Deck the Walls with Anisotropic Colloids in Nematic Liquid Crystals.

Nematic liquid crystals (NLCs) offer remarkable opportunities to direct colloids to form complex structures. The elastic energy field that dictates colloid interactions is determined by the NLC director field, which is sensitive to and can be controlled by boundaries including vessel walls and colloid surfaces. By molding the director field via liquid-crystal alignment on these surfaces, elastic energy landscapes can be defined to drive structure formation. We focus on colloids in otherwise defect-free director fields formed near undulating walls. Colloids can be driven along prescribed paths and directed to well-defined docking sites on such wavy boundaries. Colloids that impose strong alignment generate topologically required companion defects. Configurations for homeotropic colloids include a dipolar structure formed by the colloid and its companion hedgehog defect or a quadrupolar structure formed by the colloid and its companion Saturn ring. Adjacent to wavy walls with wavelengths larger than the colloid diameter, spherical particles are attracted to locations along the wall with distortions in the nematic director field that complement those from the colloid. This is the basis of lock-and-key interactions. Here, we study ellipsoidal colloids with homeotropic anchoring near complex undulating walls. The walls impose distortions that decay with distance from the wall to a uniform director in the far field. Ellipsoids form dipolar defect configurations with the colloid's major axis aligned with the far field director. Two distinct quadrupolar defect structures also form, stabilized by confinement; these include the Saturn I configuration with the ellipsoid's major axis aligned with the far field director and the Saturn II configuration with the major axis perpendicular to the far field director. The ellipsoid orientation varies only weakly in bulk and near undulating walls. All configurations are attracted to walls with long, shallow waves. However, for walls with wavelengths that are small compared to the colloid length, Saturn II is repelled, allowing selective docking of aligned objects. Deep, narrow wells prompt the insertion of a vertical ellipsoid. By introducing an opening at the bottom of such a deep well, we study colloids within pores that connect two domains. Ellipsoids with different aspect ratios find different equilibrium positions. An ellipsoid of the right dimension and aspect ratio can plug the pore, creating a class of 2D selective membranes

Embedded Energy Landscapes In Soft Matter For Micro-Robotics And Reconfigurable Structures

The ability to manipulate microscale objects with precision to form complex structures is central to the field of micro-robotics and to the realization of reconfigurable systems. Understanding and exploiting the forces that dominate at the microscale in complex environments pose major challenges and open untapped opportunities. This is particularly the case for micro-particles in soft milieu like fluid interfaces or nematic liquid crystalline fluids, which deform or reorganize around dispersed colloids or near bounding surfaces. These energetically costly deformations can be designed as embedded energy landscapes, a form of physical intelligence, to dictate emergent colloidal interactions. The fluid nature of these soft milieu allows colloids to move to minimize the free energy and externally forced robotic structures to re-write the embedded energy landscapes in the domain. Such physically intelligent systems are of great interest at the intersection of materials science and micro-robotics. Micro-particles on fluid interfaces deform the interface shape, migrate, and assemble to minimize the capillary energy. In the first part of my thesis, I design and fabricate a magnetic micro-robot as a mobile curvature source to interact with passive colloids on the water/oil interface. An analytical expression that includes both capillary and hydrodynamic interactions is derived and captures the main feature of experimental observations. I further demonstrate multiple micro-robotic tasks including directed assembly, cargo carrying, desired release and cargo delivery on the interface. Micro-particles in confined nematic liquid crystals (NLCs) distort the nematic director field, generating interactions. These interactions depend strongly on the colloids shape and surface chemistry, geometric frustration of director field and behavior of dynamic topological defects. To probe far-from-equilibrium dynamics, I fabricate a magnetic disk with hybrid anchoring. Upon controlled rotation, the disk’s companion defect undergoes periodic rearrangement, executing a complex swim stroke that propels disk translation. I study this new swimming modality in both high and low Ericksen number regimes. At high rotation rates, the defect elongates significantly adjacent to the disk, generating broken symmetries that allow steering of the disk. This ability is exploited in path planning. Thereafter, I design a four-armed micro-robot as a mobile distortion source to promote passive colloids assembly at particular sites via emergent interactions in NLCs whose strengths are characterized and found to be several orders of magnitude larger than thermal energies. While the strength of theses interactions allows colloidal cargo to be carried with the micro-robot during translation, it poses challenges for cargo release. We find that rotation of this micro-robot generates a complex dynamic defect-sharing event with colloidal cargo that spurs cargo release. Thereafter, I demonstrate the ability to exploit NLC elastodynamics to construct reconfigurable colloidal structures in a micro-robotics platform. At the colloidal scale, rotation dynamics are easier to generate, and this motivated me to exploit the topological swimming modality of the micro-robot. Using programmable rotating fields to direct the micro-robot’s motion, I achieve fully autonomous cargo manipulations including approach, assembly, transport and release. The ability to dynamically manipulate micro-particles and their structures in soft matter systems with embedded energy landscapes, as demonstrated in this thesis, creates new possibilities for micro-robotics and reconfigurable systems

Magnetic control of the valley degree of freedom of massive Dirac fermions with application to transition metal dichalcogenides

We study the valley-dependent magnetic and transport properties of massive Dirac fermions in multivalley systems such as the transition metal dichalcogenides. The asymmetry of the zeroth Landau level between valleys and the enhanced magnetic susceptibility can be attributed to the different orbital magnetic moment tied with each valley. This allows the valley polarization to be controlled by tuning the external magnetic field and the doping level. As a result of this magnetic field induced valley polarization, there exists an extra contribution to the ordinary Hall effect. All these effects can be captured by a low energy effective theory with a valley-orbit coupling term.Comment: 9 pages, 6 figure

RMT: Rule-based Metamorphic Testing for Autonomous Driving Models

Deep neural network models are widely used for perception and control in autonomous driving. Recent work uses metamorphic testing but is limited to using equality-based metamorphic relations and does not provide expressiveness for defining inequality-based metamorphic relations. To encode real world traffic rules, domain experts must be able to express higher order relations e.g., a vehicle should decrease speed in certain ratio, when there is a vehicle x meters ahead and compositionality e.g., a vehicle must have a larger deceleration, when there is a vehicle ahead and when the weather is rainy and proportional compounding effect to the test outcome. We design RMT, a declarative rule-based metamorphic testing framework. It provides three components that work in concert:(1) a domain specific language that enables an expert to express higher-order, compositional metamorphic relations, (2) pluggable transformation engines built on a variety of image and graphics processing techniques, and (3) automated test generation that translates a human-written rule to a corresponding executable, metamorphic relation and synthesizes meaningful inputs.Our evaluation using three driving models shows that RMT can generate meaningful test cases on which 89% of erroneous predictions are found by enabling higher-order metamorphic relations. Compositionality provides further aids for generating meaningful, synthesized inputs-3012 new images are generated by compositional rules. These detected erroneous predictions are manually examined and confirmed by six human judges as meaningful traffic rule violations. RMT is the first to expand automated testing capability for autonomous vehicles by enabling easy mapping of traffic regulations to executable metamorphic relations and to demonstrate the benefits of expressivity, customization, and pluggability