Discover Materials最新文献

Buried charges such as improvised explosive devices continue to be one of the most lethal and hidden threats service members face. On detonation, ground debris near the blast area is accelerated towards service members as secondary fragmentation, consisting of sand, gravel and rocks. In order to mitigate injury, protective equipment can be worn, yet it is difficult to gather accurate data for engineering decisions when the standard test uses a fragment simulating projectile made from metal. It is difficult to test secondary fragmentation from ground debris due to the natural heterogeneity and variance of the material. A methodical and reproducible method of testing fragmentation damage from ground debris was developed to study and improve protective equipment against natural secondary fragmentation. We present herein the novel process of 3D-printing ballistic projectiles from silica sand, followed by launching with an air canon. Outlined within are the successes, challenges and proposed implementations of the technology. The 3D-printed sand projectiles achieved speeds over 170 m/s, resulting in measurable damage to single Kevlar sheets. Other flight parameters such as yaw and rotation were captured, resulting in observations about design and shape of the projectiles. It was found that one design performed better in terms of velocity, rotation and impact. The technology has the potential to disrupt the protective equipment sector by providing a controlled means of assessing natural fragmentation damage.

Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.

Throughout human history, most further developments or new achievements were accompanied by new materials or new processes that enabled the technologic progress. With concrete devices and applications in mind, synthesis and subsequent treatment of materials naturally went along with the progress. The aim of the underlying article is to spot the role of optimization, of discovery, of trial-and-error approaches, of fundamentals and curiosity driven design and development. In a consecutive examination, five missions addressing the challenges facing our world (identified by the European Council) will be cross linked with seven topical areas from materials science defined by the European Materials Research Society. The scope of this examination is to identify approaches and methods to further develop and innovate materials which form the basis of the anticipated solutions.