Modeling low energy sputtering of hexagonal boron nitride by xenon ions

The sputtering of hexagonal boron nitride due to low energy xenon ion bombardments occurs in various applications including fabrication of cubic boron nitride and erosion of Hall thruster channel walls. At low ion energies, accurate experimental characterization of sputter yields increases in difficulty due to the low yields involved. A molecular dynamics model is employed to simulate the sputtering process and to calculate sputter yields for ion energies ranging from 10 eV to 350 eV. The results are compared to experimental data and a semi-empirical expression developed by Bohdansky is found to adequately describe the simulation data. Surface temperature effects are also investigated, and the sputter yield at 850 K is approximately twice that at 423 K.Comment: 19 pages, 8 figure

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Analytical and discrete solutions for the incipient motion of ellipsoidal sediment particles

[EN] This work introduces analytical and numerical approaches to compute the incipient motion of ellipsoidal sediment particles. Initiation of motion of spherical particles is dominated by rolling mode. However, solutions for initiation of motion for non-spherical grains have to incorporate rolling, sliding, and mixed modes. The proposed approaches include a wide variety of shapes and inclinations that represent realistic configurations of sediment bed layers. The numerical procedure is based on the discrete element method, simulating the micro-mechanics of the sediment as an aggregate of rigid ellipsoids interacting by contact. The numerical solution covers a range of incipient movements that cannot be covered by the analytical approach. Hence, some trapped modes observed in analytical calculations are complemented by the numerical computation of threshold stresses. The main results are organized as novel extended Shields diagrams for non-spherical grains, where non-dimensional critical shear stress is represented in terms of friction Reynolds number.This work was supported by the Ministerio de Ciencia e Innovación Grant [#BIA-2012-32918 and #BIA-2015-64994-P (MINECO/FEDER)].Bravo, R.; Ortiz, P.; Pérez-Aparicio, JL. (2018). Analytical and discrete solutions for the incipient motion of ellipsoidal sediment particles. Journal of Hydraulic Research. 56(1):29-43. https://doi.org/10.1080/00221686.2017.1289263S2943561Belytschko, T., & Neal, M. O. (1991). Contact-impact by the pinball algorithm with penalty and Lagrangian methods. International Journal for Numerical Methods in Engineering, 31(3), 547-572. doi:10.1002/nme.1620310309Bravo, R., Ortiz, P., & Pérez-Aparicio, J. L. (2014). Incipient sediment transport for non-cohesive landforms by the discrete element method (DEM). Applied Mathematical Modelling, 38(4), 1326-1337. doi:10.1016/j.apm.2013.08.010Bravo, R., Pérez-Aparicio, J. L., & Gómez-Hernández, J. J. (2015). Numerical sedimentation particle-size analysis using the Discrete Element Method. Advances in Water Resources, 86, 58-72. doi:10.1016/j.advwatres.2015.09.024Bravo, R., Pérez-Aparicio, J. L., & Laursen, T. A. (2012). An energy consistent frictional dissipating algorithm for particle contact problems. International Journal for Numerical Methods in Engineering, 92(9), 753-781. doi:10.1002/nme.4346Buffington, J. M., & Montgomery, D. R. (1997). A systematic analysis of eight decades of incipient motion studies, with special reference to gravel-bedded rivers. Water Resources Research, 33(8), 1993-2029. doi:10.1029/96wr03190Cheng, N.-S., & Chiew, Y.-M. (1999). Incipient sediment motion with upward seepage. Journal of Hydraulic Research, 37(5), 665-681. doi:10.1080/00221689909498522Chiew, Y.-M., & Parker, G. (1994). Incipient sediment motion on non-horizontal slopes. Journal of Hydraulic Research, 32(5), 649-660. doi:10.1080/00221689409498706Derksen, J. J. (2015). Simulations of granular bed erosion due to a mildly turbulent shear flow. Journal of Hydraulic Research, 53(5), 622-632. doi:10.1080/00221686.2015.1077354Dey, S. (1999). Sediment threshold. Applied Mathematical Modelling, 23(5), 399-417. doi:10.1016/s0307-904x(98)10081-1Dey, S. (2003). Threshold of sediment motion on combined transverse and longitudinal sloping beds. Journal of Hydraulic Research, 41(4), 405-415. doi:10.1080/00221680309499985Dey, S., Sarker, H. K. D., & Debnath, K. (1999). Sediment Threshold under Stream Flow on Horizontal and Sloping Beds. Journal of Engineering Mechanics, 125(5), 545-553. doi:10.1061/(asce)0733-9399(1999)125:5(545)Hölzer, A., & Sommerfeld, M. (2008). New simple correlation formula for the drag coefficient of non-spherical particles. Powder Technology, 184(3), 361-365. doi:10.1016/j.powtec.2007.08.021James, C. S. (1990). Prediction of entrainment conditions for nonuniform, noncohesive sediments. Journal of Hydraulic Research, 28(1), 25-41. doi:10.1080/00221689009499145Ji, C., Munjiza, A., Avital, E., Ma, J., & Williams, J. J. R. (2013). Direct numerical simulation of sediment entrainment in turbulent channel flow. Physics of Fluids, 25(5), 056601. doi:10.1063/1.4807075Klamkin, M. S. (1971). Elementary Approximations to the Area of N-Dimensional Ellipsoids. The American Mathematical Monthly, 78(3), 280. doi:10.2307/2317530Mandø, M., & Rosendahl, L. (2010). On the motion of non-spherical particles at high Reynolds number. Powder Technology, 202(1-3), 1-13. doi:10.1016/j.powtec.2010.05.001MILLER, M. C., McCAVE, I. N., & KOMAR, P. D. (1977). Threshold of sediment motion under unidirectional currents. Sedimentology, 24(4), 507-527. doi:10.1111/j.1365-3091.1977.tb00136.xWan Mohtar, W. H. M., & Munro, R. J. (2013). Threshold criteria for incipient sediment motion on an inclined bedform in the presence of oscillating-grid turbulence. Physics of Fluids, 25(1), 015103. doi:10.1063/1.4774341Ortiz, P., & Smolarkiewicz, P. K. (2006). Numerical simulation of sand dune evolution in severe winds. International Journal for Numerical Methods in Fluids, 50(10), 1229-1246. doi:10.1002/fld.1138Ortiz, P., & Smolarkiewicz, P. K. (2009). Coupling the dynamics of boundary layers and evolutionary dunes. Physical Review E, 79(4). doi:10.1103/physreve.79.041307Van Rijn, L. C. (1984). Sediment Transport, Part I: Bed Load Transport. Journal of Hydraulic Engineering, 110(10), 1431-1456. doi:10.1061/(asce)0733-9429(1984)110:10(1431)Shi, G.-H., & Goodman, R. E. (1985). Two dimensional discontinuous deformation analysis. International Journal for Numerical and Analytical Methods in Geomechanics, 9(6), 541-556. doi:10.1002/nag.1610090604Shields, A. (1936). Application of similarity principles and turbulence research to bed-load movement (Tech. Rep.). Lab. for Hydraulic Water Resources.Wellmann, C., Lillie, C., & Wriggers, P. (2008). A contact detection algorithm for superellipsoids based on the common‐normal concept. Engineering Computations, 25(5), 432-442. doi:10.1108/02644400810881374Wiberg, P. L., & Smith, J. D. (1985). A theoretical model for saltating grains in water. Journal of Geophysical Research, 90(C4), 7341. doi:10.1029/jc090ic04p0734

Back-flow ripples in troughs downstream of unit bars: Formation, preservation and value for interpreting flow conditions

Back-flow ripples are bedforms created within the lee-side eddy of a larger bedform with migration directions opposed or oblique to that of the host bedform. In the flume experiments described in this article, back-flow ripples formed in the trough downstream of a unit bar and changed with mean flow velocity; varying from small incipient back-flow ripples at low velocities, to well-formed back-flow ripples with greater velocity, to rapidly migrating transient back-flow ripples formed at the greatest velocities tested. In these experiments back-flow ripples formed at much lower mean back-flow velocities than predicted from previously published descriptions. This lower threshold mean back-flow velocity is attributed to the pattern of velocity variation within the lee-side eddy of the host bedform. The back-flow velocity variations are attributed to vortex shedding from the separation zone, wake flapping and increases in the size of, and turbulent intensity within, the flow separation eddy controlled by the passage of superimposed bedforms approaching the crest of the bar. Short duration high velocity packets, whatever their cause, may form back-flow ripples if they exceed the minimum bed shear stress for ripple generation for long enough or, if much faster, may wash them out. Variation in back-flow ripple cross-lamination has been observed in the rock record and, by comparison with flume observations, the preserved back-flow ripple morphology may be useful for interpreting formative flow and sediment transport dynamics

Associations with photoreceptor thickness measures in the UK Biobank.

Spectral-domain OCT (SD-OCT) provides high resolution images enabling identification of individual retinal layers. We included 32,923 participants aged 40-69 years old from UK Biobank. Questionnaires, physical examination, and eye examination including SD-OCT imaging were performed. SD OCT measured photoreceptor layer thickness includes photoreceptor layer thickness: inner nuclear layer-retinal pigment epithelium (INL-RPE) and the specific sublayers of the photoreceptor: inner nuclear layer-external limiting membrane (INL-ELM); external limiting membrane-inner segment outer segment (ELM-ISOS); and inner segment outer segment-retinal pigment epithelium (ISOS-RPE). In multivariate regression models, the total average INL-RPE was observed to be thinner in older aged, females, Black ethnicity, smokers, participants with higher systolic blood pressure, more negative refractive error, lower IOPcc and lower corneal hysteresis. The overall INL-ELM, ELM-ISOS and ISOS-RPE thickness was significantly associated with sex and race. Total average of INL-ELM thickness was additionally associated with age and refractive error, while ELM-ISOS was additionally associated with age, smoking status, SBP and refractive error; and ISOS-RPE was additionally associated with smoking status, IOPcc and corneal hysteresis. Hence, we found novel associations of ethnicity, smoking, systolic blood pressure, refraction, IOPcc and corneal hysteresis with photoreceptor thickness

Measurement of 30-Centimeter Ion Thruster Discharge Cathode Erosion

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76209/1/AIAA-22982-649.pd

Observations of bedforms on a dissipative macrotidal beach

NERC NE/H004262/1 and NE/H02543X/1 DRIB

Digital karyotyping reveals probable target genes at 7q21.3 locus in hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>Hepatocellular carcinoma (HCC) is a worldwide malignant liver tumor with high incidence in China. Subchromosomal amplifications and deletions accounted for major genomic alterations occurred in HCC. Digital karyotyping was an effective method for analyzing genome-wide chromosomal aberrations at high resolution.</p> <p>Methods</p> <p>A digital karyotyping library of HCC was constructed and 454 Genome Sequencer FLX System (Roche) was applied in large scale sequencing of the library. Digital Karyotyping Data Viewer software was used to analyze genomic amplifications and deletions. Genomic amplifications of genes detected by digital karyotyping were examined by real-time quantitative PCR. The mRNA expression level of these genes in tumorous and paired nontumorous tissues was also detected by real-time quantitative RT-PCR.</p> <p>Results</p> <p>A total of 821,252 genomic tags were obtained from the digital karyotyping library of HCC, with 529,162 tags (64%) mapped to unique loci of human genome. Multiple subchromosomal amplifications and deletions were detected through analyzing the digital karyotyping data, among which the amplification of 7q21.3 drew our special attention. Validation of genes harbored within amplicons at 7q21.3 locus revealed that genomic amplification of SGCE, PEG10, DYNC1I1 and SLC25A13 occurred in 11 (21%), 11 (21%), 11 (21%) and 23 (44%) of the 52 HCC samples respectively. Furthermore, the mRNA expression level of SGCE, PEG10 and DYNC1I1 were significantly up-regulated in tumorous liver tissues compared with corresponding nontumorous counterparts.</p> <p>Conclusions</p> <p>Our results indicated that subchromosomal region of 7q21.3 was amplified in HCC, and SGCE, PEG10 and DYNC1I1 were probable protooncogenes located within the 7q21.3 locus.</p