Spark plasma sintering of dielectric BaTaO2N close to the melting point of the BaCN2 additive

The perovskite-type oxynitride BaTaO2N is a promising lead-free relaxor ferroelectric material. However, this material releases a part of its nitrogen during its sintering above 1350 degrees C. Subsequent annealing of the resulting sintered BaTaO2N0.85 ceramics under an ammonia flow is necessary to recover an insulating, stoichiometric BaTaO2N ceramics. Very recently, molten BaCN2 was reported to be a useful flux for the preparation of BaTaO2N microcrystals without the loss of nitrogen. In the present work, BaTaO2N powder was sintered with a BaCN2 additive under mechanical pressure, using a spark plasma sintering system. Nitrogen loss from the BaTaO2N ceramic was avoided during liquid phase sintering at approximately 900 degrees C. The stoichiometric BaTaO2N ceramic product with a relative density of 79.8 % was an electrically insulator and it showed its relative dielectric constants, epsilon(r) in the range from 320 to 650 and a loss tans values from 0.04 to 0.19 at room temperature

High-density excitation effect on photoluminescence in ZnO nanoparticles

In this study, photoluminescence PL under high excitation intensity as a function of crystalline size was systematically investigated through ZnO nanocrystalline films prepared by spin-coating a colloidal solution of ZnO nanoparticles obtained using the microemulsion method. Annealing of the films at 723, 633, and 593 K allowed us to tune the crystalline radius R. PL studies distinguished different regimes of crystalline size according to the ratio of R to the effective Bohr radius aB R/aB. For the sample annealed at 723 K R/aB=7.2 , the peak of stimulated emission due to the exciton-exciton collisions appeared on the low-energy side of the exciton emission with an increase in excitation intensity. A further increase in excitation intensity eventually resulted in the occurrence of an electron-hole plasma EHP accompanied by consequent band gap renormalization, which indicates that high excitation intensity provokes the dissociation of excitons. For the sample annealed at 633 K R/aB=4.7 , the stimulated emission was observed while the transition to EHP was obscure. For the sample annealed at 593 K R/aB=2.1 , only emissions due to the recombination of the electron-hole pair were observed, and stimulated emission did not appear even when the excitation intensity was increased. The transition from free-exciton emission to donor-bound exciton emission was observed in temperature dependence of PL only for the sample with R/aB=7.2. The origin of annihilation of the stimulated emission with a size reduction is discussed based on nonradiative Auger recombination

Collapsed Building Detection Using 3D Point Clouds and Deep Learning

Collapsed buildings should be detected with the highest priority during earthquake emergency response, due to the associated fatality rates. Although deep learning-based damage detection using vertical aerial images can achieve high performance, as depth information cannot be obtained, it is difficult to detect collapsed buildings when their roofs are not heavily damaged. Airborne LiDAR can efficiently obtain the 3D geometries of buildings (in the form of point clouds) and thus has greater potential to detect various collapsed buildings. However, there have been few previous studies on deep learning-based damage detection using point cloud data, due to a lack of large-scale datasets. Therefore, in this paper, we aim to develop a dataset tailored to point cloud-based building damage detection, in order to investigate the potential of point cloud data in collapsed building detection. Two types of building data are created: building roof and building patch, which contains the building and its surroundings. Comprehensive experiments are conducted under various data availability scenarios (pre–post-building patch, post-building roof, and post-building patch) with varying reference data. The pre–post scenario tries to detect damage using pre-event and post-event data, whereas post-building patch and roof only use post-event data. Damage detection is implemented using both basic and modern 3D point cloud-based deep learning algorithms. To adapt a single-input network, which can only accept one building’s data for a prediction, to the pre–post (double-input) scenario, a general extension framework is proposed. Moreover, a simple visual explanation method is proposed, in order to conduct sensitivity analyses for validating the reliability of model decisions under the post-only scenario. Finally, the generalization ability of the proposed approach is tested using buildings with different architectural styles acquired by a distinct sensor. The results show that point cloud-based methods can achieve high accuracy and are robust under training data reduction. The sensitivity analysis reveals that the trained models are able to locate roof deformations precisely, but have difficulty recognizing global damage, such as that relating to the roof inclination. Additionally, it is revealed that the model decisions are overly dependent on debris-like objects when surroundings information is available, which leads to misclassifications. By training on the developed dataset, the model can achieve moderate accuracy on another dataset with different architectural styles without additional training