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Natural calcium phosphates derived from fish wastes are a promising material for biomedical application. However, their sintered ceramics are not fully characterized in terms of mechanical and biological properties. In this study, natural calcium phosphate was synthesized through a thermal calcination process from salmon fish bone wastes. The salmon-derived calcium phosphates (sCaP) were sintered at different temperatures to obtain natural calcium phosphate bioceramics and then were investigated in terms of their microstructure, mechanical properties and biocompatibility. In particular, this work is concerned with the effects of grain size on the relative density and microhardness of the sCaP bioceramics. Ca/P ratio of the sintered sCaP ranged from 1.73 to 1.52 when the sintering temperature was raised from 1000 to 1300 °C. The crystal phase of all the sCaP bioceramics obtained was biphasic and composed of hydroxyapatite (HA) and tricalcium phosphate (TCP). The density and microhardness of the sCaP bioceramics increased in the temperature interval 1000–1100 °C, while at temperatures higher than 1100 °C, these properties were not significantly altered. The highest compressive strength of 116 MPa was recorded for the samples sintered at 1100 °C. In vitro biocompatibility was also examined in the behavior of osteosarcoma (Saos-2) cells, indicating that the sCaP bioceramics had no cytotoxicity effect. Salmon-derived biphasic calcium phosphates (BCP) have the potential to contribute to the development of bone substituted materials.

The main inconvenience in design process of modern high performance reflectarray antennas is that these designs are heavily depended on full-wave electromagnetic simulation tools, where in most of the cases the design optimization process would be an inefficient or impractical. However, thanks to the recent advances in computer-aided design and advanced hardware systems, artificial neural networks based modeling of microwave systems has become a popular research topic. Herein, design optimization of an alumina-based ceramic substrate reflectarray antenna by using multilayer perceptron (MLP) and 3D printing technology had been presented. MLP-based model of ceramic reflectarray (CRA) unit element is used as a fast, accurate, and reliable surrogated model for the prediction of reflection phase of the incoming EM wave on the CRA unit cell with respect to the variation of unit elements design parameters, operation frequency, and substrate thickness. The structural design of a reflectarray antenna with nonuniform reflector height operating in X band has been fabricated for the experimental measurement of reflectarray performance using 3D printer technology. The horn feeding based CRA antenna has a measured gain characteristic of 22 dBi. The performance of the prototyped CRA antenna is compared with the counterpart reflectarray antenna designs in the literature. K E Y W O R D S 3D printer, artificial neural network, ceramic, reflectarray, surrogate-based modeling 1 | INTRODUCTION Alumina (Al 2 O 3) is a commonly used engineering ceramic material with high melting temperature, good strength, and hardness obtained via aluminum oxidation. Alumina is a ceramic based material in which the amount of sodium oxide (Na 2 O) inside is a decisive parameter for the engineering applications. For microwave applications where the RF durable substrates with high dielectric constant are required to design electrically small microwave circuits, the amount of Na 2 O should be less than 0.1% while in mechanical applications, the amount of Na 2 O can be in the range of 0.5%. 1,2 Alumina has been alternatively named as Korund (a-Al 2 O 3), which is one of the hardest structures after the diamond and some synthetic diamond formed structures used in industrial applications is a-Al 2 O 3. Although pure alumina can be found in many crystal forms, all these forms depending on time, crystal size, and atmospheric conditions can be transformed into a-Al 2 O 3 at the temperature of 750 C-1200 C. It is possible to speed up the transformation process by

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Herein, by using 3D printing technology and data‐driven surrogate model‐assisted optimization method, design of a ceramic material‐based nonuniform nonplanar microstrip filter is taken into the consideration in a computationally efficient and low‐cost manner. For this aim, 3D EM model of the proposed design had been used for generating training and validation data sets. Then commonly used state‐of‐the‐art regression algorithms had been used for creating accurate and fast surrogate models to create a mapping between inputs of the model and outputs of the unit element design. After that, Grey Wolf Optimization algorithm had been used for design optimization of a bandpass filter. Then, via the use of 3D printer, the optimally designed filter had been prototyped and its performance characteristics are measured. As a result, by using 3D printer technology, ceramic material, and the proposed method, design optimization of nonuniform nonplanar microstrip filter can be achieved in a computationally efficient way.