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Non-equilibrium transport is an important research area in statistical physics. The influences of the structures of polyatomic molecules on their transport have attracted the attention of researchers. Up to now, most of researchers deemed that temperature gradient is the main factor for molecular orientation and neglected the effect of the chemical potential gradient on the molecular orientation. To make up the deficiency in the study of chemical potential gradients, we build a non-equilibrium system with both chemical potential gradient and temperature gradient, and study the transport diffusion behavior of asymmetric diatomic molecules by using molecular dynamics and Monte Carlo methods. It is found that the diatomic molecules implement the orientation effect during non-equilibrium transport. Under the chemical potential gradient, the molecular orientation effect leads to the fact that the large atom tends to be in the direction of low concentration particle bath, while the small atom tends to be in the direction of high concentration particle bath. The molecular orientation is opposite to the direction of the flow. Under the temperature gradient, the molecular orientation effect leads to the fact that the large atom tends to be in the direction of high temperature particle bath, while the small atom tends to be in the direction of low temperature particle bath. The molecular orientation is the same as the direction of the flow. The orientation direction caused by concentration gradients is opposite to that caused by temperature gradients and it appears as a competitive relationship. At the same time, the influence of the asymmetry of the molecule itself on the molecular orientation is also studied. The larger the asymmetry of the molecule itself (σB/σA), the more obvious the molecular orientation effect is. When σB/σA>1.6, the influence of the asymmetry of the molecule itself on the orientation effect is gradually saturated. When σB/σA=1, which is also for a symmetric molecule, even if neither the temperature gradient nor the chemical potential gradient is zero, no molecular orientation occurs. We explain the physical mechanism of orientation through the principle of minimum entropy production. This work is of theoretical significance for in depth understanding the relationship between mass transport and molecular structure under non-equilibrium conditions.
[1] Karger J, Grinberg F, Heitjans P 2005 Diffusion Fundamentals (Leipzig: Leipziger Universitatsverlag) p80
[2] Skoulidas A I, Sholl D S 2002 Phys. Chem. B 106 5058
[3] Papadopoulos G K, Jobic H, Theodorou D N 2004 J. Phys. Chem. B 108 12748
[4] Mutat T, Adler J, Sheintuch M 2012 J. Chem. Phys. 136 234902
[5] Xu Z C, Zheng D Q, Ai B Q, Hu B, Zhong W R 2015 AIP Adv. 5 107145
[6] Salles F, Jobic H, Devic T, Llewellyn P L, Serre C, Ferey G, Maurin G 2010 ACS Nano 4 143
[7] Romer F, Bresme F, Muscatello J, Bedeaux D, Rubi J M 2012 Phys. Rev. Lett. 108 105901
[8] Lee A A 2016 Soft Matter 12 8661
[9] Tan Z H, Yang M C, Pipoll M 2017 Soft Matter 13 7283
[10] Gustavsson K, Jucha J, Naso A, Leveque E, Pumir A, Mehilg B 2017 Phys. Rev. Lett. 119 254501
[11] Kiharu A, Him K D 2017 J. Phys. Chem. Lett. 8 3595
[12] Peter W, Domagoj F, Roger A L, Andela S, Christoph D, Daan F 2017 PNAS 114 4911
[13] Christopher D D, Joakim T, Signe K, Dick B, Fernando B 2016 Phys. Chem. Chem. Phys. 18 12213
[14] Kubo R 1957 J. Phys. Soc. Jpn. 12 570
[15] Seifert U 2005 Phys. Rev. Lett. 95 040602
[16] Girifalco L A, Hodak M, Lee R S 2000 Phys. Rev. B 62 13104
[17] Chen Z L 2007 Theory and Practice of Molecular Simulation (Beijing: Chemical Industry Press) p9 (in Chinese) [陈正隆 2007 分子模拟的理论与实践 (北京: 化学工业出版社) 第9页]
[18] Allen M P, Tildesley D J 1987 Computer Simulation of Liquids (1st Ed.) (Oxford: Oxford University Press) p13
[19] Tabar H R 2008 Computational Physics of Carbon Nanotubes (1st Ed.) (Cambridge: Cambridge University Press) p113
[20] Landau D P, Binder K 2014 A Guide to Monte Carlo Simulations in Statistical Physics (2nd Ed.) (Cambridge: Cambridge University Press) p196
[21] Adams D J 1975 Mol. Phys. 29 307
[22] Chen P R, Xu Z C, Gu Y, Zhong W R 2016 Chin. Phys. B 25 086601
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[1] Karger J, Grinberg F, Heitjans P 2005 Diffusion Fundamentals (Leipzig: Leipziger Universitatsverlag) p80
[2] Skoulidas A I, Sholl D S 2002 Phys. Chem. B 106 5058
[3] Papadopoulos G K, Jobic H, Theodorou D N 2004 J. Phys. Chem. B 108 12748
[4] Mutat T, Adler J, Sheintuch M 2012 J. Chem. Phys. 136 234902
[5] Xu Z C, Zheng D Q, Ai B Q, Hu B, Zhong W R 2015 AIP Adv. 5 107145
[6] Salles F, Jobic H, Devic T, Llewellyn P L, Serre C, Ferey G, Maurin G 2010 ACS Nano 4 143
[7] Romer F, Bresme F, Muscatello J, Bedeaux D, Rubi J M 2012 Phys. Rev. Lett. 108 105901
[8] Lee A A 2016 Soft Matter 12 8661
[9] Tan Z H, Yang M C, Pipoll M 2017 Soft Matter 13 7283
[10] Gustavsson K, Jucha J, Naso A, Leveque E, Pumir A, Mehilg B 2017 Phys. Rev. Lett. 119 254501
[11] Kiharu A, Him K D 2017 J. Phys. Chem. Lett. 8 3595
[12] Peter W, Domagoj F, Roger A L, Andela S, Christoph D, Daan F 2017 PNAS 114 4911
[13] Christopher D D, Joakim T, Signe K, Dick B, Fernando B 2016 Phys. Chem. Chem. Phys. 18 12213
[14] Kubo R 1957 J. Phys. Soc. Jpn. 12 570
[15] Seifert U 2005 Phys. Rev. Lett. 95 040602
[16] Girifalco L A, Hodak M, Lee R S 2000 Phys. Rev. B 62 13104
[17] Chen Z L 2007 Theory and Practice of Molecular Simulation (Beijing: Chemical Industry Press) p9 (in Chinese) [陈正隆 2007 分子模拟的理论与实践 (北京: 化学工业出版社) 第9页]
[18] Allen M P, Tildesley D J 1987 Computer Simulation of Liquids (1st Ed.) (Oxford: Oxford University Press) p13
[19] Tabar H R 2008 Computational Physics of Carbon Nanotubes (1st Ed.) (Cambridge: Cambridge University Press) p113
[20] Landau D P, Binder K 2014 A Guide to Monte Carlo Simulations in Statistical Physics (2nd Ed.) (Cambridge: Cambridge University Press) p196
[21] Adams D J 1975 Mol. Phys. 29 307
[22] Chen P R, Xu Z C, Gu Y, Zhong W R 2016 Chin. Phys. B 25 086601
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