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Hydrodynamically Tunable Affinities for Fluidic Assembly

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Download Hi-Res ImageDownload to MS-PowerPointCite This:Langmuir 2009, 25, 6, 3769-3774
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    Hydrodynamically Tunable Affinities for Fluidic Assembly
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    Sibley School of Mechanical and Aerospace Engineering and Computing and Information Science, Cornell University, Ithaca, New York 14853
    * To whom correspondence should be addressed.
    †Sibley School of Mechanical and Aerospace Engineering.
    ‡Computing and Information Science.
    Other Access OptionsSupporting Information (5)

    Langmuir

    Cite this: Langmuir 2009, 25, 6, 3769–3774
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    https://doi.org/10.1021/la803517f
    Published February 10, 2009
    Copyright © 2009 American Chemical Society
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    Abstract

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    Most current micro- and nanoscale self-assembly methods rely on static, preprogrammed assembly affinities between the assembling components such as capillarity, DNA base pair matching, and geometric interactions. While these techniques have proven successful at creating relatively simple and regular structures, it is difficult to adapt these methods to enable dynamic reconfiguration of the structure or on-the-fly error correction. Here we demonstrate a technique to hydrodynamically tune affinities between assembling components by direct thermal modulation of the local viscosity field surrounding them. This approach is shown here for two-dimensional silicon elements of 500 μm length using a thermorheological fluid that undergoes reversible sol−gel transition on heating. Using this system, we demonstrate the ability to dynamically change the assembly point in a fluidic self-assembly process and selectively attract and reject elements from a larger structure. Although this technique is demonstrated here for a small number of passive mobile components around a fixed structure, it has the potential to overcome some of the limitations of current static affinity based self-assembly.

    Copyright © 2009 American Chemical Society

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    Supporting Information

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    Details of numerical simulations performed to design the heaters used in the experiments and supplementary movies showing the experiments that have been described above. This material is available free of charge via the Internet at http://pubs.acs.org.

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    Cited By

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    This article is cited by 16 publications.

    1. Li Jiang and David Erickson . Directed Self-Assembly of Microcomponents Enabled by Laser-Activated Bubble Latching. Langmuir 2011, 27 (17) , 11259-11264. https://doi.org/10.1021/la2019617
    2. Junyi Han, Quentin Lahondes, Shuhei Miyashita. Size Changing Soft Modules for Temperature Regulated Self-assembly and Self-disassembly. 2022, 461-466. https://doi.org/10.1109/RoboSoft54090.2022.9762093
    3. Frédéric Demoly, Jean-Claude André, . Is order creation through disorder in additive manufacturing possible?. Cogent Engineering 2021, 8 (1) https://doi.org/10.1080/23311916.2021.1889110
    4. Taiki Okuyama, Tatsuya Hikida, Taiji Okano, Hiroaki Suzuki. Selective self-assembly of three-component system based on hydrophilic/hydrophobic patterning. Sensors and Actuators A: Physical 2020, 312 , 112143. https://doi.org/10.1016/j.sna.2020.112143
    5. Bahar Haghighat, Massimo Mastrangeli, Grégory Mermoud, Felix Schill, Alcherio Martinoli. Fluid-Mediated Stochastic Self-Assembly at Centimetric and Sub-Millimetric Scales: Design, Modeling, and Control. Micromachines 2016, 7 (8) , 138. https://doi.org/10.3390/mi7080138
    6. Paul White, Stella Latscha, Mark Yim. Modeling of a Dielectric Elastomer Bender Actuator. Actuators 2014, 3 (3) , 245-269. https://doi.org/10.3390/act3030245
    7. Nathan B. Crane, Onursal Onen, Jose Carballo, Qi Ni, Rasim Guldiken. Fluidic assembly at the microscale: progress and prospects. Microfluidics and Nanofluidics 2013, 14 (3-4) , 383-419. https://doi.org/10.1007/s10404-012-1060-1
    8. Massimo Mastrangeli. Surface Tension-Driven Self-Assembly. 2013, 227-253. https://doi.org/10.1007/978-3-642-37552-1_12
    9. M. A. Jalil, Surachart Kamoldilok, T. Saktioto, C. T. Ong, Preecha P. Yupapin. Drug trapping and delivery for Alzheimer's diagnosis. Artificial Cells, Blood Substitutes, and Biotechnology 2012, 40 (5) , 303-308. https://doi.org/10.3109/10731199.2012.657203
    10. Umut Atakan Gurkan, Savas Tasoglu, Doga Kavaz, Melik C. Demirel, Utkan Demirci. Emerging Technologies for Assembly of Microscale Hydrogels. Advanced Healthcare Materials 2012, 1 (2) , 149-158. https://doi.org/10.1002/adhm.201200011
    11. Michael T Tolley, Hod Lipson. On-line assembly planning for stochastically reconfigurable systems. The International Journal of Robotics Research 2011, 30 (13) , 1566-1584. https://doi.org/10.1177/0278364911398160
    12. Paul J. White, Stella Latscha, Steve Schlaefer, Mark Yim. Dielectric elastomer bender actuator applied to modular robotics. 2011, 408-413. https://doi.org/10.1109/IROS.2011.6094898
    13. Massimo Mastrangeli, Grégory Mermoud, Alcherio Martinoli. Modeling Self-Assembly Across Scales: The Unifying Perspective of Smart Minimal Particles. Micromachines 2011, 2 (2) , 82-115. https://doi.org/10.3390/mi2020082
    14. David Erickson, Xavier Serey, Yih-Fan Chen, Sudeep Mandal. Nanomanipulation using near field photonics. Lab on a Chip 2011, 11 (6) , 995. https://doi.org/10.1039/c0lc00482k
    15. Jonas Neubert, Abraham P Cantwell, Stephane Constantin, Michael Kalontarov, David Erickson, Hod Lipson. A robotic module for stochastic fluidic assembly of 3D self-reconfiguring structures. 2010, 2479-2484. https://doi.org/10.1109/ROBOT.2010.5509455
    16. David Erickson. Vivo-Fluidics and Programmable Matter. 2010, 553-576. https://doi.org/10.1007/978-90-481-9029-4_26
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    Langmuir

    Cite this: Langmuir 2009, 25, 6, 3769–3774
    Click to copy citationCitation copied!
    https://doi.org/10.1021/la803517f
    Published February 10, 2009
    Copyright © 2009 American Chemical Society
    Request reuse permissions

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