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非互易耦合布朗粒子的定向输运

付天琦 申伯洋 马欣然 黄仁忠 范黎明 艾保全 高天附 郑志刚

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非互易耦合布朗粒子的定向输运

付天琦, 申伯洋, 马欣然, 黄仁忠, 范黎明, 艾保全, 高天附, 郑志刚
cstr: 32037.14.aps.74.20250689

Directional transport of non-reciprocal coupled Brownian particles

FU Tianqi, SHEN Boyang, MA Xinran, HUANG Renzhong, FAN Liming, AI Baoquan, GAO Tianfu, ZHENG Zhigang
cstr: 32037.14.aps.74.20250689
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  • 研究了具有非互易相互作用的耦合布朗粒子的定向输运问题. 通过建立非互易耦合布朗棘轮模型, 研究了耦合自由长度、热噪声强度和非互易耦合强度系数比等参量对棘轮定向输运的影响. 结果发现, 通过调节耦合自由长度可以诱导粒子的流反转. 同时, 存在一个最优的耦合强度系数比, 能使非互易耦合布朗粒子的定向输运达到最强. 这一结果表明非互易相互作用确实能够促进耦合系统定向输运的产生. 此外, 通过调节热噪声强度、非对称系数和外势高度等参量还可以实现非互易耦合布朗粒子的定向输运控制. 未来的研究可进一步探索非互易相互作用在复杂环境下的动力学机制.
    In recent years, the physics of systems with non-reciprocal interactions has received increasing attention. Systems with non-reciprocal interactions are existent in soft matters, active matters, as well as biological and artificial nanoscale systems. The directional transport of coupled Brownian particles with nonreciprocal interactions is investigated by establishing a nonreciprocal coupled Brownian ratchet model. The effects of parameters such as the coupling free length, thermal noise intensity, and the ratio of nonreciprocal coupling strength coefficients on the directional transport of ratchets are systematically examined in this work.The research result reveals that the flow reversal of particles can be induced by adjusting the coupling free length. Meanwhile, there exists an optimal ratio of coupling strength coefficients that maximizes the directional transport of the nonreciprocally coupled Brownian particles. These findings demonstrate that the nonreciprocal interactions indeed enhance the directional transport of coupled system. Additionally, directional transport control can be achieved by modulating parameters such as thermal noise intensity, asymmetry coefficient, and external potential barrier height. Future research may further explore the dynamical mechanisms of nonreciprocal interactions in complex environments, especially the swarm behaviors in many-particle systems. Furthermore, by combining relevant experimental and theoretical studies, deeper insights can be gained into the regularity and universality of non-reciprocal interactions in both natural and artificial nanoscale systems.
      通信作者: 高天附, tianfugao@synu.edu.cn ; 郑志刚, zgzheng@hqu.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 12375031, 11875135) 和沈阳师范大学研究生教育教学改革研究一般项目(批准号: YJSJG320240062)资助的课题.
      Corresponding author: GAO Tianfu, tianfugao@synu.edu.cn ; ZHENG Zhigang, zgzheng@hqu.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 12375031, 11875135) and the Postgraduate Education Reform Project of Shenyang Normal University, China (Grant No. YJSJG320240062).
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    You Z, Baskaran A, Marchetti M C 2020 Proc. Natl. Acad. Sci. 117 19767Google Scholar

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    Scheibner C, Souslov A, Banerjee D, Surówka P, Irvine W T, Vitelli V 2020 Nat. Phys. 16 475Google Scholar

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    He Y F, Ai B Q, Dai C X, Song C, Wang R Q, Sun W T, Liu F C, Feng Y 2020 Phys. Rev. Lett. 124 075001Google Scholar

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    Xiong L Y, Cao Y S, Cooper R, Rappel W J, Hasty J, Tsimring L 2020 Elife 9 e48885Google Scholar

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    Theveneau E, Steventon B, Scarpa E, Garcia S, Trepat X, Streit A, Mayor R 2013 Nat. Cell Biol. 15 763Google Scholar

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    Saha S, Ramaswamy S, Golestanian R 2019 New. J. Phys. 21 063006Google Scholar

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    Soto R, Golestanian R 2014 Phys. Rev. Lett. 112 068301Google Scholar

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    Sompolinsky H, Kanter I 1986 Phys. Rev. Lett. 57 2861Google Scholar

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    Brunel N 2000 J. Comput. Neurosci. 8 183Google Scholar

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    Golomb D, Hansel D 2000 Neural Comput. 12 1095Google Scholar

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    Boergers C, Kopell N 2003 Neural Comput. 15 509Google Scholar

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    Loos S A, Klapp S H, Martynec T 2023 Phys. Rev. Lett. 19 130

    [15]

    Fruchart M, Littlewood P B, Hanai R 2020 Nature 592 7854

    [16]

    Poncet A, Bartolo D 2022 Phys. Rev. Let. 128 048002Google Scholar

    [17]

    Fruchart M, Hanai R, Littlewood P B, Vitelli V 2021 Nature 592 363Google Scholar

    [18]

    Lachance J, Suh K, Clausen J, Cohen D J 2022 Plos Comput. Biol. 18 e1009293Google Scholar

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    Dzubiella J, Lowen H 2003 Phys. Rev. Lett. 24 91

    [20]

    Lisin E A, Petrov O F, Sametov E A 2020 Sci. Rep. 10 13653Google Scholar

    [21]

    Sabass B, Seifert U 2010 Phys. Rev. Lett. 105 218103Google Scholar

    [22]

    Sriram I, Furst E M 2012 Soft Matter 8 3335Google Scholar

    [23]

    Felipe J, Harmon K J, Nguyen T D 2020 Phys. Rev. Res. 2 043244Google Scholar

    [24]

    Bhattacherjee, Hayakawa M, Shibata T 2024 Soft Matter 20 2739Google Scholar

    [25]

    Kreienkamp K L, Klapp S H 2022 New J. Phys. 24 123009Google Scholar

    [26]

    Li C P, Chen H B, Zheng Z G 2017 Front. Phys. 12 120507Google Scholar

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    Archana G R, Barik D Physica A 2024 Physica. A 650 129992Google Scholar

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    Ai B Q 2023 Phys. Rev. E 108 064409

  • 图 1  $ \left| {{x_1} - {x_2}} \right| \lt a $时, 非互易耦合布朗粒子的受力分析图示

    Fig. 1.  At $ \left| {{x_1} - {x_2}} \right| \lt a $, the force analysis diagram of non-reciprocally coupled Brownian particles.

    图 2  $ \left| {{x_1} - {x_2}} \right| \gt a $时, 非互易耦合布朗粒子的受力分析图示

    Fig. 2.  At $ \left| {{x_1} - {x_2}} \right| \gt a $, the force analysis diagram of non-reciprocally coupled Brownian particles.

    图 3  不同热噪声强度$ D $下, 耦合布朗粒子的平均速度$ \left\langle V \right\rangle $随耦合自由长度$ a $变化的曲线, 其中$ \varDelta = 1.0 $, $ {U_0} = $$ 0.5 $, $ \mu = 5.0 $, $ {k_2} = 3.0 $

    Fig. 3.  The curve of the average velocity $ \left\langle V \right\rangle $ of coupled Brownian particles as a function of the coupling free length $ a $ under different thermal noise intensities $ D $, where $ \varDelta = $$ 1.0 $, $ {U_0} = 0.5 $, $ \mu = 5.0 $, $ {k_2} = 3.0 $.

    图 4  不同非对称系数$ \varDelta $下, 耦合布朗粒子的平均速度$ \left\langle V \right\rangle $随热噪声强度$ D $变化的曲线, 其中$ a = 0.7 $, $ {U_0} = 0.5 $, $ \mu = 5.0 $, $ {k_2} = 3.0 $

    Fig. 4.  The curve of the average velocity $ \left\langle V \right\rangle $ of coupled Brownian particles with varying thermal noise intensity $ D $ under different asymmetric coefficients $ \varDelta $, where $ a = 0.7 $, $ {U_0} = 0.5 $, $ \mu = 5.0 $, $ {k_2} = 3.0 $.

    图 5  不同非对称周期势的势垒高度$ {U_0} $下, 耦合布朗粒子的平均速度$ \left\langle V \right\rangle $随非对称系数$ \varDelta $变化的曲线, 其中$ a = 0.7 $, $ D = 0.4 $, $ \mu = 5.0 $, $ {k_2} = 3.0 $

    Fig. 5.  The curve of the average velocity $ \left\langle V \right\rangle $ of coupled Brownian particles as a function of the asymmetry coefficient $ \varDelta $ under different barrier heights $ {U_0} $ of asymmetric periodic potentials, where $ a = 0.7 $, $ D = 0.4 $, $ \mu = 5.0 $, $ {k_2} = 3.0 $.

    图 6  不同非互易耦合强度系数比值$ \mu $下, 耦合布朗粒子的平均速度$ \left\langle V \right\rangle $随非对称周期势的高度$ {U_0} $变化的曲线, 其中$ a = 0.7 $, $ D = 0.4 $, $ \varDelta $ = 1.0, $ {k_2} = 3.0 $

    Fig. 6.  The curve of the average velocity $ \left\langle V \right\rangle $ of coupled Brownian particles varying with the height $ {U_0} $ of the asymmetric periodic potential under different non-reciprocal coupling strength coefficient ratios $ \mu $, where $ a = 0.7 $, $ D = 0.4 $, $ \varDelta $ = 1.0, $ {k_2} = 3.0 $.

    图 7  不同耦合自由长度$ a $下, 耦合布朗粒子的平均速度$ \left\langle V \right\rangle $随非互易耦合强度系数比$ \mu $变化的曲线, 其中$ D = 0.4 $, $ \varDelta $ = 1.0, $ {U_0} = 0.5 $, $ {k_2} = 3.0 $

    Fig. 7.  The curve of the average velocity $ \left\langle V \right\rangle $ of coupled Brownian particles as a function of the non-reciprocal coupling strength coefficient ratio $ \mu $ under different coupling free lengths $ a $, where $ D = 0.4 $, $ \varDelta $ = 1.0, $ {U_0} = 0.5 $, $ {k_2} = $$ 3.0 $.

  • [1]

    Bowick M J, Fakhri N, Marchetti M C, Ramaswamy S 2022 Phys. Rev. X 12 010501

    [2]

    You Z, Baskaran A, Marchetti M C 2020 Proc. Natl. Acad. Sci. 117 19767Google Scholar

    [3]

    Scheibner C, Souslov A, Banerjee D, Surówka P, Irvine W T, Vitelli V 2020 Nat. Phys. 16 475Google Scholar

    [4]

    Gupta R K, Kant R, Soni H, Sood A K, Ramaswamy S 2020 Phys. Rev. E 105 064602

    [5]

    He Y F, Ai B Q, Dai C X, Song C, Wang R Q, Sun W T, Liu F C, Feng Y 2020 Phys. Rev. Lett. 124 075001Google Scholar

    [6]

    Xiong L Y, Cao Y S, Cooper R, Rappel W J, Hasty J, Tsimring L 2020 Elife 9 e48885Google Scholar

    [7]

    Theveneau E, Steventon B, Scarpa E, Garcia S, Trepat X, Streit A, Mayor R 2013 Nat. Cell Biol. 15 763Google Scholar

    [8]

    Saha S, Ramaswamy S, Golestanian R 2019 New. J. Phys. 21 063006Google Scholar

    [9]

    Soto R, Golestanian R 2014 Phys. Rev. Lett. 112 068301Google Scholar

    [10]

    Sompolinsky H, Kanter I 1986 Phys. Rev. Lett. 57 2861Google Scholar

    [11]

    Brunel N 2000 J. Comput. Neurosci. 8 183Google Scholar

    [12]

    Golomb D, Hansel D 2000 Neural Comput. 12 1095Google Scholar

    [13]

    Boergers C, Kopell N 2003 Neural Comput. 15 509Google Scholar

    [14]

    Loos S A, Klapp S H, Martynec T 2023 Phys. Rev. Lett. 19 130

    [15]

    Fruchart M, Littlewood P B, Hanai R 2020 Nature 592 7854

    [16]

    Poncet A, Bartolo D 2022 Phys. Rev. Let. 128 048002Google Scholar

    [17]

    Fruchart M, Hanai R, Littlewood P B, Vitelli V 2021 Nature 592 363Google Scholar

    [18]

    Lachance J, Suh K, Clausen J, Cohen D J 2022 Plos Comput. Biol. 18 e1009293Google Scholar

    [19]

    Dzubiella J, Lowen H 2003 Phys. Rev. Lett. 24 91

    [20]

    Lisin E A, Petrov O F, Sametov E A 2020 Sci. Rep. 10 13653Google Scholar

    [21]

    Sabass B, Seifert U 2010 Phys. Rev. Lett. 105 218103Google Scholar

    [22]

    Sriram I, Furst E M 2012 Soft Matter 8 3335Google Scholar

    [23]

    Felipe J, Harmon K J, Nguyen T D 2020 Phys. Rev. Res. 2 043244Google Scholar

    [24]

    Bhattacherjee, Hayakawa M, Shibata T 2024 Soft Matter 20 2739Google Scholar

    [25]

    Kreienkamp K L, Klapp S H 2022 New J. Phys. 24 123009Google Scholar

    [26]

    Li C P, Chen H B, Zheng Z G 2017 Front. Phys. 12 120507Google Scholar

    [27]

    Archana G R, Barik D Physica A 2024 Physica. A 650 129992Google Scholar

    [28]

    Ai B Q 2023 Phys. Rev. E 108 064409

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出版历程
  • 收稿日期:  2025-05-27
  • 修回日期:  2025-07-02
  • 上网日期:  2025-07-03
  • 刊出日期:  2025-09-05

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