| ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS |
Prev
Next
|
|
|
Effect of distribution shape on the melting transition, local ordering, and dynamics in a model size-polydisperse two-dimensional fluid |
| Jackson Pame and Lenin S. Shagolsem† |
| Department of Physics, National Institute of Technology Manipur, Imphal, India |
|
|
|
|
Abstract We study the effect of particle size polydispersity ($\delta$) on the melting transition ($T^*$), local ordering, solid-liquid coexistence phase and dynamics of two-dimensional Lennard-Jones fluids up to moderate polydispersity by means of computer simulations. The particle sizes are drawn at random from the Gaussian (G) and uniform (U) distribution functions. For these systems, we further consider two different kinds of particles, viz., particles having the same mass irrespective of size, and in the other case the mass of the particle scales with its size. It is observed that with increasing polydispersity, the value of $T^\ast$ initially increases due to improved packing efficiency ($\phi$) followed by a decrease and terminates at $\delta\approx 8%$ (U-system) and $14%$ (G-system) with no significant difference for both mass types. The interesting observation is that the particular value at which $\phi$ drops suddenly coincides with the peak of the heat capacity $(C_{P})$ curve, indicating a transition. The quantification of local particle ordering through the hexatic order parameter ($Q_6$), Voronoi construction and pair correlation function reveals that the ordering decreases with increasing $\delta$ and $T$. Furthermore, the solid-liquid coexistence region for the G-system is shown to be comparatively wider in the $T$-$\delta$ plane phase diagram than that for the U system. Finally, the study of dynamics reveals that polydisperse systems relax faster compared to monodisperse systems; however, no significant qualitative differences, depending on the distribution type and mass polydispersity, are observed.
|
Received: 25 January 2024
Revised: 24 March 2024
Accepted manuscript online: 03 April 2024
|
|
PACS:
|
47.57.-s
|
(Complex fluids and colloidal systems)
|
| |
87.10.Tf
|
(Molecular dynamics simulation)
|
| |
83.10.Rs
|
(Computer simulation of molecular and particle dynamics)
|
| |
87.15.Zg
|
(Phase transitions)
|
|
| Fund: JP acknowledges NFST fellowship (Award No. 201920- NFST-NAG-02957). |
Corresponding Authors:
Lenin S. Shagolsem
E-mail: slenin2001@gmail.com
|
Cite this article:
Jackson Pame and Lenin S. Shagolsem Effect of distribution shape on the melting transition, local ordering, and dynamics in a model size-polydisperse two-dimensional fluid 2024 Chin. Phys. B 33 074702
|
[1] Adachi Y, Fijihara I, Takamiya M and Nakanishi K 1988 Fluid Phase Equilibria 39 1
[2] Galliéro G, Boned C and Baylaucq A 2005 Ind. Amp. Eng. Chem. Res. 44 6963
[3] Galliéro G, Boned C and Fern ández J 2011 J. Chem. Phys. 134 064505
[4] Bohn M, Lustig R and Fischer J 1986 Fluid Ph. Equilibr. 25 251
[5] Barker J A, Henderson D and Abraham F F 1981 Physica A 106 226
[6] Nasrabad A E, Laghaei R and Deiters U K 2004 J. Chem. Phys. 121 6423
[7] Fasolo M and Sollich P 2003 Phys. Rev. Lett. 91 068301
[8] Pagonabarraga I, Cates M E and Ackland G J 2000 Phys. Rev. Lett. 84 911
[9] Kosterlitz J M and Thouless D J 1973 J. Phys. C: Solid State Phys. 6 1181
[10] Kosterlitz J M and Thouless D J 1972 J. Phys. C: Solid State Phys. 5 L124
[11] Nelson D R and Halperin B I 1979 Phys. Rev. B 19 2457
[12] Halperin B I and Nelson D R 1978 Phys. Rev. Lett. 41 121
[13] Frenkel D and McTague J P 1979 Phys. Rev. Lett. 42 1632
[14] Wierschem K and Manousakis E 2011 Phys. Rev. B 83 214108
[15] Evans R M L 1999 Phys. Rev. E 59 3192
[16] Ingebrigtsen T S and Tanaka H 2016 J. Phys. Chem. B 120 7704
[17] Ingebrigtsen T S and Tanaka H 2015 J. Phys. Chem. B 119 11052
[18] Wilding N B, Sollich P, Fasolo M and Buzzacchi M 2006 J. Chem. Phys. 125 014908
[19] Shagolsem L S, Osmanovi′c D, Peleg O and Rabin Y 2015 J. Chem. Phys. 142 051104
[20] Shagolsem L S and Rabin Y 2016 J. Chem. Phys. 144 194504
[21] Eastwood A R 1981 J. Chem. Soc., Faraday Trans. 77 1411
[22] Murarka R K and Bagchi B 2003 Phys. Rev. E 67 051504
[23] Gotze W and Sjogren L 1992 Rep. Prog. Phys. 55 241
[24] Bengtzelius U, Gotze W and Sjolander A 1984 J. Phys. C: Solid State Phys. 17 5915
[25] Ingebrigtsen T S and Dyre J C 2023 J. Phys. Chem. B 127 2837
[26] Patashinski A Z, Ratner M A, Grzybowski B A, Orlik R and Mitus A C 2012 J. Phys. Chem. Lett. 3 2431
[27] Patashinski A Z, Orlik R, Mitus A C, Grzybowski B A and Ratner M A 2010 J. Phys. Chem. C 114 20749
[28] Salacuse J J and Stell G 1982 J. Chem. Phys. 77 3714
[29] Sollich P and Cates M E 1998 Phys. Rev. Lett. 80 1365
[30] Warren P B 1998 Phys. Rev. Lett. 80 1369
[31] Reichhardt C O and Reichhardt C 2003 J. Phys. A: Math. Gen. 36 5841
[32] Reichhardt C and Reichhardt C O 2005 Phys. Rev. E 71 062403
[33] Chen J X, Yuan R, Cui R and Qiao L 2021 Nanoscale 13 1055
[34] Ruiz P S, Lei Q L and Ni R 2019 Commun. Phys. 2 70
[35] Pal, Sayan, et al. 2023 Chemical Engineering Journal 462 142007
[36] Horta-Pineres, S, et al. 2020 Appl. Phys. A 126 1
[37] Phongtongpasuk, Siriporn, Sarinya Poadang and Niti Yongvanich 2016 Energy Procedia 89 239
[38] Singh, Priyanka, et al. 2016 Artificial cells, nanomedicine and biotechnology 44 1569
[39] Pigolotti S, Lopez C, Hernández-García E and Andersen K H 2010 Theoretical Ecology 3 89
[40] Lai C D, Murthy D N and Xie M 2006 Springer Handbooks pp. 63-78 (Springer)
[41] Matsumura Y and Jackson T L 2014 Phys. Fluids 26 123302
[42] Wilding N B and Sollich P 2010 J. Chem. Phys. 133 224102
[43] Sarkar S, Biswas R, Santra M and Bagchi B 2013 Phys. Rev. E 88 022104
[44] Pame J and Shagolsem L S 2021 Macromol. Symp. 399 2100037
[45] Allen M P and Tildesley D J 2017 Computer simulation of liquids, Oxford University Press
[46] Frenkel D and Smit B 2001 Understanding Molecular Simulation: From Algorithms to Applications (Cambridge: Academic Press)
[47] Plimpton S 1995 J. Comput. Phys. 117 1
[48] Urikhinbam S S and Shagolsem L S 2023 J. Phys. Chem. B 127 2739
[49] Pusey P N 1987 J. Phys. 48 709
[50] Barrat J L and Hansen J P 1986 J. Phys. 47 1547
[51] McRae R and Haymet A D J 1988 J. Chem. Phys. 88 1114
[52] Dickinson E, Parker R and Lal M 1981 Chem. Phys. Lett 79 578
[53] Dickinson E and Parker R 1985 J. Physique, Lett. 46 229
[54] Bolhuis P G and Kofke D A 1996 Phys. Rev. E 54 634
[55] Dullens R P and Kegel W K 2004 Phys. Rev. Lett. 92 195702
[56] Kofke D A and Bolhuis P G 1999 Phys. Rev. E 59 618
[57] Abraham S E, Bhattacharrya S M and Bagchi B 2008 Phys. Rev. Lett. 100 167801
[58] Volkel S and Huang K 2020 In Traffic and Granular Flow, 2019 pp. 429-437, Springer International Publishing
[59] Meitei T P and Shagolsem L S 2024 Pramana 98 1 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
