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Velocity is an important factor affecting the friction coefficient of polymers. Polytetrafluoroethylene (PTFE), as a typical self-lubricating polymer, has attracted extensive attention because of its low friction coefficient. Currently, the friction coefficient of PTFE is investigated usually by using experimental method. The experimental study which is limited by the functionality and precision of the apparatus is inaccessible to the exploration of the microscopic tribological mechanism of PTFE. Therefore, the coarse-grained molecular dynamics simulation method is adopted in this study. In the coarse-grained model, one PTFE molecule is simplified into ten beads, including two end beads and eight backbone beads. The non-bonding and bonding interactions between beads are described by using Lennard-Jones (L-J) and multi-centered Gaussian-based potential. In order to investigate the effect of velocity on the friction coefficient of PTFE at an atomic level, we build a two-layer PTFE friction model by using the coarse-grained molecular dynamics simulation method. To directly compare the experimental results with the simulation results, we set the value of the externally applied load and the range of the velocities that match each other as closely as possible. The mechanism of how the velocity affects PTFE friction coefficient is obtained at an atomic level through analyzing the bond length distribution, bond angle distribution, the deformation of the bottom PTFE molecules within the contact area, and the friction force and normal force as a function of simulation time. The simulation results show that the bond length and bond angle decrease, the deformation of the bottom PTFE molecules along the x-direction and the friction force increase with velocity increasing. This is because the bounce back caused by the deformed PTFE molecules enhances the friction force. The severer the deformation, the larger the friction force will be. However, when the velocity exceeds a critical velocity, the bond length and bond angle increase, the deformation of the bottom PTFE molecule and the friction force decrease with velocity increasing. This is most likely due to the fact that the bottom PTFE molecules within the contact area tend to tilt along the moving direction of the upper PTFE layer, thereby reducing the angle between the upper and the bottom PTFE molecules to an angle close to the angle of parallel sliding, finally resulting in the decrease of the friction force. The deformations of PTFE molecules along the z-direction are nearly invariable under different velocities. This corresponds to the variation of the normal force. Therefore, for a constant externally applied load, the friction coefficient first increases then decreases with velocity increasing. In addition, the critical velocity is 1.2 m/s, which is in line with the published experimental result. -
Keywords:
- polytetrafluoroethylene /
- friction /
- velocity /
- molecular dynamics simulation
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图 1 (a) 化学式; (b) 全原子模型; (c) 映射原理; (d) 粗粒化模型
Figure 1. (a) Chemical structure; (b) all-atom model; (c) mapping scheme; (d) coarse-grained model.
图 2 粒子间相互作用势
Figure 2. Schematic of interaction among beads.
图 3 PTFE-PTFE摩擦的粗粒化模型 (a)初始分布; (b)随机分布; (c)平衡态分布; (d)最终分布
Figure 3. The coarse-grained PTFE-PTFE friction model: (a) Initial distribution; (b) random distribution; (c) equilibrium distribution; (d) final distribution.
图 4 (a) 摩擦力; (b)正压力; (c)摩擦系数随模拟时间的变化
Figure 4. (a) Friction force; (b) normal force; (c) friction coefficient as a function of simulation time.
图 5 当外载荷为31 MPa时, 摩擦系数随速度的变化
Figure 5. Effect of velocity on friction coefficient when the externally applied load is 31 MPa.
图 6 不同速度下接触区内下层PTFE的键长 (a)和键角分布 (b)
Figure 6. (a) Bond length and (b) bond angle distributions of the bottom PTFE molecules within the contact area under different velocities.
图 7 接触区内下层PTFE分子沿(a) x方向及(b) z方向的回转半径分布
Figure 7. Distributions of radius of gyration along (a) x and (b) z directions of the bottom PTFE molecules within the contact area.
图 8 (a) 摩擦力和(b) 正压力随模拟时间的变化
Figure 8. (a) Friction force and (b) normal force as a function of simulation time.
Bond type nb i Abi wbi/Å lbci/Å B-B/B-E 2 1 0.3 0.15 2.7 2 0.03 0.2 2.5 
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Angle type na i Aai wai/(°) θaci/(°) B-B-B/B-B-E 3 1 1.44 30 180 2 1.66 12 180 3 0.12 29 145
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[1] Tian K, Goldsby D L, Carpick R W 2018 Phys. Rev. Lett. 120 186101
Google Scholar
[2] Dong Y, Duan Z, Tao Y, Wei Z, Gueye B, Zhang Y, Chen Y 2019 Tribol. Int. 136 259
Google Scholar
[3] 董赟, 段早琦, 陶毅, Gueye Birahima, 张艳, 陈云飞 2019 68 016801
Google Scholar
Dong Y, Duan Z Q, Tao Y, Gueye B, Zhang Y, Chen Y F 2019 Acta Phys. Sin. 68 016801
Google Scholar
[4] Li Q, Dong Y, Perez D, Martini A, Carpick R W 2011 Phys. Rev. Lett. 106 126101
Google Scholar
[5] Sharma N, Kumar N, Dash S, Tyagi A K 2012 AIP Conf. Proc. 1447 651
[6] Sun F, Hou Y, Wang L, Huang L, Qian Z 2017 Int. J. Pave. Res. Tech. 10 343
Google Scholar
[7] Lin L, Pei X Q, Bennewitz R, Schlarb A K 2018 Tribol. Int. 122 108
Google Scholar
[8] Xiong X 2018 Ind. Lubr. Tribol. 70 273
Google Scholar
[9] Barry P R, Jang I, Perry S S, Sawyer W G, Sinnott S B, Phillpot S R 2007 J. Computer-Aided Mater. Des. 14 239
Google Scholar
[10] Yuan X D, Yang X J 2010 Wear 269 291
Google Scholar
[11] Harris K L, Pitenis A A, Sawyer W G, Krick B A, Blackman G S, Kasprzak D J, Junk C P 2015 Macromolecules 48 3739
Google Scholar
[12] 杨学宾, 晋欣桥, 杜志敏, 崔天生, 杨绍侃 2010 内燃机工程 31 105
Google Scholar
Yang X B, Jin X Q, Du Z M, Cui T S, Yang S K 2010 Chin. Int. Combu. Engine. Eng. 31 105
Google Scholar
[13] 郭丰镐 1981 机械工程材料 4 5
Guo F G 1981 Mater. Mech. Eng. 4 5
[14] 黄传辉 2008 徐州工程学院学报(自然科学版) 23 7
Google Scholar
Huang C H 2008 Xuzhou Inst. Technol. (Natural Sciences Edition)
23 7 Google Scholar
[15] 马姗, 马军, 杨光参 2016 65 148701
Google Scholar
Ma S, Ma J, Yang G C 2016 Acta Phys. Sin. 65 148701
Google Scholar
[16] Ewen J P, Heyes D M, Dini D 2018 Friction 6 349
Google Scholar
[17] Dong Y, Li Q, Martini A 2013 J. Vac. Sci. Technol. A 31 030801
[18] Dong Y, Wang F, Zhu Z, He T 2019 AIP Adv. 9 045213
Google Scholar
[19] Barry P R, Chiu P Y, Perry S S, Sawyer W G, Sinnott S B, Phillpot S R 2015 Tribol. Lett. 58 50
Google Scholar
[20] Barry P R, Chiu P Y, Perry S S, Sawyer W G, Phillpot S R, Sinnott S B 2009 J. Phys.: Condens. Matter 21 144201
Google Scholar
[21] Chiu P Y, Barry P R, Perry S S, Sawyer W G, Phillpot S R, Sinnott S B 2011 Tribol. Lett. 42 193
Google Scholar
[22] 王曦, 黎明, 叶方富, 周昕 2017 66 150201
Google Scholar
Wang X, Li M, Ye F F, Zhou X 2017 Acta Phys. Sin. 66 150201
Google Scholar
[23] Hagita K, Morita H, Doi M, Takano H 2016 Macromolecules 49 1972
Google Scholar
[24] Thota N, Luo Z, Hu Z, Jiang J 2013 J. Phys. Chem. B 117 9690
Google Scholar
[25] Zuo Z, Yang Y, Qi X, Su W, Yang X 2014 Wear 320 87
Google Scholar
[26] Milano G, Mü1ller P F 2005 J. Phys. Chem. B 109 18609
Google Scholar
[27] Milano G, Goudeau S, Mü1ller P F 2005 J. Polym. Sci. Pol. Phys. 43 871
Google Scholar
[28] Pan D, Liu C, Qi X, Yang Y, Hao X 2019 Tribol. Int. 133 32
Google Scholar
[29] Onodera T, Nunoshige J, Kawasaki K, Adachi K, Kurihara K, Kubo M 2017 J. Phys. Chem. C 121 14589
Google Scholar
[30] Plimpton S 1995 J. Comp. Physiol. 117 1
Google Scholar
[31] Jang I, Burris D L, Dickrell P L, Barry P R, Santos C, Perry S S, Phillpot S R, Sinnott S B, Sawyer W G 2007 J. Appl. Phys. 102 123509
Google Scholar
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