MRS Communications最新文献

By applying an aluminum coating to the copper-nickel alloy resistor, we successfully utilized air sintering. At 800 and 850°C, the alloy was sintered within an aluminum layer. This coating of aluminum serves as a protective layer. The copper-nickel was not oxidized as a result. After wet etching, the protective coating of aluminum was removed, leaving alloy resistors with Temperature Coefficient of Resistance (TCR) values of 417.86 ppm/°C. We produced an alloy paste using copper-nickel alloy (7:3) and then used screen printing to create thick-film alloy resistors. Therefore, it is feasible to create copper-nickel alloy resistors by air sintering.

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Additive manufacturing (AM) has many advantages over conventional subtractive manufacturing methods. The cost-effective AM allows for precise fabrication of complex structures with less material waste, making it a popular manufacturing process in many applications. The recent development of AM materials has advanced significantly. By precisely controlling material distribution and microstructural features, AM facilitates the creation of new composites with specific requirements. AM techniques have contributed considerably to integrating unique functionalities for various applications. This review emphasizes multiple categories of materials, including metal alloys, polymer-based composites, and sustainable composites, as well as applications of sensing materials and strategies and emerging artificial intelligence and machine learning.

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Methacrylation of Pluronics enables 3D printing of irreversible hydrogel constructs, thereby diversifying their potential applications. Current methods rely on organic solvents and time-consuming reactions. Herein, a rapid method for the microwave-assisted methacrylation of Pluronics by methacrylic anhydride (MA) in the absence of organic solvents is presented, which reduced the reaction time from 24 h to ca. 10 min. ¹H-NMR analysis showed that this method allows modulating the methacrylation degree (MD) of Pluronics from 45 to 97% by using different MA:Pluronic ratios. 3D-printed constructs with lower MD showed larger pores and lower compression modulus.

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This research investigates the effect of silicon (Si) addition on the phase transformation behaviour of the Ni–Ti alloy. The Ni–Ti–Si alloys were fabricated with varying proportions of Si through a modified vacuum induction melting technique with argon backfilling. Scanning electron microscopy, X-ray diffraction and differential scanning calorimetry were done to analyse the microstructure, elemental composition, and phase transformation temperatures, respectively, to validate the effectiveness of this modified melting technique. The results indicate an increase in the phase transformation temperature with an increase in the Si content in the alloy mixture and the alloys were prepared with a very minimal amount of detrimental impurities. This technique helps to produce high-temperature shape memory alloys with utmost purity which helps in retaining their functional properties. The transformation temperatures observed from differential scanning calorimetry revealed that the A_f temperature varies from 342.68 K to 379.95 K for 3 to 12% variation in Si content. This group of shape memory alloys have potential use as actuators in aircrafts and various other applications.

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Electrical impedance tomography is a method of mapping the conductivity distribution of a domain. For decades it has been considered a potential in situ nondestructive evaluation technique for characterization of conductivity changes in aerospace composites. Yet, several challenges must be addressed before this technique can be transitioned from the laboratory to meaningful practice; for example, the expense of the inverse problem that must be solved to estimate conductivity. An alternative is to characterize damage from the measured voltage-current relationship using deep learning. In this work, we develop and test a deep learning algorithm to characterize time-independent damage events in complex geometry.

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This perspective article focuses on the innovative field of materials-based bacterial engineering, highlighting interdisciplinary research that employs material science to study, augment, and exploit the attributes of living bacteria. By utilizing exogenous abiotic material interfaces, researchers can engineer bacteria to perform new functions, such as enhanced bioelectric capabilities and improved photosynthetic efficiency. Additionally, materials can modulate bacterial communities and transform bacteria into biohybrid microrobots, offering promising solutions for sustainable energy production, environmental remediation, and medical applications. Finally, the perspective discusses a general paradigm for engineering bacteria through the materials-driven modulation of their transmembrane potential. This parameter regulates their ion channel activity and ultimately their bioenergetics, suggesting that controlling it could allow scientists to hack the bioelectric language bacteria use for communication, task execution, and environmental response.

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Development of low-cost and accessible techniques for creating intricate carbon structures is an important research area for advancing various applications. In this work, fused filament fabrication (FFF) of polyethylene (PE) is used to generate 3D-printed structures which are converted to carbon through sulfonation-induced crosslinking and subsequent pyrolysis. Carbons from PE precursors display more continuous morphologies than their polypropylene (PP) counterparts while achieving enhanced reaction kinetics at lower temperatures. This phenomenon enables robust mechanical properties under optimal carbonization conditions. This work provides an essential expansion of precursor selection and understanding of carbonization effects for 3D-printed carbon materials.

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There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed.

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Hybrid halide perovskites (HHPs) are very promising absorber materials for solar cells due to their high power conversion efficiency and the low-cost solution-based processing methods. We applied small angle X-ray scattering to MAPbI₃, FAPbI₃ and MAPbBr₃ precursor solutions in different solvents (GBL, DMF, and mixtures) to gain a deeper understanding of the building blocks during the early stage of HHP formation. We present a core–shell model where the core is formed by [PbX₆] octahedra surrounded by a shell of solvent molecules, which explains the arrangement of the precursors in solution and how the solvent and the halide influence such arrangement.

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Liquid phase exfoliation of non-van der Waals materials has generated renewed interest in fundamental optical and electronic materials discovery and processing. However, such approaches can limit access to novel two-dimensional materials due to the chemistry of exfoliation and processing conditions employed (e.g., processing temperature, mechanical energy input, volatile organic compounds, and sensitive redox chemistries). Here, we demonstrate the exfoliation of bulk hematite (α-Fe₂O₃) powder using a mild bath sonication methodology in liquid monomer media to form stable colloidal dispersions with quasi-two-dimensional hematene nanoflakes. These colloidal dispersions were further processed to form hematene poly(methyl methacrylate) matrix composite substrates.

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