calque

Workshops

Self-assembly: from fundamental principles to design rules for experiment

March 4, 2013 to March 6, 2013
Location : CECAM-HQ-EPFL, Lausanne, Switzerland
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Organisers

  • Steve Whitelam (Lawrence Berkeley National Laboratory, USA)
  • Rob Jack (University of Cambridge, United Kingdom)
  • Michael Hagan (Brandeis University, Waltham, USA)

Supports

   CECAM

Lawrence Berkeley National Lab Materials Sciences Division

APS Topical Group on Statistical and Nonlinear Physics

National Science Foundation

Brandeis MRSEC

Description

 

Components are said to ‘self-assemble’ when they organize to form stable patterns or aggregates without external direction. Self-assembly is driven by interactions as different as hydrogen bonds and capillary forces, is undergone by components ranging in size from Angstroms to centimeters, and occurs both in inorganic settings and living organisms [1–4]. Mimicry of the self-assembly seen in the natural world promises the development of new, functional materials patterned on the nanometer scale [5, 6]. 
To realize this promise, however, we need to understand the fundamental principles that govern assembly driven by thermal fluctuations. With these principles in hand we could conceive general ‘design rules’ for experiment that would allow us to ignore most of experimental design space – which is vast – and focus on the small space of parameters that gives rise to successful assembly. 
This space is small because of the competition of many factors. Imagine that our goal is to engineer a set of components that will self-assemble in solution into a desired structure. At first sight it might seem a good idea to equip our components with strong and specific (e.g. directional and/or chemically selective) attractions that render the desired structure thermodynamically stable with respect to solution, and with respect to other possible assemblies. However, a few experiments (or computer simulations) would quickly reveal that components undergoing Brownian motion will not necessarily arrange themselves spontaneously into the wanted structure. For one, successful assembly requires components’ free energies of binding to be weak enough that component-component bonds can be disrupted by thermal fluctuations. Sufficiently frequent disruptions confer upon assembly the crucial property of quasi-reversibility, allowing bound components to temporarily unbind, and so to correct errors made upon initial binding [7]. If inter-component binding is too strong then this error-correction mechanism is suppressed, and the result is a malformed assembly. Furthermore, if interactions are overly specific then productive binding events will be too rare to promote structural growth on the requisite timescale: some ‘slack’ in the way components fit together is needed. But satisfying the latter requirement can render viable other structures, ordered or disordered, and such metastable structures can preempt the formation of the stable assembly [8]. 
NOVELTY
This workshop aims to bring together computer simulators and experimentalists whose work bears upon the fundamental rules that govern self-assembly. Participants will be united not by their study of a particular physical system, but by their focus on the rule-based control of assembly. Our proposed speaker list is highly multidisciplinary, and includes theoretical physicists, virologists, synthetic biologists, and materials scientists. Our understanding of several basic principles of self-assembly has been advanced by recent computer simulations; part of the novelty of our proposed workshop is in putting these new ideas side-by-side with recent work in which control of assembly is demonstrated in real systems, in order for experimentalists and simulators to learn from each other. How can general concepts like ‘reversibility’ and other lessons learned from simple models be translated into improvements in experimental design? And can we extract general principles from the successful control of specific experiments? 
[1] S. C. Glotzer and M. J. Solomon, Nature Materials 6, 557 (2007).
[2] G. M. Whitesides and B. Grzybowski, Science 295, 2418 (2002). 
[3] K. Ariga, J. P. Hill, M. V. Lee, A. Vinu, R. Charvet, and S. Acharya, Science and Technology of Advanced Materials 9, 014109 (2008). 
[4] S. Zhang, D. M. Marini, W. Hwang, and S. Santoso, Current Opinion in Chemical Biology 6, 865 (2002). 
[5] J. C. Huie, Smart Materials and Structures 12, 264 (2003). 
[6] S. Zhang, Nature Biotechnology 21, 1171 (2003). 
[7] G. M. Whitesides and M. Boncheva, Proceedings of the National Academy of Sciences 99, 4769 (2002). 
[8] W. Ostwald, Z. Phys. Chem. 22, 289 (1897). 

Components are said to ‘self-assemble’ when they organize to form stable patterns or aggregates without external direction. Self-assembly is driven by interactions as different as hydrogen bonds and capillary forces, is undergone by components ranging in size from Angstroms to centimeters, and occurs both in inorganic settings and living organisms [1–4]. Mimicry of the self-assembly seen in the natural world promises the development of new, functional materials patterned on the nanometer scale [5, 6]. 
To realize this promise, however, we need to understand the fundamental principles that govern assembly driven by thermal fluctuations. With these principles in hand we could conceive general ‘design rules’ for experiment that would allow us to ignore most of experimental design space – which is vast – and focus on the small space of parameters that gives rise to successful assembly. 
This space is small because of the competition of many factors. Imagine that our goal is to engineer a set of components that will self-assemble in solution into a desired structure. At first sight it might seem a good idea to equip our components with strong and specific (e.g. directional and/or chemically selective) attractions that render the desired structure thermodynamically stable with respect to solution, and with respect to other possible assemblies. However, a few experiments (or computer simulations) would quickly reveal that components undergoing Brownian motion will not necessarily arrange themselves spontaneously into the wanted structure. For one, successful assembly requires components’ free energies of binding to be weak enough that component-component bonds can be disrupted by thermal fluctuations. Sufficiently frequent disruptions confer upon assembly the crucial property of quasi-reversibility, allowing bound components to temporarily unbind, and so to correct errors made upon initial binding [7]. If inter-component binding is too strong then this error-correction mechanism is suppressed, and the result is a malformed assembly. Furthermore, if interactions are overly specific then productive binding events will be too rare to promote structural growth on the requisite timescale: some ‘slack’ in the way components fit together is needed. But satisfying the latter requirement can render viable other structures, ordered or disordered, and such metastable structures can preempt the formation of the stable assembly [8]. 

This workshop aims to bring together computer simulators and experimentalists whose work bears upon the fundamental rules that govern self-assembly. Participants will be united not by their study of a particular physical system, but by their focus on the rule-based control of assembly. Our proposed speaker list is highly multidisciplinary, and includes theoretical physicists, virologists, synthetic biologists, and materials scientists. Our understanding of several basic principles of self-assembly has been advanced by recent computer simulations; part of the novelty of our proposed workshop is in putting these new ideas side-by-side with recent work in which control of assembly is demonstrated in real systems, in order for experimentalists and simulators to learn from each other. How can general concepts like ‘reversibility’ and other lessons learned from simple models be translated into improvements in experimental design? And can we extract general principles from the successful control of specific experiments? 

Computational work will focus on recent advances coming from the study of coarse-grained, minimalist simulation models whose power is their ability to reveal the effect upon assembly of specific physical mechanisms [9–12]. Work on such models has advanced our understanding of just how ‘reversible’ successful assembly must be [13, 14]; has quantified notions of ‘positive’ design for self-assembly, done to promote a desired structure, and ‘negative’ design, done to ward off undesirable structures [15, 16]; and suggests strategies for the design of specified structures [17] and the selection of self-assembly pathways [18,19] through variation of ambient conditions [20, 21] or microscopic interactions [22]. Experimental work will address the extent to which design rules can be successful in specific systems. Recent studies show the power of the rational design of molecular structures [23], symmetries [24], and interaction specificities [25–28]. The experimental synthesis of self-assembling components of precise shapes and sizes is now common: witness "lock and key" [29] or "patchy" [30] colloids. As a consequence, experimental design space is continually becoming larger; practically-applicable design rules are now an absolute necessity.

References

[1] S. C. Glotzer and M. J. Solomon, Nature Materials 6, 557 (2007).
[2] G. M. Whitesides and B. Grzybowski, Science 295, 2418 (2002).
[3] K. Ariga, J. P. Hill, M. V. Lee, A. Vinu, R. Charvet, and S. Acharya, Science and Technology of Advanced Materials 9, 014109 (2008).
[4] S. Zhang, D. M. Marini, W. Hwang, and S. Santoso, Current Opinion in Chemical Biology 6, 865 (2002).
[5] J. C. Huie, Smart Materials and Structures 12, 264 (2003).
[6] S. Zhang, Nature Biotechnology 21, 1171 (2003).
[7] G. M. Whitesides and M. Boncheva, Proceedings of the National Academy of Sciences 99, 4769 (2002).
[8] W. Ostwald, Z. Phys. Chem. 22, 289 (1897).
[9] S. Whitelam, E. H. Feng, M. F. Hagan, and P. L. Geissler, Soft Matter 5, 1251 (2009).
[10] M. Hagan and D. Chandler, Biophys. J. 91, 42 (2006).
[11] F. Sciortino, A. Giacometti, and G. Pastore, Physical Review Letters 103, 237801 (2009).
[12] E. Rabani, D. R. Reichman, P. L. Geissler, L. E. Brus, et al., Nature 426, 271 (2003).
[13] R. L. Jack, M. F. Hagan, and D. Chandler, Physical Review E 76, 21119 (2007).
[14] D. Rapaport, Physical Review Letters 101, 186101 (2008).
[15] J. P. K. Doye, A. A. Louis, and M. Vendruscolo, Physical Biology 1, P9 (2004).
[16] J. P. K. Doye, A. A. Louis, I. C. Lin, L. R. Allen, E. G. Noya, A. W. Wilber, H. C. Kok, and R. Lyus, Physical Chemistry Chemical Physics 9, 2197 (2007).
[17] S. Hormoz and M. P. Brenner, Proceedings of the National Academy of Sciences 108, 5193 (2011).
[18] P. R. Wolde and D. Frenkel, Science 277, 1975 (1997).
[19] P. R. Wolde and D. Frenkel, Physical Chemistry Chemical Physics 1, 2191 (1999).
[20] C. Desgranges and J. Delhommelle, Physical Review Letters 98, 235502 (2007).
[21] N. Duff; and B. Peters, The Journal of Chemical Physics 131, 184101 (2009).
[22] L. O. Hedges and S. Whitelam, arXiv:1103.0334 (2011).
[23] K. T. Nam, S. A. Shelby, P. H. Choi, A. B. Marciel, R. Chen, L. Tan, T. K. Chu, R. A. Mesch, B. C. Lee, M. D. Connolly, et al., Nature Materials 9, 454 (2010).
[24] M. O. Blunt, J. C. Russell, M. C. Giménez-López, J. P. Garrahan, X. Lin, M. Schröder, N. R. Champness, and P. H. Beton, Science 322, 1077 (2008).
[25] E. Winfree, F. Liu, L. A. Wenzler, and N. C. Seeman, Nature 394, 539 (1998).
[26] P. W. K. Rothemund, Nature 440, 297 (2006).
[27] S. Park, A. Lytton-Jean, B. Lee, S. Weigand, G. Schatz, and C. Mirkin, Nature 451, 553 (2008).
[28] D. Nykypanchuk, M. M. Maye, D. van der Lelie, and O. Gang, Nature 451, 549 (2008).
[29] S. Sacanna, W. Irvine, P. Chaikin, and D. Pine, Nature 464, 575 (2010).
[30] D. Kraft, J. Groenewold, and W. Kegel, Soft Matter 5, 3823 (2009).