Diamond lattices formed by atomic or colloidal elements exhibit remarkable functional properties. However, building such structures via self-assembly has proven to be challenging due to the low packing fraction, sensitivity to bond orientation, and local heterogeneity. We report a strategy for creating a diamond superlattice of nano-objects via self-assembly, and demonstrate its experimental realization by assembling two variant diamond lattices, one with and one without atomic analogs. Our approach relies on the association between anisotropic particles with well-defined tetravalent binding topology and isotropic particles. The constrained packing of triangular binding footprints of truncated tetrahedra on a sphere defines a unique three-dimensional lattice. Hence, the diamond self-assembly problem is solved via its mapping onto two-dimensional triangular packing on the surface of isotropic spherical particles.

We demonstrate that a statistically-significant chiral bias in NaClO 3 chiral crystallization can be provoked by inducing nucleation via the optical trapping of Ag nano-aggregates using a continuous wave visible circularly polarized laser (λ = 532 nm). The laser was focused at the interface between air and an unsaturated NaClO 3 aqueous solution containing Ag nanoparticles. The "dominant" enantiomorph was switchable by changing the handedness of the incident circularly polarized laser, indicating that the chiral bias is enantioselective. Moreover, it has been found that the resulting crystal enantiomeric excess (CEE) reached approximately 25%. The CEE is much higher than the typical enantiomeric excess (EE) in the asymmetric photosynthesis of organic compounds ranging from 0.5 to 2%. The efficient induction of the nucleation and the large chiral bias imply the contribution of localized surface plasmon resonance of the Ag nanoaggregates to chiral nucleation. Our method has potential to offer the benefit for studies on the spatiotemporal nucleation control, optical resolution of chiral compounds and biohomochirality.

The control of step bunching by solution flow in 4H-SiC solution growth is proposed. We achieved the solution flow control with the specially designed top-seeded solution growth method as follows: by deviating a seed crystal from the center of a crucible and rotating the crucible in one direction, the solution flow direction was controlled to be parallel or antiparallel to the step-flow direction. After the growth, the widely spaced, accumulated macrosteps were observed and the surface of the grown crystal became rough under the parallel flow. On the other hand, the development of the macrosteps was suppressed under the antiparallel flow. As the growth proceeds, the surface roughness of the growth surface increases under the parallel flow, while the surface roughness decreases under the antiparallel flow. This fact suggests the solution flow control can be an effective method to suppress the step bunching during the solution growth of SiC single crystals.

We succeeded in measuring the density and direction of the edge component of threading dislocations (TDs) in c-plane (0001) GaN by micro-Raman spectroscopy mapping. In the micro-Raman spectroscopy mapping of the E2H peak shift between 567.85 and 567.75 cm−1, six different contrast images are observed toward directions of . By comparing X-ray topography and etch pit images, the E2H peak shift is observed where the edge component of TDs exists. In contrast, the E2H peak is not observed where the screw component of TDs exists.

Evolution of threading screw dislocation (TSD) conversion during the solution growth of 4H-SiC on a vicinal crystal of 4H-SiC(0001) was investigated by synchrotron X-ray topography. Selecting appropriate X-ray wavelength and g vector, we can change the penetration of X-ray, and the dislocation behaviors with the different depth were successfully observed. Evidently TSDs parallel to the c-axis having c-component Burgers vector were changed into defects on the (0001) basal planes with the same Burgers vector as the TSDs, propagating to the [112¯0] step-flow direction by advancing macrosteps during the solution growth. The TSD conversions stochastically took place during the growth. The conversion rate was almost uniform and finally almost all TSDs were converted to the basal plane defects. The conversion rate was low at the very early stage of the growth, which implies that the macrosteps were not formed at the initial stage of the solution growth

We report a marked reduction in the dislocation density of a 4H-SiC crystal using a high-efficiency dislocation conversion phenomenon. During the solution growth, threading dislocations were efficiently converted to basal plane defects by the step flow of macrosteps. Utilizing this dislocation conversion phenomenon, we achieved the marked reduction of threading dislocation density. Consequently, the threading screw dislocation density was only 30 cm−2, which was two orders of magnitude lower than that of the seed crystal. The 4H-SiC polytype of the seed crystal was replicated in the grown crystal, which was attributed to the spiral growth owing to a few remaining threading screw dislocations upstream of the step flow.

The atomic structure of TiC͑100͒ was determined precisely by low-energy electron diffraction intensity analysis. The surface atomic structure is relaxed; the topmost C atoms are displaced outward and Ti atoms inward with respect to the truncated bulk atomic positions. The results agree qualitatively with earlier theoretical work ͓D. L. Price, J. M. Wills, and B. R. Cooper, Phys. Rev. Lett. 77, 3375 ͑1996͔͒ and quantitatively with the latest theoretical results based on the first-principles molecular dynamics method ͓K. Kobayashi, Jpn.

Accelerating the optimization of material processing is essential for rapid prototyping of advanced materials to achieve practical applications. High-quality and large-diameter semiconductor crystals improve the performance, reliability and cost efficiency of semiconductor devices. However, much time is required to optimize the growth conditions and obtain a superior semiconductor crystal. Here, we demonstrate a rapid prediction of the results of computational fluid dynamics (CFD) simulations for SiC solution growth using a neural network for optimization of the growth conditions. The prediction speed was 10 7 times faster than that of a single CFD simulation. The combination of the CFD simulation and machine learning thus makes it possible to determine optimized parameters for high-quality and large-diameter crystals. Such a simulation is therefore expected to become the technology employed for the design and control of crystal growth processes. The method proposed in this study will also be useful for simulations of other processes.6546 | CrystEngComm, 2018, 20, 6546-6550This journal is