Direct observation of phase transitions in truncated tetrahedral microparticles under quasi-2D confinement.

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.

Direct Observation of the Pressure-Induced Structural Variation in Gold Nanoclusters and the Correlated Optical Response.

The ability to gradually modify the atomic structures of nanomaterials and directly identify such structural variation is important in nanoscience research. Here, we present the first example of a high-pressure single-crystal X-ray diffraction analysis of atomically precise metal nanoclusters. The pressure-dependent, subangstrom structural evolution of an ultrasmall gold nanoparticle, AuS, has been directly identified.

Mechanical nanolattices printed using nanocluster-based photoresists.

Natural materials exhibit emergent mechanical properties as a result of their nanoarchitected, nanocomposite structures with optimized hierarchy, anisotropy, and nanoporosity. Fabrication of such complex systems is currently challenging because high-quality three-dimensional (3D) nanoprinting is mostly limited to simple, homogeneous materials. We report a strategy for the rapid nanoprinting of complex structural nanocomposites using metal nanoclusters.

Machine learning analysis of self-assembled colloidal cones.

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.