1932

Abstract

A colloidal system is a large collection of micrometer-sized particles suspended in a liquid, and the state of the system can be measured in real time, using imaging techniques and image processing. The assembly of the particles is driven by interactions between the particles and the surrounding liquid, as well as by external fields, including electromagnetic, flow, and gravitational fields. The dynamics of the many-body system are high-dimensional, nonlinear, and stochastic. However, low-order models are derived in some cases, often using physics-based order parameters, to facilitate studying the system dynamics. With an understanding of the system dynamics, and by manipulating the aforementioned interactions, one can control the assembly process in real time using open-loop and closed-loop feedback control. Theoretical studies and experimental demonstrations of colloidal self-assembly control have been reported, with methods ranging from heuristic rules to model-based optimal feedback control.

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2022-05-03
2024-06-23
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Literature Cited

  1. 1. 
    Klavins E, Burden S, Napp N 2007. Optimal rules for programmed stochastic self-assembly. Robotics: Science and Systems II GS Sukhatme, S Schaal, W Burgard, D Fox 9–16 Cambridge, MA: MIT Press
    [Google Scholar]
  2. 2. 
    Takatori SC, Brady JF. 2016. Forces, stresses and the (thermo?) dynamics of active matter. Curr. Opin. Colloid Interface Sci. 21:24–33
    [Google Scholar]
  3. 3. 
    Li S, Bahnisikha D, Cannon S, Daymude J, Avinery R et al. 2021. Programming active cohesive granular matter with mechanically induced phase changes. Sci. Adv. 7:eabe8494
    [Google Scholar]
  4. 4. 
    Li F, Josephson DP, Stein A. 2011. Colloidal assembly: the road from particles to colloidal molecules and crystals. Angew. Chem. Int. Ed. 50:360–88
    [Google Scholar]
  5. 5. 
    Hayward RC, Saville DA, Aksay IA. 2000. Electrophoretic assembly of colloidal crystals with optically tunable micropatterns. Nature 404:56–59
    [Google Scholar]
  6. 6. 
    Semouchkina E, Duan R, Gandji NP, Jamilan S, Semouchkin G, Pandey R. 2016. Superluminal media formed by photonic crystals for transformation optics-based invisibility cloaks. J. Opt. 18:044007
    [Google Scholar]
  7. 7. 
    Lavergne F, Wendehenne H, Bäuerle T, Bechinger C. 2019. Group formation and cohesion of active particles with visual perception-dependent motility. Science 364:70–74
    [Google Scholar]
  8. 8. 
    Paulson JA, Mesbah A, Zhu XX, Molaro MC, Braatz RD. 2015. Control of self-assembly in micro- and nano-scale systems. J. Process Control 27:38–49
    [Google Scholar]
  9. 9. 
    Findeisen R, Grover MA, Wagner C, Maiworm M, Temirov R et al. 2016. Control on a molecular scale: a perspective. 2016 American Control Conference3069–82 Piscataway, NJ: IEEE
    [Google Scholar]
  10. 10. 
    Li Q, Jonas U, Zhao X, Kappl M 2008. The forces at work in colloidal self-assembly: a review on fundamental interactions between colloidal particles. Asia-Pac. J. Chem. Eng. 3:255–68
    [Google Scholar]
  11. 11. 
    Xu Z, Wang L, Fang F, Fu Y, Yin Z. 2016. A review on colloidal self-assembly and their application. Curr. Nanosci. 12:725–46
    [Google Scholar]
  12. 12. 
    Ma F, Yang X, Wu N 2018. Directed assembly of anisotropic particles under external fields. Anisotropic Particle Assemblies: Synthesis, Assembly, Modeling, and Applications N Wu, D Lee, A Striolo 131–65 Amsterdam: Elsevier
    [Google Scholar]
  13. 13. 
    Allen MP, Tildesley DJ 2017. Computer Simulation of Liquids Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  14. 14. 
    Padding JT, Louis AA. 2006. Hydrodynamic interactions and Brownian forces in colloidal suspensions: coarse-graining over time and length scales. Phys. Rev. E 74:031402
    [Google Scholar]
  15. 15. 
    Mazo RM. 2002. Brownian Motion: Fluctuations, Dynamics, and Applications Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  16. 16. 
    Wang D, Hermes M, Kotni R, Wu Y, Tasios N et al. 2018. Interplay between spherical confinement and particle shape on the self-assembly of rounded cubes. Nat. Commun. 9:2228
    [Google Scholar]
  17. 17. 
    Kim J, Song X, Ji F, Luo B, Ice N et al. 2017. Polymorphic assembly from beveled gold triangular nanoprisms. Nano Lett 17:3270–75
    [Google Scholar]
  18. 18. 
    Haji-Akbari A, Chen E, Engel M, Glotzer S 2013. Packing and self-assembly of truncated triangular bipyramids. Phys. Rev. 88:012127
    [Google Scholar]
  19. 19. 
    Zygmunt W, Teich E, Anders G, Glotzer S 2019. Topological order in densely packed anisotropic colloids. Phys. Rev. 100:032608
    [Google Scholar]
  20. 20. 
    Harper E, Waters B, Glotzer S. 2019. Hierarchical self-assembly of hard cube derivatives. Soft Matter 15:3733–39
    [Google Scholar]
  21. 21. 
    Moore T, Anderson J, Glotzer S 2021. Shape-driven entropic self-assembly of an open, reconfigurable, binary host-guest colloidal crystal. Soft Matter 17:2840–48
    [Google Scholar]
  22. 22. 
    Teich E, Anders G, Glotzer S 2021. Particle shape tunes fragility in hard polyhedron glass-formers. Soft Matter 17:600–10
    [Google Scholar]
  23. 23. 
    Rogers W, Shih W, Manaharan V. 2016. Using DNA to program the self-assembly of colloidal nanoparticles and microparticles. Nat. Rev. Mater. 1:16008
    [Google Scholar]
  24. 24. 
    Lin Q, Mason J, Li Z, Zhou W, O'Brien M et al. 2018. Building superlattices from individual nanoparticles via template-confined DNA-mediated assembly. Science 359:669–72
    [Google Scholar]
  25. 25. 
    Zion M, He X, Maass C, Sha R, Seeman N, Chaikin P. 2017. Self-assembled three-dimensional chiral colloidal architecture. Science 358:633–36
    [Google Scholar]
  26. 26. 
    Zhang Y, Lu F, Yager K, Lelie D, Gang O 2013. A general strategy for the DNA-mediated self-assembly of functional nanoparticles into heterogeneous systems. Nat. Nanotechnol. 8:865–72
    [Google Scholar]
  27. 27. 
    Xia X, Hu H, Ciamarra M, Ni R 2020. Linker-mediated self-assembly of mobile DNA-coated colloids. Sci. Adv. 6:6921
    [Google Scholar]
  28. 28. 
    Yan J, Bloom M, Bae S, Luijten E, Granick S 2012. Linking synchronization to self-assembly using magnetic Janus colloids. Nature 491:578–81
    [Google Scholar]
  29. 29. 
    Gao W, Pei A, Feng X, Hennessy C, Wang J. 2013. Organized self-assembly of Janus micromotors with hydrophobic hemispheres. J. Am. Chem. Soc. 135:998–1001
    [Google Scholar]
  30. 30. 
    Wang Y, Hollingsworth A, Yang S, Patel S, Pine D, Weck M. 2013. Patchy particle self-assembly via metal coordination. J. Am. Chem. Soc. 135:14064–67
    [Google Scholar]
  31. 31. 
    Kraft D, Ni R, Smallenburg F, Hermes M, Yoon K et al. 2012. Surface roughness directed self-assembly of patchy particles into colloidal micelles. PNAS 109:10787–92
    [Google Scholar]
  32. 32. 
    Zhang J, Grzybowski B, Granick S. 2017. Janus particle synthesis, assembly, and application. Langmuir 33:6964–77
    [Google Scholar]
  33. 33. 
    Kang C, Honciuc A. 2018. Self-assembly of Janus nanoparticles into transformable suprastructures. J. Phys. Chem. Lett. 9:1415–21
    [Google Scholar]
  34. 34. 
    Banik M, Sett S, Bakli C, Raychaudhuri A, Chakraborty S, Mukherjee R. 2021. Substrate wettability guided oriented self assembly of Janus particles. Sci. Rep. 11:1182
    [Google Scholar]
  35. 35. 
    Walther A, Müller A. 2013. Janus particles: synthesis, self-assembly, physical properties, and applications. Chem. Rev. 113:5194–261
    [Google Scholar]
  36. 36. 
    Beltran-Villegas DJ, Bevan MA. 2011. Free energy landscapes for colloidal crystal assembly. Soft Matter 7:3280–85
    [Google Scholar]
  37. 37. 
    Tang X, Bevan MA, Grover MA. 2017. The construction and application of Markov state models for colloidal self-assembly process control. Mol. Syst. Des. Eng. 2:78–88
    [Google Scholar]
  38. 38. 
    Xue Y, Ludovice PJ, Grover MA, Dsilva CJ, Kevrekidis IG. 2013. State reduction in molecular simulations. Comput. Chem. Eng. 151:102–10
    [Google Scholar]
  39. 39. 
    Oguz C, Gallivan MA. 2008. Optimization of a thin film process using a dynamic model extracted from molecular simulations. Automatica 44:1958–69
    [Google Scholar]
  40. 40. 
    Long AW, Ferguson AL. 2014. Nonlinear machine learning of patchy colloid self-assembly pathways and mechanisms. J. Phys. Chem. B 118:4228–44
    [Google Scholar]
  41. 41. 
    Yang Y, Thyagarajan R, Ford DM, Bevan MA. 2016. Dynamic colloidal assembly pathways via low dimensional models. J. Chem. Phys. 144:204904
    [Google Scholar]
  42. 42. 
    O'Leary J, Rao M, Pretti EJ, Paulson JA, Mittal J, Mesbah A 2021. Deep learning for characterizing the self-assembly of three-dimensional colloidal systems. Soft Matter 17:989–99
    [Google Scholar]
  43. 43. 
    Edwards T, Bevan M. 2014. Controlling colloidal particles with electric fields. Langmuir 30:10793–803
    [Google Scholar]
  44. 44. 
    Tang X, Rupp B, Yang Y, Edwards T, Grover M, Bevan M 2016. Optimal feedback controlled assembly of perfect crystals. ACS Nano 10:6791–98
    [Google Scholar]
  45. 45. 
    Zhang J, Yang J, Zhang Y, Bevan M. 2020. Controlling colloidal crystals via morphing energy landscapes and reinforcement learning. Sci. Adv. 6:6716
    [Google Scholar]
  46. 46. 
    Yakovlev E, Komarov K, Zaytsev K, Kryuchkov N, Koshelev K et al. 2017. Tunable two-dimensional assembly of colloidal particles in rotating electric fields. Sci. Rep. 7:13727
    [Google Scholar]
  47. 47. 
    Ahniyaz A, Sakamoto Y, Bergström L. 2007. Magnetic field-induced assembly of oriented superlattices from maghemite nanocubes. PNAS 104:17570–74
    [Google Scholar]
  48. 48. 
    Harraq A, Lee J, Bharti B 2020. Magnetic field-driven assembly and reconfiguration of multicomponent supraparticles. Sci. Adv. 6:5337
    [Google Scholar]
  49. 49. 
    Stenhammar J, Wittkowski R, Marenduzzo D, Cates M 2016. Light-induced self-assembly of active rectification devices. Sci. Adv. 2:1501850
    [Google Scholar]
  50. 50. 
    Marchetti M, Joanny J, Ramaswamy S, Liverpool T, Prost J et al. 2013. Hydrodynamics of soft active matter. Rev. Mod. Phys. 85:1143
    [Google Scholar]
  51. 51. 
    Vilanova N, Feijter I, Teunissen A, Voets I 2018. Light induced assembly and self-sorting of silica microparticles. Sci. Rep. 8:1271
    [Google Scholar]
  52. 52. 
    Elacqua E, Zheng X, Weck M. 2017. Light-mediated reversible assembly of polymeric colloids. ACS Macro Lett 6:1060–65
    [Google Scholar]
  53. 53. 
    Klajn R, Bishop K, Grzybowski B 2006. Light-controlled self-assembly of reversible and irreversible nanaparticle suprastructures. PNAS 104:10305–9
    [Google Scholar]
  54. 54. 
    Liu J, Song K, Shin Y, Liu X, Chen J et al. 2019. Light-induced self-assembly of cubic CsPbBr3 perovskite nanocrystals into nanowires. Chem. Mater. 31:6642–49
    [Google Scholar]
  55. 55. 
    Zhang J, Guo J, Mou F, Guan J. 2018. Light-controlled swarming and assembly of colloidal particles. Micromachines 9:88
    [Google Scholar]
  56. 56. 
    Schmidt F, Liebchen B, Löwen H, Volpe G. 2019. Light-controlled assembly of active colloidal molecules. J. Chem. Phys. 150:094905
    [Google Scholar]
  57. 57. 
    Ropp C, Cummins Z, Nah S, Qin S, Seog J et al. 2013. Fabrication of nanoassemblies using flow control. Nano Lett 13:3936–41
    [Google Scholar]
  58. 58. 
    Pandey K, Prabhakaran D, Basu S 2019. Review of transport processes and particle self-assembly in acoustically levitated nanofluid droplets. Phys. Fluids 31:112102
    [Google Scholar]
  59. 59. 
    Destgeer G, Hashmi A, Park J, Ahmed H, Afzal M, Sung H. 2019. Microparticle self-assembly induced by travelling surface acoustic waves. RCS Adv 9:7916–21
    [Google Scholar]
  60. 60. 
    Takella M, Juárez J. 2018. High-throughput acoustofluidic self-assembly of colloidal crystals. ACS Omega 3:1425–36
    [Google Scholar]
  61. 61. 
    Chen Q, Bae S, Granick S. 2011. Directed self-assembly of a colloidal kagome lattice. Nature 469:381–85
    [Google Scholar]
  62. 62. 
    Bodnarchuk M, Kovalenko M, Heiss W, Talapin D 2010. Energetic and entropic contributions to self-assembly of binary nanocrystal superlattices: temperature as the structure-directing factor. J. Am. Chem. Soc. 132:11967–77
    [Google Scholar]
  63. 63. 
    Bevan M, Ford D, Grover M, Shapiro B, Maroudas D et al. 2015. Controlling assembly of colloidal particles into structured objects: basic strategy and a case study. J. Process Control 27:64–75
    [Google Scholar]
  64. 64. 
    Wang L, Wang J 2019. Self-assembly of colloids based on microfluidics. Nanoscale 11:16708–22
    [Google Scholar]
  65. 65. 
    Wang P, Liang J, Wang L. 2020. Single-shot ultrafast imaging attaining 70 trillion frames per second. Nat. Commun. 11:2091
    [Google Scholar]
  66. 66. 
    Altemose A, Harris A, Sen A. 2020. Autonomous formation and annealing of colloidal crystals induced by light-powered oscillations of active particles. ChemSystemsChem 2:e1900021
    [Google Scholar]
  67. 67. 
    Canette A, Briandet R 2014. Confocal laser scanning microscopy. Encyclopedia of Food Microbiology CA Batt, ML Tortorello 676–83 Amsterdam: Elsevier, 2nd ed..
    [Google Scholar]
  68. 68. 
    Choi S, Kim P, Boutilier R, Kim M, Lee Y, Lee H 2013. Development of a high speed laser scanning confocal microscope with an acquisition rate up to 200 frames per second. Opt. Express 21:23611–18
    [Google Scholar]
  69. 69. 
    Trinh L, Fraser S 2015. Imaging the cell and molecular dynamics of craniofacial development. Curr. Top. Dev. Biol. 115:599–629
    [Google Scholar]
  70. 70. 
    Demirörs A, Alison L. 2018. Electric field assembly of colloidal superstructures. J. Phys. Chem. Lett. 9:4437–43
    [Google Scholar]
  71. 71. 
    Kim Y, Shah A, Solomon M. 2014. Spatially and temporally reconfigurable assembly of colloidal crystals. Nat. Commun. 5:3676
    [Google Scholar]
  72. 72. 
    Kim C, Kim W, Lee K, Yoo H 2019. High-speed color three-dimensional measurement based on parallel confocal detection with a focus tunable lens. Opt. Express 27:28466
    [Google Scholar]
  73. 73. 
    Nych A, Ognysta U, Skarabot M, Ravnik M, Zumer S, Musevic I 2013. Assembly and control of 3D nematic dipolar colloidal crystals. Nat. Commun. 4:1489
    [Google Scholar]
  74. 74. 
    Oho E, Suzuki K, Yamazaki S. 2020. Applying fast scanning method coupled with digital image processing technology as standard acquisition mode for scanning electron microscopy. Scanning 2020:4979431
    [Google Scholar]
  75. 75. 
    Suh Y, Pham Q, Shao B, Won Y. 2019. The control of colloidal grain boundaries through evaporative vertical self-assembly. Small 15:1804523
    [Google Scholar]
  76. 76. 
    Deleted in proof
  77. 77. 
    Mayer M, Tebbe M, Kuttner C, Schnepf M, König T, Fery A 2016. Template-assisted colloidal self-assembly of macroscopic magnetic metasurfaces. Faraday Discuss 191:159–76
    [Google Scholar]
  78. 78. 
    Boles M, Engel M, Talapin D. 2016. Self-assembly of colloidal nanocrystals: from intricate structures to functional materials. Chem. Rev. 116:11220–89
    [Google Scholar]
  79. 79. 
    Deleted in proof
  80. 80. 
    Di Gianfrancesco A. 2017. Technologies for chemical analyses, microstructural and inspection investigations. Materials for Ultra-Supercritical and Advanced Ultra-Supercritical Power Plants A Di Gianfrancesco 197–245 Duxford, UK: Woodhead
    [Google Scholar]
  81. 81. 
    Deleted in proof
  82. 82. 
    Stemmer A, Schitter G, Rieber J, Allgöwer F 2005. Control strategies towards faster quantitative imaging in atomic force microscopy. Eur. J. Control 11:384–95
    [Google Scholar]
  83. 83. 
    Butterworth J, Pao L, Abramovitch D. 2009. A comparison of control architectures for atomic force microscopes. Asian J. Control 11:175–81
    [Google Scholar]
  84. 84. 
    Mahdavi M, Nikooienejad N, Moheimani S 2020. AFM microcantilever with a collocated AIN sensor-actuator pair: enabling efficient Q-control for dynamic imaging. J. Microelectromech. Syst. 29:661–68
    [Google Scholar]
  85. 85. 
    Dong F, Liu M, Grebe V, Ward M, Weck M 2020. Assembly of shape-tunable colloidal dimers in a dielectrophoretic field. Chem. Mater. 32:6898–905
    [Google Scholar]
  86. 86. 
    Swan J, Bauer J, Liu Y, Furst E. 2014. Directed colloidal self-assembly in toggled magnetic fields. Soft Matter 10:1102–9
    [Google Scholar]
  87. 87. 
    Sherman Z, Swan J 2016. Dynamic, directed self-assembly of nanoparticles via toggled interactions. ACS Nano 10:5260–71
    [Google Scholar]
  88. 88. 
    Sherman Z, Swan J 2019. Transmutable colloidal crystals and active phase separation via dynamic, directed self-assembly with toggled external fields. ACS Nano 13:764–71
    [Google Scholar]
  89. 89. 
    Kim H, Sau M, Furst E 2020. An expanded state diagram for the directed self-assembly of colloidal suspensions in toggled fields. Langmuir 36:9926–34
    [Google Scholar]
  90. 90. 
    Tang X, Zhang J, Bevan MA, Grover MA. 2017. A comparison of open-loop and closed-loop strategies in colloidal self-assembly. J. Process Control 60:141–51
    [Google Scholar]
  91. 91. 
    Klotsa D, Jack R. 2013. Controlling crystal self-assembly using a real-time feedback scheme. J. Chem. Phys. 138:094502
    [Google Scholar]
  92. 92. 
    Fullerton C, Jack R. 2016. Optimising self-assembly through time-dependent interactions. J. Chem. Phys. 145:244505
    [Google Scholar]
  93. 93. 
    Nemoto T, Fodor E, Cates M, Jack R, Tailleur J. 2019. Optimizing active work: dynamic phase transitions, collective motion, and jamming. Phys. Rev. 99:022695
    [Google Scholar]
  94. 94. 
    Guioth J, Jack R. 2020. Dynamical phase transitions for the activity biased Ising model in a magnetic field. J. Stat. Mech. 2020:063215
    [Google Scholar]
  95. 95. 
    Gao Y, Lakerveld R. 2019. Gain scheduling PID control for directed self-assembly of colloidal particles in microfluidic devices. AIChE J 65:16582
    [Google Scholar]
  96. 96. 
    Gao Y, Lakerveld R. 2018. Feedback control for defect-free alignment of colloidal particles. Lab Chip 18:2099–110
    [Google Scholar]
  97. 97. 
    Wang Q, Yang L, Yu J, Chiu P, Zheng Y, Zhang L. 2020. Real-time magnetic navigation of a rotating colloidal microswarm under ultrasound guidance. IEEE Trans. Biomed. Eng. 67:3403–12
    [Google Scholar]
  98. 98. 
    Fernandez-Rodriguez A, Grillo F, Alvarez L, Rathlef M, Buttinoni I et al. 2020. Feedback-controlled active Brownian colloids with space-dependent rotational dynamics. Nat. Commun. 11:4223
    [Google Scholar]
  99. 99. 
    Tang X, Yang Y, Bevan M, Grover M. 2014. Grain boundary control in colloidal self-assembly with dynamic programming. 2014 American Control Conference1120–25 Piscataway, NJ: IEEE
    [Google Scholar]
  100. 100. 
    Xue Y, Beltran-Villegas D, Tang X, Bevan M, Grover M. 2014. Optimal design of a colloidal self-assembly process. IEEE Trans. Control Syst. Technol. 22:1956–63
    [Google Scholar]
  101. 101. 
    Whitelam S, Tamblyn I. 2020. Learning to grow: control of material self-assembly using evolutionary reinforcement learning. Phys. Rev. 101:052604
    [Google Scholar]
  102. 102. 
    Ye Y, Badilescu S, Truong V, Rochon P, Natansohn A. 2001. Self-assembly of colloidal spheres on patterned substrates. Appl. Phys. Lett. 79:872
    [Google Scholar]
  103. 103. 
    Cherepanov V, Naumovets A, Posudievsky O, Koshechko V, Pokhodenko V 2020. Self-assembly of the deposited graphene-like nanoparticles and possible nanotrack artefacts in AFM studies. Nano Express 1:010004
    [Google Scholar]
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