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Colloidal crystals are used to understand fundamentals of atomic rearrangements in condensed matter and build complex metamaterials with unique functionalities. Simulations predict a multitude of self-assembled crystal structures from anisotropic colloids, but these shapes have been challenging to fabricate. Here, we use two-photon lithography to fabricate Archimedean truncated tetrahedrons and self-assemble them under quasi-2D confinement. These particles self-assemble into a hexagonal phase under an in-plane gravitational potential. Under additional gravitational potential, the hexagonal phase transitions into a quasi-diamond two-unit basis. In-situ imaging reveal this phase transition is initiated by an out-of-plane rotation of a particle at a crystalline defect and causes a chain reaction of neighboring particle rotations. Our results provide a framework of studying different structures from hard-particle self-assembly and demonstrates the ability to use confinement to induce unusual phases.

 

Nanocluster-based photoresists enable 3D printing of polymer nanocomposites with enhanced mechanical strength and stability. 

Optical and confocal microscopy is used to image the self-assembly of microscale colloidal particles. The density and size of self-assembled structures is typically quantified by hand, but this is extremely tedious. Here, we investigate whether machine learning can be used to improve the speed and accuracy of identification. This method is applied to confocal images of dense arrays of two-photon lithographed colloidal cones. RetinaNet, a deep learning implementation that uses a convolutional neural network, is used to identify self-assembled stacks of cones. Synthetic data is generated using Blender to supplement experimental training data for the machine learning model. This synthetic data captures key characteristics of confocal images, including slicing in the z-direction and Gaussian noise. We find that the best performance is achieved with a model trained on a mixture of synthetic data and experimental data. This model achieves a mean Average Precision (mAP) of ∼85%, and accurately measures the degree of assembly and distribution of self-assembled stack sizes for different cone diameters. Minor discrepancies between machine learning and hand labeled data is discussed in terms of the quality of synthetic data, and differences in cones of different sizes. 

Polymeric particles with complex shapes are required for biomedical therapies, colloidal self‐assembly, and micro‐robotics. It has been challenging to synthesize particles beyond simple shapes (e.g., spheres, cubes) with high structural accuracy using existing methods. Here, a method for fabricating polymeric microparticles of complex 3D shapes is reported using two‐photon lithography, and dispersing the particles in an aqueous solution on a glass substrate. The fabrication of polyhedrons (e.g., tetrahedron, pyramid), polypods (e.g., tetrapod, hexapod), and other shapes of 5–10 µm in size is demonstrated. Confocal microscopy is used to track the motion of the sphere, tetrahedron, tetrapod, and screw‐shaped particles near the substrate, and determine their translational diffusion coefficients. HYDRO++ is used to simulate the motion of the particles far from the substrate. The influence of particle size and substrate effects on diffusion in the spherical particles is determined and finds that the non‐spherical particles have increased hindrance at the substrate compared to the spherical particles.