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Biomolecular motors are macromolecules of enzyme proteins that convert chemical energy into mechanical energy. Experimental studies have shown that the directed movement of the biomolecular motor fully participates in the material transport process in the cell. Theoretically, the directed movement of biomolecular motors can be studied by the ratchet model. However, in most of feedback control ratchet models, none of the influences of external factors on experimental manipulation is considered, especially the inevitable random error, systematic error and human error in the experiment. Therefore, in order to further study the influences of error factors on feedback control ratchets, Cao's research group (Feito M, Cao F J 2007 Eur. Phys. J. B 59 63 ) pioneered the idea of error probability and discussed the transport behavior of feedback ratchets in the presence of error probability.Based on Cao's error ratchet model, in this paper the temperature factor in introduced to further control the feedback ratchets, and the directed transport characteristics of the coupled Brownian particles in the temperature feedback ratchets are studied. The effects of temperature factor, phase difference and temperature frequency on the directed transport of coupled Brownian particles are discussed in detail. It is found that the temperature factor does not always reduce the directed transport of Brownian particles. There is a minimum value which means that the temperature factor can enhance the directed transport of the feedback ratchets within a certain change interval. In addition, in a small temperature amplitude range, the directed transport of the coupled particles exhibits a multi-peak structure with the change of temperature frequency. It is means that the appropriate temperature change frequency can enhance the directed transport of the feedback ratchets multiple times. The conclusions obtained in this paper can not only inspire experimental selection of appropriate temperature feedback information to optimize the directed transport of the Brownian ratchets, but also provide theoretical references for analyzing and processing the experimental data, especially error analysis. -
Keywords:
- temperature feedback ratchets /
- error probability /
- temperature factor /
- directed transport
[1] 舒咬根, 欧阳钟灿 2007 物理 36 735
Google Scholar
Shu Y G, Ouyang Z C 2007 Physics 36 735
Google Scholar
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[3] Oster G, Wang H 2003 Trends Cell Biol 13 114
Google Scholar
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National Natural Science Foundation of China, Chinese Academy of Sciences 2020 Chinese Subject Development Strategy· Soft Condensed Matter Physics (Part 2) (Beijing: Science Press) p1037 (in Chinese)
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Google Scholar
[7] van den Heuvel M G L, Dekker C 2007 Science 317 333
Google Scholar
[8] Zhang H W, Wen S T, Zhang H T, Li Y X, Chen G R 2012 Chin. Phys. B 21 078701
Google Scholar
[9] Doering C R 1995 Nuovo Cimento 17 685
Google Scholar
[10] Astumian R D, Bier M 1994 Phys. Rev. Lett. 72 1766
Google Scholar
[11] Gao T F, Chen J C 2009 J. Phys. A: Math. Theor. 42 065002
Google Scholar
[12] Reimann P 2002 Phys. Rep. 361 57
Google Scholar
[13] Rosalie L W, Fabrice M K P 2016 Physica A 460 326
Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
Fan L M, Lv M T, Gao T F, Huang R Z, Zheng Z G 2017 Acta. Phys. Sin. 66 010501
Google Scholar
[17] Feito M, Cao F J 2007 Phys. Rev. E 76 061113
Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
Wang L F, Gao T F, Huang R Z, Zheng Y X 2013 Acta. Phys. Sin. 62 070502
Google Scholar
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Google Scholar
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图 1 (a) 质心平均速度
$\left\langle {{V_{{\rm{cm}}}}} \right\rangle $ , (b) 质心扩散系数$ {D_{{\text{eff}}}} $ 和 (c)$ Pe $ 数随温度相位差$ \theta $ 的变化曲线, 其中$ \omega = 0.1{\text{π}} $ ,$A = $ $ 1.0$ ,$ {T_0} = 0.7 $ Figure 1. Curves of (a) the center-of-mass mean velocity
$\left\langle {{V_{{\rm{cm}}}}} \right\rangle $ , (b) the center-of-mass diffusion coefficient$ {D_{{\text{eff}}}} $ and (c)$ Pe $ number varying with the phase different of temperature$ \theta $ , where$ \omega = 0.1{\text{π}} $ ,$ A = 1.0 $ ,$ {T_0} = 0.7 $ .
图 2 (a) 质心平均速度
$\left\langle {{V_{{\rm{cm}}}}} \right\rangle $ , (b) 质心扩散系数$ {D_{{\text{eff}}}} $ 和 (c)$ Pe $ 数随临界温度$ {T_{\text{C}}} $ 的变化曲线, 其中$ \omega = 0.1{\text{π}} $ ,$A = $ $ 1.0$ ,$ \theta = 0.2{\text{π}} $ Figure 2. Curves of (a) the center-of-mass mean velocity
$\left\langle {{V_{{\rm{cm}}}}} \right\rangle $ , (b) the center-of-mass diffusion coefficient$ {D_{{\text{eff}}}} $ and (c)$ Pe $ number varying with the critical temperature${T_{\rm{C}}}$ , where$ \omega = 0.1{\text{π}} $ ,$ A = 1.0 $ ,$ \theta = 0.2{\text{π}} $ .
图 3 (a) 质心平均速度
$\left\langle {{V_{{\rm{cm}}}}} \right\rangle $ , (b) 质心扩散系数$ {D_{{\text{eff}}}} $ 和 (c)$ Pe $ 数随温度频率$ \omega $ 的变化曲线, 其中$ {T_0} = 0.7 $ ,$\theta = $ $ 0.2{\text{π}}$ ,$ {\alpha _i} = 0.8 $ Figure 3. Curve of (a) the center-of-mass mean velocity
$\left\langle {{V_{{\rm{cm}}}}} \right\rangle $ , (b) the center-of-mass diffusion coefficient$ {D_{{\text{eff}}}} $ and (c)$ Pe $ number varying with the temperature frequency$ \omega $ , where$ {T_0} = 0.7 $ ,$ \theta = 0.2{\text{π}} $ ,$ {\alpha _i} = 0.8 $ . -
[1] 舒咬根, 欧阳钟灿 2007 物理 36 735
Google Scholar
Shu Y G, Ouyang Z C 2007 Physics 36 735
Google Scholar
[2] Xie P 2010 Int. J. Biol. Sci 6 665
[3] Oster G, Wang H 2003 Trends Cell Biol 13 114
Google Scholar
[4] 国家自然科学基金委员会, 中国科学院 2020 中国学科发展战略·软凝聚态物理学(下) (北京: 科学出版社) 第1037页
National Natural Science Foundation of China, Chinese Academy of Sciences 2020 Chinese Subject Development Strategy· Soft Condensed Matter Physics (Part 2) (Beijing: Science Press) p1037 (in Chinese)
[5] Palmigiano A, Santaniello F, Cerutti A, Penkov D, Purushothama D 2018 Sci. Rep. 8 3198
Google Scholar
[6] Linke H 2002 Appl. Phys. A 75 167
Google Scholar
[7] van den Heuvel M G L, Dekker C 2007 Science 317 333
Google Scholar
[8] Zhang H W, Wen S T, Zhang H T, Li Y X, Chen G R 2012 Chin. Phys. B 21 078701
Google Scholar
[9] Doering C R 1995 Nuovo Cimento 17 685
Google Scholar
[10] Astumian R D, Bier M 1994 Phys. Rev. Lett. 72 1766
Google Scholar
[11] Gao T F, Chen J C 2009 J. Phys. A: Math. Theor. 42 065002
Google Scholar
[12] Reimann P 2002 Phys. Rep. 361 57
Google Scholar
[13] Rosalie L W, Fabrice M K P 2016 Physica A 460 326
Google Scholar
[14] Pawel R, Felix M 2010 Phys. Rev. E 81 061120
Google Scholar
[15] Feito M, Cao F J 2006 Phys. Rev. E 74 041109
Google Scholar
[16] 范黎明, 吕明涛, 黄仁忠, 高天附, 郑志刚 2017 66 010501
Google Scholar
Fan L M, Lv M T, Gao T F, Huang R Z, Zheng Z G 2017 Acta. Phys. Sin. 66 010501
Google Scholar
[17] Feito M, Cao F J 2007 Phys. Rev. E 76 061113
Google Scholar
[18] Feito M, Cao F J 2008 Physica A 387 4553
Google Scholar
[19] Wang H Y, Bao J D 2007 Physica A 374 33
Google Scholar
[20] Feito M, Baltanas J P, Cao F J 2009 Phys. Rev. E 80 031128
Google Scholar
[21] Rousselet J, Salome L, Ajdari A, Prost J 1994 Nature 370 446
Google Scholar
[22] Feito M, Cao F J 2007 Eur. Phys. J. B 59 63
Google Scholar
[23] Dan D, Jayannavarar A M, Menon G I 2003 Physica A 318 40
Google Scholar
[24] 王莉芳, 高天附, 黄仁忠, 郑玉祥 2013 62 070502
Google Scholar
Wang L F, Gao T F, Huang R Z, Zheng Y X 2013 Acta. Phys. Sin. 62 070502
Google Scholar
[25] Li C P, Chen H B, Zheng Z G 2017 Front. Phys. 12 120507
Google Scholar
[26] Cao F J, Feito M, Touchette H 2007 Physica A 388 113
[27] Zheng Z G, Cross M C, Hu G 2002 Phys. Rev. Lett. 89 154102
Google Scholar
[28] Mateos J L 2004 Fluctuation Noise Lett 4 161
Google Scholar
[29] Lindner B, Schimanasky-Geier L 2002 Phys. Rev. Lett. 89 230602
Google Scholar
[30] Wang H Y, Bao J D 2005 Physica A 357 373
Google Scholar
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