This paper studies the response of a radiation sensor that was developed from single-walled carbon nanotube (SWCNT) and poly (methyl methacrylate) (PMMA) nanocomposite thin films placed on an interdigitated electrode (IDE). We studied the effects of X-rays in real-time electrical resistance of the thin film nanocomposites. A 160 kV X-ray source irradiated the nanocomposite devices with ionizing X-ray radiation at different doses and dose rates, causing the decrease in resistance of thin film through the production of charge carriers. The embedded SWCNT network helped transport radiation-induced electrons to the electrodes. Pre-irradiation resistance can be obtained rapidly from heat treatment of irradiated nanocomposite devices. It is observed that the required heat treatment time for thin film device recovery increased almost linearly with applied radiation dose. The thin film devices were operated at dc voltage as low as 0.05 V for reliable measurement of the radiation-induced change in electrical resistance. The results of this study suggest that SWCNT/PMMA thin film devices could be used for sensing Xray radiation, with significantly higher sensitivity than comparable devices made with multi-walled CNT (MWCNT) based nanocomposites in a previous study.
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Artificial intelligence (AI) is changing the way we discover new materials. The emergence of AI brings a new dawn to the development of material science. It enables material researchers to harness the power of machine learning and artificial intelligence to develop a system that autonomously discovers new materials. AI is also helping materials scientists and engineers to revolutionize the way of understanding and discovering materials used in applications ranging from aerospace engineering to soft robotic prosthetics. This paper provides an introduction to the uses of AI in materials science.
Metal-oxide composites are commonly used in high temperature environments for their thermal stability and high melting points. Commonly employed with refractory oxides or carbides such as ZrC and HfC, these materials may be improved with the use of a low density, high melting point ceramic such as CeO2. In this work, the consolidation of W-CeO2 metal matrix composites in the high CeO2 concentration regime is explored. The CeO2 concentrations of 50, 33, and 25 wt.%, the CeO2 particle size from nanometer to micrometer, and various hot isostatic pressing temperatures are investigated. Decreasing the CeO2 concentration is observed to increase the composite density and increase the Vickers hardness. The CeO2 oxidation state is observed to be a combination of Ce3+ and Ce4+, which is hypothesized to contribute to the porosity of the composites. The hardness of the metal-oxide composite can be improved more than 2.5 times compared to pure W processed by the same route. This work offers processing guidelines for further consolation of oxide-doped W composites.
Deep learning is presently receiving a lot of attention. It is a subset of machine learning, based on multi-layer neural networks or deep neural networks. It is a novel, data-hungry, and high-accuracy analytics approach. This paper discusses deep learning algorithms and their applications in manufacturing.
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