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An efficient and accurate method to calculate diffusion coefficient of structured particles. A first case study of Pb diffusion in rare gases

Abstract

Diffusion coefficient depends on temperature, pressure, reduced mass of colliding particles and collision cross section. The presented method is designed to calculate the diffusion coefficient in loose systems containing molecules with relatively complicated colliding trajectories. It is a combination of the Chapman-Enskog theory and the molecular dynamics calculation. The Chapman-Enskog theory provides the relation between the diffusion coefficient and the collision cross section which is the result of multiple integration of the scattering angle of all possible initial conditions of the collision. The scattering angle is obtained by numerical integration of the Newton’s equation of motion with previously selected initial conditions. The proposed method has been verified for the simple system of a lead atom diffusion in rare gases and the results were compared to those of two other theoretical methods.

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Correlations among various factors influencing learning of chemistry

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Trichoderma atroviride: an isolate from forest environment with secondary metabolites with high antimicrobial potential

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Batch drying of sliced tomatoes at specific ambient conditions

, Salmon T, Plougonven E, Leonard A (2016) Journal of Engineering Science and Technology 3: 443–457. Kumer S, Akanda MAR, Biswas DK, Roy A, Khatun MA, Goffar SK (2016) Journal of Integrative Agriculture 15: 2380–2392. Lucini L, Rocchetti G, Kane D, Trevisan M (2017) Food Control 73: 696–703. Movagharnejad K, Maryam N (2007) Computers and Electronics in Agriculture 59: 78–85. Ruiz CA, Cuadros F, López-Rodríguez F (2009) food and bioproducts processing 87: 282–291. Sacilik K, Keskin R, Konuralp EA (2006) Journal of Food Engineering 73: 231

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How can pupils see what is invisible?: Possibilities of inquiry probeware experiment implementation in primary schools

Education Research Association; 2005. https://repository.nie.edu.sg/bitstream/10497/2737/1/datalogging_ASERA.pdf . [13] Sorgo A, Kocijancic S. False reality or hidden messages: Reading graphs obtained in computerized biological experiments. Eurasia J Math Sci T. 2012;8(2):129-135. DOI: 10.12973/eurasia.2012.826a. [14] Bingimlas KA. Barriers to the successful integration of ICT in teaching and learning environments. Eurasia J Math Sci T. 2009;5(3):235-245. DOI: 10.12973/ejmste/75275. [15] Corbin J, Strauss A. Basics of Qualitative Research: Techniques

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Analysis of C3 fraction splitting system performance by mathematical modeling in MATLAB environment

, Corsten M, Galitsky Ch (2015) Energy Efficiency Improvement and Cost Saving Opportunities for Petroleum Refineries. An ENERGY STAR® Guide for Energy and Plant Managers. A document prepared for The United States Environmental Protection Agency. www.energystar.gov/industry . Accessed on 17. 1. 2019. You X, Rodriguez-Donis I, Gerbaud V (2016) Reducing process cost and CO 2 emissions for extractive distillation by double-effect heat integration and mechanical heat pump.Applied Energy, 166: 128—140.

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Attitudes among chemistry teachers towards increasing personal competencies in applying ICT

teacher knowledge. Teachers College Record. 2006;108(6):1017-1054. DOI: 10.1111/j.1467-9620.2006.00684.x. [21] So H, Kim B. Learning about problem based learning: Student teachers integrating technology, pedagogy and content knowledge. Austral J Educat Technol. 2009;25(1):101-116. DOI: 10.14742/ajet.1183. [22] Doering A, Hughes J, Huffman D. Preservice teachers: Are we thinking with technology? J Res Technol Educat. 2003;35(3):342-361. DOI: 10.1080/15391523.2003.10782390. [23] Brand GA. What research says: Training teachers for using technology. J

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