The elastic polymer composite embedded with carbon nanotubes (CNTs) is an ideal candidate for stretchable and flexible sensor fabrication due to the perfect combination between the excellent properties of CNTs and the high stretchability of the elastomer. A cube model of nanotube/polymer composite is constructed to comprehensively and theoretically analyze its electrical behavior, which is dominantly governed by the CNT network. The aspect ratio and alignment of CNTs significantly influence both the percolation threshold range and the electrical conductivity; however, the electrical conductivity of CNTs has little impact on the percolation threshold. The piezoresistivity of the composite is not only governed by the property of CNTs but also by the mechanical property of the polymer matrix, including the Poisson’s ratio and alignment of CNTs. The specific reasons why the composite resistance rises when it is stretched are investigated. Finally, one optimizing suggestion is given for making the CNTs/polymer composite with high sensitivity.

Cell morphology and relative density (ρrel) are two crucial intrinsic parameters controlling the mechanical properties of metal foams (MFs) and directly depend on their structure (closed/open-cell) and composition (affecting processing parameters). Here, we report on compressive studies of MFs of aluminum (Al) and 7075-T6 alloy processed via a customized route at strain rate, έ = 0.002 and 2.0 s−1. In both sets of MFs, the strength and apparent elastic modulus (E) monotonically increased with ρrel at both έ. At έ = 2.0 s−1, an increase in cell size (Cs) enhanced the strength of both sets of MFs, while at έ = 0.002 s−1, only the alloy foams showed strength increment. The densification strain (εd) of Al foams at έ = 0.002 s−1 monotonically decreased with increasing ρrel, whereas the alloy foams collapsed before the onset of densification. None of the MFs showed any particular trend of εd at έ = 2.0 s−1. The studies conclude that the mechanical properties of MFs with similar morphology, foam parameters, and processing route depend on έ and Cs. Absorption energy (W) and absorption efficiency (Im) of the two sets of MFs were also compared.

Multi-pass warm rolling with falling temperature was proposed and investigated to obtain AZ31 Mg alloy sheets with a fine-grained microstructure. The results indicated that the grain microstructure of AZ31 alloy sheets was successfully refined from 22.1 to 4.5 μm after multi-pass warm rolling with falling temperature and annealing. Compared to the as-received sheet, the multi-pass warm rolled sheets in annealed condition exhibited weaker (0001) basal texture intensity, which resulted in the significantly increased Schmid factor of 〈a〉 basal slip. After multi-pass warm rolling with falling temperature, the rolled sheets in annealed condition also exhibited much better mechanical properties, e.g., higher tensile strength, larger fracture elongation, and higher Erichsen value, especially the IE of 8-pass warm rolled sheet in annealed condition was significantly increased by ∼33% under the same thickness, which could be attributed to the refined grain microstructure and the weakened basal texture.

With the development of high-speed computers, networks, and huge storage, researchers can utilize a large volume and wide variety of materials data generated by experimental facilities and computations. The emergence of these big data and advanced analytical techniques has opened unprecedented opportunities for materials research. The discovery of many kinds of materials, such as energy-harvesting materials, structural materials, catalysts, optoelectronic materials, and magnetic materials, have been greatly accelerated through high-throughput screening. The utility of data-centric science for materials research is likely to grow significantly in the future. Unraveling the complexities inherent in big data could lead to novel design rules as well as new materials and functionalities.

In this study, we have investigated electron field emission (EFE) characteristics of Q-carbon at room temperature and above. At room temperature the Q-carbon requires only ~2.4 V/μm electric field to turn-on the EFE. The EFE properties of the Q-carbon composite structure improve with temperature by lowering the turn-on field and increasing the current density. At 500 K we observed a turn-on field of ~2.34 V/μm, and a maximum current density was found to be ~53 µA/cm2 at 2.66 V/μm. The Q-carbon field emitters also show very stable EFE characteristics (within 7% fluctuations) over time for current intensities between 7.5 and 47 µA/cm2.

In this paper, the melting of stainless steel 316L using Computational Fluid Dynamics to observe the melt pool characteristics is studied. The simulation model allows the observation of the molten pool flow during the selective laser melting process due to Marangoni's effect and recoil pressure. Furthermore, different parameters are tested to show their effects on the melt pool and track formation. Different laser powers, as well as scanning speeds, were used to study the effects they have on the melt pool characteristics. The results were used to determine the relationships between these factors and the melt pool characteristics.

Ongoing, rapid innovations in fields ranging from microelectronics, aerospace, and automotive to defense, energy, and health demand new advanced materials at even greater rates and lower costs. Traditional materials R&D methods offer few paths to achieve both outcomes simultaneously. Materials informatics, while a nascent field, offers such a promise through screening, growing databases of materials for new applications, learning new relationships from existing data resources, and building fast predictive models. We highlight key materials informatics successes from the atomic-scale modeling community, and discuss the ecosystem of open data, software, services, and infrastructure that have led to broad adoption of materials informatics approaches. We then examine emerging opportunities for informatics in materials science and describe an ideal data ecosystem capable of supporting similar widespread adoption of materials informatics, which we believe will enable the faster design of materials.

Data-centric approaches have become increasingly popular in materials science, also known as informational materials science. Nanostructures often play essential roles in materials properties. Nanoinformatics is an important subset of informational materials science and a powerful tool for characterization and design of nanostructures. It allows discovery of meaningful and useful information and patterns from experimental and theoretical data and databases. This article reviews progress in nanoinformatics and informational materials science. Data-centric approaches for materials property description, construction of interatomic potentials, discovery of new inorganic compounds, efficient characterization of ionic transport and interfacial structures, hyperspectral image data analysis, and design of catalytic nanoparticles are outlined.

Improvements in computational resources over the last decade are enabling a new era of computational prediction and design of novel materials. The resulting resources are databases such as the Materials Project (www.materialsproject.org), which is harnessing the power of supercomputing together with state-of-the-art quantum mechanical theory to compute the properties of all known inorganic materials, to design novel materials, and to make the data available for free to the community, together with online analysis and design algorithms. The current release contains data derived from quantum mechanical calculations for more than 70,000 materials and millions of associated materials properties. The software infrastructure carries out thousands of calculations per week, enabling screening and predictions for both novel solids as well as molecular species with targeted properties. As the rapid growth of accessible computed materials properties continues, the next frontier is harnessing that information for automated learning and accelerated discovery. In this article, we highlight some of the emerging and exciting efforts, and successes, as well as current challenges using descriptor-based and machine-learning methods for data-accelerated materials design.

The near-exponential expansion in computing resources over the last few decades has enabled a rapid increase in the capabilities of computational science, including applications to materials research. In order to harness the available resources and accelerate the field of materials design, it is critically important to develop robust and reusable automation software for preparing and performing multistep computational workflows, starting with crystal structures and ending with material properties. In the domain of first-principles calculations of crystalline materials, we highlight emerging tools for automated symmetry analysis of the atomic and electronic structure. With automation capabilities in hand, the ever-increasing amount of data also becomes a serious bottleneck in terms of organization, analysis, and reproducibility. We describe some of the progress and strategic challenges in the development of a general infrastructure for coupling computational automation with data management, emphasizing data reproducibility and provenance capture.