ABSTRACT
Morphology plays a crucial role in defining the optical, electronic, and mechanical properties of halide perovskite microcrystals. Therefore, developing strategies that offer precise control over crystal morphology during the growth process is highly desirable. This work presents a simple scheme to simultaneously grow distinct geometries of cesium lead bromide (CsPbBr3) microcrystals, including microrods (MR), microplates (MP), and microspheres (MS), in a single chemical vapor deposition (CVD) experiment. By strategically adjusting precursor evaporation temperatures, flux density, and the substrate temperature, we surpass previous techniques by achieving simultaneous yet selective growth of multiple CsPbBr3 geometries at distinct positions on the same substrate. This fine growth control is attributed to the synergistic variation in fluid flow dynamics, precursor substrate distance, and temperature across the substrate, offering regions suitable for the growth of different morphologies. Pertinently, perovskite MR are grown at the top, while MP and MS are observed at the center and bottom regions of the substrate, respectively. Structural analysis reveals high crystallinity and an orthorhombic phase of the as-grown perovskite microcrystals, while persistent photonic lasing manifests their nonlinear optical characteristics, underpinning their potential application for next-generation photonic and optoelectronic devices.
ABSTRACT
Two-phase flow separation is a key step in various downstream purification processes. The use of a separator with controllable flow behavior is recommended to avoid contamination. In this study, a core-annular separator for biphasic flow separation with four different chemical polarities was developed, and two machine learning-based methods were proposed for answering two emergent questions to meet real industrial needs. (1) Could complete two-phase separation be achieved under these operating conditions? (2) Could the separation process be accelerated by determining the maximum input flow rate of the water? Process prediction for automation, machine learning-based classifiers, and multilayer perceptron were used to address these questions by predicting successful separation and the maximum input flow rates of unknown water-solvent systems with limited experimental data as training samples. The core-annular separator achieved complete two-phase water-solvent separation at a maximum total input flow rate of 4000 µL min-1. Moreover, the classification accuracy for complete separation reached 92.2%, and the multilayer perceptron network had the best performance for predicting the flow rate. This liquid-liquid interface manipulation separator and machine learning method could decrease the cost of relevant process development.