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Large silicon nitride (Si3N4) grain can enhance thermal conductivity and fracture toughness in the Si3N4 ceramics. In this study, in order to use pores as a grain growth trigger and to achieve final densification, a limited amount of pores were introduced into the green compact. The pores were introduced by using four types of pore formers with diameters of 2, 20, 30, and 100 µm, and the pore volume was in the range of 3–18 vol%. The presence of pores promoted grain growth regardless of their diameter and volume, because no collisions occurred among the grains in the pores, and larger pores formed larger elongated grains. However, their large volume and diameter inhibited densification. Based on this tradeoff, the pore formers with a diameter of 30 µm exhibited superior properties, and fracture toughness and thermal conductivity increased to 10.2 MPa·m1/2 and 118.2 W/(m·K), respectively, as the pore volume increased to 5 vol%. These results revealed that the pores in the compacts before sintering could improve the thermal conductivity and fracture toughness if they promoted grain growth during the sintering procedure and finally disappeared. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) models such as a convolutional neural network (CNN) are powerful tools for predicting the properties of materials from their microstructural images, etc. It is, however, critically essential to understand how the AI models use images and information to predict the target properties. In this study, we tried to gain insight into the inner workings of two AI models trained to predict bending strength (BS) and thermal conductivity (TC) of silicon nitride ceramics. Focusing on the intermediate feature representation of the microstructural images in the networks, the high‐dimensional data points corresponding to sample images were mapped onto a two‐dimensional plane using t‐distributed stochastic neighbor embedding (t‐SNE). The maps demonstrated that the AI models predicted BS and TC primarily based on the porosity and grain sizes of the samples. The result indicates that t‐SNE is a useful technique for making the basis of models' predictions more understandable and well founded. [ABSTRACT FROM AUTHOR]

We prepared sintered reaction‐bonded silicon nitride (Si3N4) ceramics using various amounts of yttria (Y2O3) and a fixed amount of magnesia as sintering additives and evaluated their effects on the microstructure and dielectric breakdown strength (DBS). The DBS tests were conducted at substrate thicknesses of approximately.3,.2,.1, and.05 mm for each sample. All the prepared samples exhibited a high relative density (> 98%) and were composed of grains of almost identical sizes and shapes. The DBS decreased as the amount of added Y2O3 increased, with the degree of decrease being more pronounced for the thinner substrates. In the case of substrates with the largest Y2O3 amount (5 mol%), the DBS decreased to the level observed for the thinnest substrate of.05 mm. The presence of yttrium ions around the grains increased the electrical conductivity of the glassy phase, resulting in a decreased DBS. Therefore, a smaller amount of Y2O3 led to a higher DBS. The observed decrease in the DBS (.05 mm) was attributed to the connection between the top and bottom surfaces of the substrate through a highly conductive path. [ABSTRACT FROM AUTHOR]

Eelgrass meadows have attracted much attention not only for their ability to maintain marine ecosystems as feeding grounds for marine organisms but also for their potential to store atmospheric and dissolved CO2 as blue carbon. This study comprehensively evaluated the bacterial and chemical data obtained from eelgrass sediments of different scales along the Japanese coast to investigate the effect on the acclimatization of eelgrass. Regardless of the eelgrass habitat, approximately 1% Anaerolineales, Babeliales, Cytophagales, and Phycisphaerales was present in the bottom sediment. Sulfate-reducing bacteria (SRB) were present at 3.69% in eelgrass sediment compared to 1.70% in bare sediment. Sulfur-oxidizing bacteria (SOB) were present at 2.81% and 1.10% in the eelgrass and bare sediment, respectively. Bacterial composition analysis and linear discriminant analysis revealed that SOB detoxified H2S in the eelgrass meadows and that the larger-scale eelgrass meadows had a higher diversity of SOB. Our result indicated that there were regional differences in the system that detoxifies H2S in eelgrass meadows, either microbial oxidation mediated by SOB or O2 permeation via the physical diffusion of benthos. However, since bacterial flora and phylogenetic analyses cannot show bias and/or causality due to PCR, future kinetic studies on microbial metabolism are expected. [ABSTRACT FROM AUTHOR]