THESIS
2017
xxiv, 136 pages : illustrations (some color) ; 30 cm
Abstract
Self-assembly is the autonomous organization of components into patterns or structures
without human intervention, which is common throughout nature and technology. An
important reason for human's interest in self-assembly is that self-assembly provides a
practical approach to synthesizing useful structures at the nano/micro scale with exciting
applications such as photonics, electronics, drug delivery, sensors and tissues etc.
However, optimal design of a self-assembly process to obtain nano/micro-structured
material with desired properties is challenging due to the stochastic nature and highly
nonlinear behavior of self-assembly, which may lead to defects and kinetic trapping. In
this thesis, new optimization and control methods are investigated for self-assembly to
overcome...[
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Self-assembly is the autonomous organization of components into patterns or structures
without human intervention, which is common throughout nature and technology. An
important reason for human's interest in self-assembly is that self-assembly provides a
practical approach to synthesizing useful structures at the nano/micro scale with exciting
applications such as photonics, electronics, drug delivery, sensors and tissues etc.
However, optimal design of a self-assembly process to obtain nano/micro-structured
material with desired properties is challenging due to the stochastic nature and highly
nonlinear behavior of self-assembly, which may lead to defects and kinetic trapping. In
this thesis, new optimization and control methods are investigated for self-assembly to
overcome this challenge. Two application areas are investigated, which involve structural
design of DNA tiles and directed self-assembly of colloidal particles.
First, a new optimization-based approach for structural design of DNA tiles is developed.
The approach is based on potential-energy minimization of a desired structure. The
significance of multiple local minima is demonstrated and various algorithms for
optimization are investigated to identify the global minimum, which requires model
reduction. The potential energy model is reformulated by including integer variables as
degrees of freedom for structural design. By minimization of the new potential energy
function, an optimal structural design is identified that is consistent with experimental
results reported in literature when available.
Second, novel feedback control strategies to self-assemble colloidal particles in a
microfluidic device are investigated experimentally. Different electrokinetic phenomena
are exploited for directing self-assembly by manipulating field frequency and voltage. An
automated two-step control strategy to self-assembly colloidal particles into non-periodic
structures with single-particle resolution is presented. In the first step, the local particle
density is controlled at a desired level, after which a second automated control step is
applied to align the particles into a defect-free line with single-particle resolution. To
address the non-linearity of the system, an alternative design of the feedback controller
for particle density based on gain-scheduled PID control is also developed. The results
show that a wide range of set points for particle density can be attained with this controller,
which makes the defect-free assembly of a wide variety of non-periodic small-scale
structures attainable.
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