Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 36, 28 May 2024


Open Access | Article

Using Brownian model to study the effect of temperature on diffusion coefficient

Xiaoyun Wu * 1
1 Pinghe bilingual school

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 36, 48-57
Published 28 May 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Xiaoyun Wu. Using Brownian model to study the effect of temperature on diffusion coefficient. TNS (2024) Vol. 36: 48-57. DOI: 10.54254/2753-8818/36/20240511.

Abstract

This study employs molecular simulation techniques, particularly the Brownian motion model, to investigate the influence of temperature on diffusion coefficients. Leveraging Einstein’s relationship for calculating diffusion coefficients and a one-dimensional random walk model, we systematically explore the impact of simulation direction, particle quantity, and simulation duration on diffusion behavior. The research extends its scope to three-dimensional Brownian motion simulations, offering insights into the stochastic motion of particles in a fluid. The primary objective is to analyze the relationship between temperature and diffusion coefficients. Through rigorous linear regression analysis, a significant and strong linear association is identified, demonstrating that an increase in temperature correlates with an increase in the diffusion coefficient. The research not only contributes to the fundamental understanding of molecular motion but also provides practical recommendations for simulation parameters, especially in resource-constrained computational environments. The study acknowledges certain limitations in the current Brownian motion algorithm and proposes avenues for future research to enhance computational efficiency and precision. This abstract encapsulates the key methodologies, findings, and implications of the research, laying the foundation for a comprehensive exploration of temperature-dependent diffusion coefficients in molecular systems.

Keywords

Brownian motion, Diffusion coefficient, Temperature-dependent dynamics, Random walk simulations

References

1. Frenkel, D., & Smit, B. (2023). Understanding molecular simulation: from algorithms to applications. Elsevier.

2. Hartley, G. S., & Crank, J. F. (1949). Some fundamental definitions and concepts in diffusion processes. Transactions of the Faraday Society, 45, 801-818.

3. Maxwell, J. C. (1867). IV. On the dynamical theory of gases. Philosophical transactions of the Royal Society of London, (157), 49-88.

4. Einstein, A. (1905). On the motion of small particles suspended in liquids at rest required by the molecular-kinetic theory of heat. Annalen der physik, 17(549-560), 208.

5. Schilling, R. L., & Partzsch, L. (2014). Brownian motion: an introduction to stochastic processes. Walter de Gruyter GmbH & Co KG.

6. Ratcliff, L. E., Mohr, S., Huhs, G., Deutsch, T., Masella, M., & Genovese, L. (2017). Challenges in large scale quantum mechanical calculations. Wiley Interdisciplinary Reviews: Computational Molecular Science, 7(1), e1290.

7. Mazo, R. M. (2008). Brownian motion: fluctuations, dynamics, and applications (Vol. 112). OUP Oxford.

8. Codling, E. A., Plank, M. J., & Benhamou, S. (2008). Random walk models in biology. Journal of the Royal society interface, 5(25), 813-834.

9. Einstein, A. (1956). Investigations on the Theory of the Brownian Movement. Courier Corporation.

10. Karlin, S. (2014). A first course in stochastic processes. Academic press.

11. Codling, E. A., Plank, M. J., & Benhamou, S. (2008). Random walk models in biology. Journal of the Royal society interface, 5(25), 813-834.

12. Witte, R. S., & Witte, J. S. (2017). Statistics. John Wiley & Sons.

13. Yap, B. W., & Sim, C. H. (2011). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation, 81(12), 2141-2155.

14. Augustin, N. H., Sauleau, E. A., & Wood, S. N. (2012). On quantile quantile plots for generalized linear models. Computational Statistics & Data Analysis, 56(8), 2404-2409.

15. Uhlenbeck, G. E., & Ornstein, L. S. (1930). On the theory of the Brownian motion. Physical review, 36(5), 823.

16. Sekimoto, K. (1998). Langevin equation and thermodynamics. Progress of Theoretical Physics Supplement, 130, 17-27.

17. Zuckerman, D. M. (2010). Statistical physics of biomolecules: an introduction. CRC press.

18. Dieker, T. (2004). Simulation of fractional Brownian motion (Doctoral dissertation, Masters Thesis, Department of Mathematical Sciences, University of Twente, The Netherlands).

19. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.

20. Seber, G. A., & Lee, A. J. (2003). Linear regression analysis (Vol. 330). John Wiley & Sons.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Mathematical Physics and Computational Simulation
ISBN (Print)
978-1-83558-441-5
ISBN (Online)
978-1-83558-442-2
Published Date
28 May 2024
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/36/20240511
Copyright
28 May 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated