Advanced Materials Science and Technology (ISSN: 2717-526X) is a peer-reviewed open access journal published semi-annual online by Omniscient Pte. Ltd. The journal covers the properties, applications and synthesis of new materials related to energy, environment, physics, chemistry, engineering, biology and medicine, including ceramics, polymers, biological, medical and composite materials and so on. Original article, Review, Report and Communication are encouraged. Advanced Materials Science and Technology aims to disseminate the latest progress in advanced materials such as nanomaterials, carbon-based materials, organic optoelectronic materials, metallic materials and functional materials and to promote the understanding of the use of materials in energy, environment, physics, chemistry, engineering, biology and medicine. This journal will be useful for professionals in the various branches of materials science and for students and academic staff concerned with the related specialties.

  • Synthesis, Electrical and Dielectric Properties of Ca-doped SrTiO3 Ceramics

    Ali Mater, Massoud Kahlaoui, Abdewaheb Inoubli, Chaabane Chefi

    CaxSr1-xTiO3 (CxST;  x=0.2 and 0.4) powders were elaborated by conventional solid state reaction. Structural and electrical properties were influenced by the Ca2+ doping content. Structural and microstructures properties of the obtained powders were conducted by X-ray diffraction and scanning electron microscopy. The powder were pressed to eight tons into cylindrical pellets and sintered at 1300°C for 3h. The electrical and dielectric properties were measured by two-probe method using HP 4192 Analyzer. The refinement of results showed that the samples were crystallized in the tetragonal with I4/mcm space group. The highest conductivity was shown for C0.2ST sample which equal to 1.21 x 10-4 S.cm-1 at 600 °C. The electrical and dielectric parameters were plotted and discussed as a function of temperature and frequency. These Ca doped perovskite ceramics  materials can be used as possible candidate for a wide range of applications in various devices.

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  • Design and Selection of Material for Plastic Gears

    Naveen Kumar, Manish Kumar Singh, Ajeet Kumar

    The use of plastic and polymer composite gears is increasing because of their low cost, lightweight and quiet operation compared to metal gears. Plastic gears find application in printers, cameras, timers, counters, etc. Four different combinations of materials for pinion and gear were selected in the present work, and a comparative study was done to investigate mechanical and thermal properties analytically. Analytical results were validated using SOLIDWORKS and ANSYS. Design 1 and Design 2 were found out to be preferable designs. Maximum principal and maximum shear stress generated were minimum for Design 1 (pinion of Polycarbonate and gear of Acetal copolymer). Simultaneously, the deformation and temperature rise were minimum for Design 2 (pinion of Nylon 66 and gear of Acetal copolymer).

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  • Nano-structured Ni-based Active Species Supported on Metallic Substrates as the Efficient Catalysts for Hydrogen Generation from Methane with Water

    Linsheng Wang

    Nanostructured Ni metal and Ni-Re alloy active species supported on the surface of the total metallic Ni honeycomb substrate and the porous Ni metal substrate are studied as the novel catalyst system for hydrogen generation from steam methane reforming. The bimetallic Ni-Re nano-alloy active species on the surface of the metallic Ni honeycomb substrate or porous Ni metal substrate exhibit the remarkably enhanced activity for steam methane reforming to generate hydrogen especially at lower reaction temperatures. The total metallic catalysts with excellent electric and heat conductivity and almost zero bed pressure loss are expected to be used for the innovation industrial processes.

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  • A Journey into the Determination of Polyaniline Molecular Weight

    Claudia L. Bianchi, Ridha Djellabi, Ermelinda Falletta

    Over the last decades, the construction and design of conducting polymer-based materials have received a huge attention due to their special physico-chemical characteristics making them very useful and alternative materials in various fields. Among intrinsically conducting polymers, polyaniline (PANI) has been widely used and a pool of scientific research has been done to improve its properties and/or to combine it with other materials to get multifunction materials with enhanced efficiency. However, the scarce information is available to date on its molecular weight (Mw) limits obtaining structural information. In fact, owing to the poor solubility of the material the most part of the results concerns dimeric and oligomeric species, whereas a complete characterization of the longest chains is still far away.

    Since the synthetic strategies used to produce PANI critically affect its properties and chemical-physical characteristics, included Mw, the present work summarizes the most common methods applied for PANI synthesis and the different analytical approaches used to determine the Mw of the polymer and of its oligomers, in order to stimulate further investigation that can contribute to widen the information on this unique polymer.

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  • Machine Learning for Next-generation Printed Technologies

    Litty Varghese Thekkekara, Shamini P. Baby, Jeffery Chan, Ivan Cole

    Modern science advances towards the development of lightweight wearable and portable applications for the promotion of human-machine interfaces. Among them, the most beneficial ones include the technologies for healthcare, telecommunications, and energy resources. Recent developments in the additive manufacturing otherwise 3D printing sector are promising for largescale applications. It promotes cost-effective production of technologies like sensors, lab on chips, solar cells, and energy storage. However, these applications' efficiency is lower to technologies fabricated using other methods like chemical approaches due to the non-optimized parameters involved in the fabrication and characterization phases. Machine learning on the other hand expands its science and engineering capabilities. It has a broader opportunity to support 3D printing to develop the potentials and efficiency through effective prediction methods for printing methods and design aspects. In this review, we discuss the use of machine learning prediction algorithms for technologies using 3D printing.

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