorithm was developed providing a better approximation to true tumor volume by using both the longest and the longest orthogonal diameters of a target lesion. In this study we compare the quality of early prediction-based individual patient prognosis based on tumor change according to either RECIST or the tumor volume algorithm. Methods: The prognostic method is combined with the volume algorithm and applied to the data from 2 studies (OPUS, n=337; CRYSTAL, n=1198) and 4 treatment regimens (FOLFOX4 +/- cetuximab and FOLFIRI +/- cetuximab), in patients with mCRC. The influence of the treatment regimen on early PFS and OS prognosis is studied by joint modeling. The quality of early prognosis depending on the tumor size assessment used is compared by the logarithmic scoring rule. Results: Individual volume shrinkage and baseline volume are considered prognostic factors. Individual predictions depend on the chemotherapy administered and whether cetuximab is added. Equivalent tumor baseline volumes and early volume changes, predict an up to 20% higher 1 year (y) PFS and 2y OS rate for FOLFIRI than for FOLFOX. The addition of cetuximab to standard chemotherapy (CT) translates into a mean increase in shrinkage of 10% resulting in an improvement in 1y PFS rates of 23% and in 2y OS rates of 18%. The quality of individual prediction over the 4 regimens is combined in one logarithmic (log) score. The log score for the RECIST-based prediction is 2.45 which is significantly higher than that for the volume-based prediction of 1.97 (p<0.0001). Lower scores correspond to a better prediction. Conclusions: The tumor volume algorithm enables a more precise prediction of individual patient PFS and OS than RECIST-based tumor assessments. Based on early tumor changes for CT, +/- cetuximab, it is possible to calculate quantitative information on a patient’s prognosis. The model has high potential to guide individual clinical decision making for mCRC patients.
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Kálmán, Alajos, László Fábián, Gyula Argay, Gábor Bernáth, and Zsuzsanna Gyarmati. "Novel, predicted patterns of supramolecular self-assembly, afforded by tetrameric R_4^4(12) rings of C 2 symmetry in the crystal structures of 2-hydroxy-1-cyclopentanecarboxylic acid, 2-hydroxy-1-cyclohexanecarboxylic acid and 2-hydroxy-1-cycloheptanecarboxylic acid." Acta Crystallographica Section B Structural Science 58, no. 3 (May 29, 2002): 494–501. http://dx.doi.org/10.1107/s0108768102001854.
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Determination of the crystal structures of the homologous (1R*,2R*)-trans-2-hydroxy-1-cyclopentanecarboxylic acid (5T), (1R*,2S*)-cis-2-hydroxy-1-cyclohexanecarboxylic acid (6C) and (1R*,2S*)-cis-2-hydroxy-1-cycloheptanecarboxylic acid (7C) proved a predicted pattern of supramolecular close packing. The prediction was based on the common features observed in the crystal structures of six related 2-hydroxy-1-cyclopentanecarboxylic acids and analogous carboxamides [Kálmán et al. (2001). Acta Cryst. B57, 539–550]. This pattern is characterized by tetrameric R_4^4(12) rings of C 2 symmetry formed from dimeric R_2^2(12) rings. The C 2 symmetry of such tetramers is not common in the literature, usually they have Ci symmetry. Both types of tetramers are formed from dimers with similar or opposite orientation. The R_2^2(12) dimers differ in their hydrogen bonds. In 5T the monomers are joined by a pair of O1—H...O2=C bonds, whereas in 7C they are joined by a pair of O3—H...O1-H bonds. In 6C 60% of the disordered R_2^2(12) dimers are similar to those in 7C, while 40% resemble those in 5T. Apart from these hydrogen-bonding differences and the ring-size differences, the three crystals exhibit isostructurality.
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Zhang, Qinmin, Xiaomin Huang, Ran Guo, and Dongyu Chen. "Thermal Creep Behavior and Creep Crystallization of Al-Mg-Si Aluminum Alloys." Materials 15, no. 22 (November 16, 2022): 8117. http://dx.doi.org/10.3390/ma15228117.
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The experimental temperature is 613.15~763.15 K, and the strain rate is 0.01~10 s−1. The hot compression creep test of the 6082-T6 aluminum alloy sample is carried out by Gleeble-3500 hot compression simulation compressor, and its creep behavior is studied by scanning electron microscope. The results show that the DRX crystal has an irregular shape and that content of the Mg phase, Si phase, and Mn phase in the crystal are the main factors to change the color of DRX crystal. Temperature and strain rate are important factors affecting dynamic recrystallization. Reducing temperature and increasing strain rate will weaken dynamic recrystallization, and DRX critical condition and peak stress (strain) will increase. The constitutive equation of hot creep of 6082 aluminum alloy was established by introducing the work hardening rate-rheological stress curve, and the relationship between DRX critical condition, peak stress (strain) and parameter Z during creep was explored. Based on the Av rami equation, the prediction equation of the DRX volume fraction is established. With the increase of strain, DRX volume fraction is characterized by slow increase, then rapid increase and then slowly increase. In the hot -forming extrusion process of 6082 aluminum alloy, according to the volume fraction prediction equation, the DRX can be reduced, and the internal structure of the material can be optimized by changing the extrusion conditions and particle size.
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Rutherford, J. S. "Theoretical Prediction of Bond-Valence Networks. II. Comparison of the Graph-Matrix and Resonance-Bond Approaches." Acta Crystallographica Section B Structural Science 54, no. 3 (June 1, 1998): 204–10. http://dx.doi.org/10.1107/s0108768197012809.
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This paper provides a common framework for the bond-valence and resonance-bond-number methods, both of which explain the principal variations in inorganic bond lengths from the sum of radii, as arising from the connectivity of the structure, and therefore may apply graph information in conjunction with the Valence-Sum Rule. Under these constraints, possible predictions are limited to specific ranges of (M − N + 1) parameters, where M and N are the size and order of a multigraph describing the crystal motif. Further restrictions on these parameters may arise from non-crystallographic graph symmetries. Convenient graph-theoretical calculation schemes are described for both approaches. As it is possible to identify the best possible prediction within the limits described, which is that most closely corresponding to the experimental result, we have a means of making a direct comparison of the effectiveness of the various methods proposed, as well as being able to evaluate them against a statistically based prediction. The resonance-bond-number method proves to be the better predictor in most cases. Examples analysed in this way comprise KVO3 (potassium metavanadate), α-Ga2O3 (gallium oxide), TeI4 [tellurium(IV) iodide], Li2SiO3 (lithium metasilicate), Li2GeO3 (lithium metagermanate) and CaCrF5 (calcium chromium fluoride).
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Seredyński, Mirosław, and Jerzy Banaszek. "Numerical study of crystal growth kinetics influence on prediction of different dendritic zones and macro-segregation in binary alloy solidification." International Journal of Numerical Methods for Heat & Fluid Flow 30, no. 5 (July 8, 2019): 2363–77. http://dx.doi.org/10.1108/hff-11-2018-0712.
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Purpose Proper selection of the stability parameter determines the accuracy of dendrite tip kinetics at a single crystal scale. Recently developed sophisticated phase field modelling of a single grain evolution provides evidence that this parameter is not constant during the process. Nevertheless, in the commonly used micro-macroscopic simulations of alloy solidification, it is a common practice to use a constant value of the stability parameter, resulting from the marginal stability theory. This paper aims to address the issue of how this inaccuracy in modelling crystal growth kinetics can influence numerically predicted zones of columnar and equiaxed dendrites and the macro-segregation formation. Design/methodology/approach Using the original authors’ micro-macroscopic computer simulation model of binary alloy solidification, the calculations have been performed for the Kurz-Giovanola-Trivedi (KGT) crystal growth kinetics with two different values of the stability parameter, and for two different compositions of Al-Cu alloys. The computational model is based on single domain-based formulation of transport equations, which are discretized on control-volume mesh. To identify zones of different grain structures, developing within the two-phase liquid-solid region, an envelope of columnar dendrite tips is tracked on a fixed non-orthogonal, triangular control volume grid. The models of porous and slurry media are used, along with the concept of the switching function, to account for diverse flow resistances in the columnar and equiaxed crystal zones. The numerical predictions are carefully studied to address the question of how the chosen stability parameter influences macroscopic structures of a cast, the most important issue from the engineering point of view. Findings The carried-out comprehensive numerical analysis shows that the value of the stability parameter of the KGT-constrained dendrite growth model does not have a direct significant impact on the macrosegregation formation. It, however, visibly influences the undercooling along the front, separating different dendritic structures and the size of the undercooled melt region where the equiaxed grains can develop. It also affects the amount of eutectic phase created. Originality/value To the best of the authors’ knowledge, this is the first attempt at estimating the influence of some inaccuracies, caused by possible ambiguities in choosing the stability constant of the KGT law, on numerically predicted macroscopic fields of solute concentration, the developing zones of columnar and equiaxed crystals and the macrosegregation patterns.
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Jeong, Young-Seob, Juhyun Kim, Dahye Kim, Jiyoung Woo, Mun Gyu Kim, Hun Woo Choi, Ah Reum Kang, and Sun Young Park. "Prediction of Postoperative Complications for Patients of End Stage Renal Disease." Sensors 21, no. 2 (January 14, 2021): 544. http://dx.doi.org/10.3390/s21020544.
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End stage renal disease (ESRD) is the last stage of chronic kidney disease that requires dialysis or a kidney transplant to survive. Many studies reported a higher risk of mortality in ESRD patients compared with patients without ESRD. In this paper, we develop a model to predict postoperative complications, major cardiac event, for patients who underwent any type of surgery. We compare several widely-used machine learning models through experiments with our collected data yellow of size 3220, and achieved F1 score of 0.797 with the random forest model. Based on experimental results, we found that features related to operation (e.g., anesthesia time, operation time, crystal, and colloid) have the biggest impact on model performance, and also found the best combination of features. We believe that this study will allow physicians to provide more appropriate therapy to the ESRD patients by providing information on potential postoperative complications.
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Martinez, Vincent A., Eric Clément, Jochen Arlt, Carine Douarche, Angela Dawson, Jana Schwarz-Linek, Adama K. Creppy, et al. "A combined rheometry and imaging study of viscosity reduction in bacterial suspensions." Proceedings of the National Academy of Sciences 117, no. 5 (January 21, 2020): 2326–31. http://dx.doi.org/10.1073/pnas.1912690117.
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Suspending self-propelled “pushers” in a liquid lowers its viscosity. We study how this phenomenon depends on system size in bacterial suspensions using bulk rheometry and particle-tracking rheoimaging. Above the critical bacterial volume fraction needed to decrease the viscosity to zero, ϕc≈0.75%, large-scale collective motion emerges in the quiescent state, and the flow becomes nonlinear. We confirm a theoretical prediction that such instability should be suppressed by confinement. Our results also show that a recent application of active liquid-crystal theory to such systems is untenable.
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Akpama, Holanyo K., Mohamed Ben Bettaieb, and Farid Abed-Meraim. "Prediction of Localized Necking Based on Crystal Plasticity: Comparison of Bifurcation and Imperfection Approaches." Key Engineering Materials 716 (October 2016): 779–89. http://dx.doi.org/10.4028/www.scientific.net/kem.716.779.
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In the present work, a powerful modeling tool is developed to predict and analyze the onset of strain localization in polycrystalline aggregates. The predictions of localized necking are based on two plastic instability criteria, namely the bifurcation theory and the initial imperfection approach. In this tool, a micromechanical model, based on the self-consistent scale-transition scheme, is used to accurately derive the mechanical behavior of polycrystalline aggregates from that of their microscopic constituents (the single crystals). The mechanical behavior of the single crystals is developed within a large strain rate-independent constitutive framework. This micromechanical constitutive modeling takes into account the essential microstructure-related features that are relevant at the microscale. These microstructural aspects include key physical mechanisms, such as initial and induced crystallographic textures, morphological anisotropy and interactions between the grains and their surrounding medium. The developed tool is used to predict sheet metal formability through the concept of forming limit diagrams (FLDs). The results obtained by the self-consistent averaging scheme, in terms of predicted FLDs, are compared with those given by the more classical full-constraint Taylor model. Moreover, the predictions obtained by the imperfection approach are systematically compared with those given by the bifurcation analysis, and it is demonstrated that the former tend to the latter in the limit of a vanishing size for the initial imperfection.
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Cui, Yehui, Xiangguo Zeng, Junfeng Xiao, and Fang Wang. "Micro-damage evolution under intensive dynamic loading and its influence on constitutive and state equations for nanocrystalline NiTi alloy through molecular dynamics." Journal of Applied Physics 131, no. 17 (May 7, 2022): 174301. http://dx.doi.org/10.1063/5.0087504.
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In this study, to comprehensively reveal the damage mechanisms of NiTi alloys, molecular dynamics (MD) simulations were applied to examine the void evolution process under uniaxial and triaxial intensive dynamic loading. A single-crystal model was first used in the MD simulations. The calculation results revealed that the single-crystal NiTi model exhibited a similar damage response to brittle fracture. The corresponding damage mechanism was the rapid growth and coalescence of voids inside the material. Meanwhile, the defect influence was also examined for the single-crystal model, and the reduction effect of the ultimate stress value due to the stress concentration was analyzed quantitatively by the MD simulations. In addition, a polycrystalline model of NiTi was used in the MD simulations. Compared with the single-crystal model, the polycrystalline model showed an evident plastic stage under uniaxial loading due to dislocation slip. The MD simulation proved that the dislocations accumulated on the grain boundaries, which led to a stress concentration effect on the grain boundaries and sequentially resulted in void generation. However, the propagation and coalescence of voids were hindered by the grain interactions, which resulted in a ductile damage behavior inside the material. Based on this mechanism, the grain size influence was also studied in the MD simulations. It was discovered that the grain size effect in the damage stage resulted in a damage ductility enhancement with the decrease in the average grain size value. Finally, based on the relationships between the stress-strain curve, void fraction, and damage behavior, novel constitutive and state equations were proposed with damage terms to consider the void evolution process during the damage stage. The prediction results showed good agreement with the MD simulation data.
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Ou, Chun-Yu, Rohit Voothaluru, and C. Richard Liu. "A Methodology for Incorporating the Effect of Grain Size on the Energy Efficiency Coefficient for Fatigue Crack Initiation Estimation in Polycrystalline Metal." Metals 10, no. 3 (March 9, 2020): 355. http://dx.doi.org/10.3390/met10030355.
Full textAbstract:
Estimating fatigue crack initiation of applied loading is challenging due to the large number of individual entities within a microstructure that could affect the accumulation of dislocations. In order to improve the prediction accuracy of fatigue crack initiation models, it is essential to accurately compute the energy dissipated into the microstructure per fatigue loading cycle. The extent of the energy dissipated within the microstructure as a fraction of the overall energy imparted by loading has previously been defined as the ‘energy efficiency coefficient’. This work studied the energy efficiency coefficient as a factor in the measurement of accumulated plastic strain energy stored at the crack initiation site during cyclic loading. In particular, the crystal plasticity constitutive formulation was known as ’length scale independent’ previously. As a result, a semi-empirical approach was presented whereby the potential effect of grain size can be accounted for without the use of a strain gradient plasticity approach. The randomized representative volume elements were created based on the experimental analysis of grain size distribution. The work was aimed at capturing some of the effects of grain size and utilizing them to complete a semi-empirical estimation of crack initiation in polycrystalline materials. The computational methodology ensured the representative of microstructural properties, including the elastic constant and critical resolved shear stress via appreciable fit achieved with the empirical tensile test results. Crystal plasticity finite element modeling was incorporated into a finite element code to estimate the potential for crack initiation. The energy efficiency coefficient was computed for a class of material with grain size to C11000 electrolytic tough pitch (ETP) copper. This methodology can improve fatigue crack initiation life estimation and advance the fundamental study of energy efficiency coefficient during fatigue crack initiation.
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Rivera-Díaz-del-Castillo, Pedro E. J., and Sybrand van der Zwaag. "A Statistical Mechanics Theory of Grain Deformation and Its Prediction of Dynamical Recovery and Recrystallization." Materials Science Forum 467-470 (October 2004): 87–92. http://dx.doi.org/10.4028/www.scientific.net/msf.467-470.87.
Full textAbstract:
A novel statistical mechanics approach to quantify the effects of hot rolling and deformation on the formation of dislocations in a single grain scenario is presented. The dislocations are dealt as equilibrium defects in the crystal structure, which is assumed to be deformed via the formation of dislocations or single atom displacements at the grain boundary, which involve breaking their bonds and are thus termed “bond breaking atoms”. The deformation process is applied to steels of a variety of grain size and dislocations densities. The model has the capacity to describe the grain energy increase as a function of crystallography, grain sizes, temperature and degree of deformation, providing thus an aid in predicting the conditions for dynamic recovery and recrystallization.
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Kassemi, Mohammad, and David Thompson. "Prediction of renal crystalline size distributions in space using a PBE analytic model. 2. Effect of dietary countermeasures." American Journal of Physiology-Renal Physiology 311, no. 3 (September 1, 2016): F531—F538. http://dx.doi.org/10.1152/ajprenal.00402.2015.
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An analytic Population Balance Equation model is used to assess the efficacy of citrate, pyrophosphate, and augmented fluid intake as dietary countermeasures aimed at reducing the risk of renal stone formation for astronauts. The model uses the measured biochemical profile of the astronauts as input and predicts the steady-state size distribution of the nucleating, growing, and agglomerating renal calculi subject to biochemical changes brought about by administration of these dietary countermeasures. Numerical predictions indicate that an increase in citrate levels beyond its average normal ground-based urinary values is beneficial but only to a limited extent. Unfortunately, results also indicate that any decline in the citrate levels during space travel below its normal urinary values on Earth can easily move the astronaut into the stone-forming risk category. Pyrophosphate is found to be an effective inhibitor since numerical predictions indicate that even at quite small urinary concentrations, it has the potential of shifting the maximum crystal aggregate size to a much smaller and plausibly safer range. Finally, our numerical results predict a decline in urinary volume below 1.5 liters/day can act as a dangerous promoter of renal stone development in microgravity while urinary volume levels of 2.5–3 liters/day can serve as effective space countermeasures.