Material characterization is the measurement and determination of a material's physical, chemical, mechanical, and microstructural properties. This technique provides the greater degree of awareness required to handle significant issues such as failure causes and process-related concerns, as well as allowing the manufacturer to make critical material decisions. The complexity of materials and devices is increasing. As a result, the methodologies and procedures utilized to investigate and characterize them must become increasingly sophisticated. To support technical endeavours, materials scientists use both standardized analytical procedures and specialized application-specific advanced techniques.
Material modelling thus faces the difficulty of high-dimensional parameter spaces, where a large number of parameter combinations must be sampled and thoroughly examined. Given the generally high-dimensional parameter space of interest, relying on experiments is typically prohibitively expensive. As a result, the combination of experimental and computational methodologies is gaining popularity. Due to recent advancements in computing power and simulation methodology, computational modelling techniques are increasingly widely used in materials research, as they can enable rapid testing of theoretical predictions or understanding of complex experimental data at a low cost.
Human needs and desires have always driven material growth, and this is expected to continue in the foreseeable future. By 2050, the world's population is predicted to reach 10 billion people, resulting in increased need for clean and efficient energy, customised consumer products, reliable food supply, and professional healthcare. The key to overcoming this difficulty will be the development of new functional materials that are created and tuned for specific qualities or behaviours. Advanced materials are typically discovered empirically or through experimental trial-and-error methods. Data-driven or machine learning (ML) technologies have created new possibilities for the discovery and rational design of materials as massive data generated by modern experimental and computational techniques becomes more widely available.
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Title : Failure oriented accelerated testing in electronics and photonics materials engineering: Types, roles, attributes, modeling
Ephraim Suhir, Portland State University, United States
Title : Dynamic explicit analysis of composite shear walls strengthened with fiber reinforced polymer (FRP) and subjected to blast load
Mahdi Hosseini, Nanjing Forestry University, China
Title : Introducing picotechnology: An exciting extension of nanotechnology
Thomas J Webster, Interstellar Therapeutics, United States
Title : Color control of electrochromes by structural modification
Will Skene, Montreal University, Canada
Title : From pixels to properties: Material classification via machine learning
K L Vasundhara, Stanley College of Engineering and Technology for Women, India
Title : The failure of both einsteins space-time theory and his equivalence principle and their resolution by the uniform scaling Method
Robert Buenker, University of Wuppertal, Germany
Title : Smart biomaterials in healthcare industry
Kilari Jyothsna Devi, PVPSIT, India
Title : Effect of impact load on posttension slabs reinforced with fiber reinforced polymers (RFP), using numerical analysis
Mohammad El Ilani, Beirut Arab University, Lebanon
Title : ZnO and Fe-doped ZnO nanoparticles: Their antibacterial and photocatalytic activities
Wahidur Raza, University of Chittagong, Bangladesh
Title : Nickel ferrite nanoparticles: Chemical synthesis and photocatalytic efficiency for degradation of organic pollutants
Samiya Fariha, University of Chittagong, Bangladesh