|
|
|
Disorder-to-order transition induced by spontaneous cooling regulation in robotic active matter |
| Shuaixu Hou(侯帅旭)1,†, Gao Wang(王高)2,3,†, Xingyu Ma(马星宇)4, Chuyun Wang(汪楚云)5, Peng Wang(王鹏)5, Huaicheng Chen(陈怀城)2, Liyu Liu(刘雳宇)1,‡, and Jing Wang(王璟)2,§ |
1 Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China;
2 Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China;
3 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
4 School of Ophthalmology and Optometry, Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325035, China;
5 Postgraduate Training Base Alliance, Wenzhou Medical University, Wenzhou 325035, China |
|
|
|
|
Abstract In classical matter systems, typical phase-transition phenomena usually stem from changes in state variables, such as temperature and pressure, induced by external regulations such as heat transfer and volume adjustment. However, in active matter systems, the self-propulsion nature of active particles endows the systems with the ability to induce unique collective-state transitions by spontaneously regulating individual properties to alter the overall states. Based on an innovative robot-swarm experimental system, we demonstrate a field-driven active matter model capable of modulating individual motion behaviors through interaction with a recoverable environmental resource field by the resource perception and consumption. In the simulated model, by gradually reducing the individual resource-conversion coefficient over time, this robotic active matter can spontaneously decrease the overall level of motion, thereby actively achieving a regulation behavior like the cooling-down control. Through simulation calculations, we discover that the spatial structures of this robotic active matter convert from disorder to order during this process, with the resulting ordered structures exhibiting a high self-adaptability on the geometry of the environmental boundaries.
|
Received: 01 March 2024
Revised: 09 April 2024
Accepted manuscript online: 25 April 2024
|
|
PACS:
|
87.15.Zg
|
(Phase transitions)
|
| |
89.75.Fb
|
(Structures and organization in complex systems)
|
| |
05.65.+b
|
(Self-organized systems)
|
| |
87.85.St
|
(Robotics)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 12174041), China Postdoctoral Science Foundation (Grant No. 2022M723118), and the Seed Grants from the Wenzhou Institute, University of Chinese Academy of Sciences (Grant No. WIUCASQD2021002). |
Corresponding Authors:
Liyu Liu, Jing Wang
E-mail: lyliu@cqu.edu.cn;wangjing@ucas.ac.cn
|
Cite this article:
Shuaixu Hou(侯帅旭), Gao Wang(王高), Xingyu Ma(马星宇), Chuyun Wang(汪楚云), Peng Wang(王鹏), Huaicheng Chen(陈怀城), Liyu Liu(刘雳宇), and Jing Wang(王璟) Disorder-to-order transition induced by spontaneous cooling regulation in robotic active matter 2024 Chin. Phys. B 33 078701
|
[1] Zhang H P, Shi X Q and Yang M C 2022 Physics 51 217 (in Chinese)
[2] Ramaswamy S 2017 J. Stat. Mech. 2017 054002
[3] Needleman D and Dogic Z 2017 Nat. Rev. Mater. 2 17048
[4] Cavagna A and Giardina I 2014 Annu. Rev. Condens. Matter Phys. 5 183
[5] Deng J and Liu D 2021 Bioinspir. Biomim. 16 046013
[6] Chen H, Zhang H, Xu T and Yu J 2021 ACS Nano 15 15625
[7] Fu Y, Yu H, Zhang X, Malgaretti P, Kishore V and Wang W 2022 Micromachines 13 295
[8] Servant A, Qiu F, Mazza M, Kostarelos K and Nelson B J 2015 Adv. Mater. 27 2981
[9] Jiang C, Yang M, Li W, Dou S X, Wang P Y and Li H 2022 iScience 25 104210
[10] Chen J X, Hu J Q and Kapral R 2024 Adv. Sci. 11 2305695
[11] Wu C, Dai J, Li X, Gao L, Wang J, Liu J, Zheng J, Zhan X, Chen J, Cheng X, Yang M and Tang J 2021 Nat. Nanotechnol. 16 288
[12] Hu J, Zhou S, Sun Y, Fang X and Wu L 2012 Chem. Soc. Rev. 41 4356
[13] Shields C W and Velev O D 2017 Chem. 3 539
[14] Fruchart M, Hanai R, Littlewood P B and Vitelli V 2021 Nature 592 363
[15] Liao G J and Klapp S H L 2021 Soft Matter 17 6833
[16] Kaspar C, Ravoo B J, van der Wiel W G, Wegner S V and Pernice W H P 2021 Nature 594 345
[17] Roy S, Shirazi M J, Jantzen B and Abaid N 2019 Phys. Rev. E 100 062415
[18] You F, Yang H X, Li Y, Du W and Wang G 2023 Appl. Comput. Math. 438 127565
[19] Chen L M 2016 Acta Phys. Sin. 65 186401 (in Chinese)
[20] Marchetti M C, Joanny J F, Ramaswamy S, Liverpool T B, Prost J, Rao M and Simha R A 2013 Rev. Mod. Phys. 85 1143
[21] Bowick M J, Fakhri N, Marchetti M C and Ramaswamy S 2022 Phys. Rev. X 12 010501
[22] Vicsek T and Zafeiris A 2012 Phys. Rep. 517 71
[23] Vicsek T, Czirók A, Ben-Jacob E, Cohen I and Shochet O 1995 Phys. Rev. Lett. 75 1226
[24] Liu Z T, Shi Y, Zhao Y, Chaté H, Shi X Q and Zhang T H 2021 Proc. Natl. Acad. Sci. USA 118 e2104724118
[25] Schockmel J, Mersch E, Vandewalle N and Lumay G 2013 Phys. Rev. E 87 062201
[26] Deblais A, Barois T, Guerin T, Delville P H, Vaudaine R, Lintuvuori J S, Boudet J F, Baret J C and Kellay H 2018 Phys. Rev. Lett. 120 188002
[27] Tian W D, Gu Y, Guo Y K and Chen K 2017 Chin. Phys. B 26 100502
[28] Wu K T, Hishamunda J B, Chen D T N, Decamp S J, Chang Y W, Fernández-Nieves A, Fraden S and Dogic Z 2017 Science 355 eaal1979
[29] Yigit B, Alapan Y and Sitti M 2019 Adv. Sci. 6 1801837
[30] Dorigo M, Theraulaz G and Trianni V 2021 Proc. IEEE 109 1152
[31] Wu R, Zhu Y, Cai X, Wu S, Xu L and Yu T 2022 Micromachines 13 1473
[32] Li S, Batra R, Brown D, Chang H D, Ranganathan N, Hoberman C, Rus D and Lipson H 2019 Nature 567 361
[33] Scholz C, Engel M and Pöschel T 2018 Nat. Commun. 9 931
[34] Wang G, Phan T V, Li S, Wang J, Peng Y, Chen G, Qu J, Goldman D I, Levin S A, Pienta K, Amend S, Austin R H and Liu L 2022 Proc. Natl. Acad. Sci. USA 119 e2120019119
[35] Jin Y, Wang G, Yuan D, Wang P, Wang J, Chen H, Liu L and Zan X 2023 Chin. Phys. B 32 088703
[36] Liu P, Zhu H, Zeng Y, Du G, Ning L, Wang D, Chen K, Lu Y, Zheng N, Ye F and Yang M 2020 Proc. Natl. Acad. Sci. USA 117 11901
[37] Wang G, Phan T V, Li S, Wombacher M, Qu J, Peng Y, Chen G, Goldman D I, Levin S A, Austin R H and Liu L 2021 Phys. Rev. Lett. 126 108002
[38] Liu Y, Wang G, Wang P, Yuan D, Hou S, Jin Y, Wang J and Liu L 2023 Chin. Phys. B 32 068701
[39] Debnath D, Gainer J S, Kilic C, Kim D, Matchev K T and Yang Y P 2016 Eur. Phys. J. C 76 645
[40] Steinhardt P J, Nelson D R and Ronchetti M 1983 Phys. Rev. B 28 784
[41] Eslami H, Sedaghat P and Müller-Plathe F 2018 Phys. Chem. Chem. Phys. 20 27059
[42] Čapek D and Müller P 2019 Development 146 dev177709
[43] Wesley C C, Mishra S and Levy D L 2020 WIREs Dev. Biol. 9 e376
[44] Gregor T, Bialek W, van Steveninck R R D R, Tank D W and Wieschaus E F 2005 Proc. Natl. Acad. Sci. USA 102 18403 |
| 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
|
|
|
