This work describes a device for time-resolved synchrotron-based in situ and operando X-ray powder diffraction measurements at elevated temperatures under controllable gaseous environments. The respective gaseous sample environment is realized via a gas-tight capillary-in-capillary design, where the gas flow is achieved through an open-end 0.5 mm capillary located inside a 0.7 mm capillary filled with a sample powder. Thermal mass flow controllers provide appropriate gas flows and computer-controlled on-the-fly gas mixing capabilities. The capillary system is centered inside an infrared heated, proportional integral differential-controlled capillary furnace allowing access to temperatures up to 1000 °C.
Additive manufacturing of endless carbon fiber-reinforced composites is a technology which produces parts with mechanical properties similar to those of additively-manufactured metallic parts. In this work, the influence of layer height and width on mechanical properties of additively-manufactured carbon fiber-reinforced polymer composites has been studied. Two different 3k carbon fibers have been used as reinforcement. The composites are printed by material extrusion technology with layer heights of 0.2, 0.3, and 0.4 mm and layer widths of 1.0, 1.2, and 1.7 mm. The composites possess higher flexural strength at smaller layer height and the flexural modulus is dependent on the fiber volume content. The formation of voids/defects decreases the mechanical properties of composite and should be optimized.
Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation. While established methods for evaluating model performance exist, an intuitive understanding of the modeling and decision-making processes in models is nonetheless desired in many cases. In this work, we demonstrate several ways of incorporating model interpretability to the structure-agnostic Compositionally Restricted Attention-Based network, CrabNet. We show that CrabNet learns meaningful, material property-specific element representations based solely on the data with no additional supervision. These element representations can then be used to explore element identity, similarity, behavior, and interactions within different chemical environments. Chemical compounds can also be uniquely represented and examined to reveal clear structures and trends within the chemical space. Additionally, visualizations of the attention mechanism can be used in conjunction to further understand the modeling process, identify potential modeling or dataset errors, and hint at further chemical insights leading to a better understanding of the phenomena governing material properties. We feel confident that the interpretability methods introduced in this work for CrabNet will be of keen interest to materials informatics researchers as well as industrial practitioners alike.
Background Recent efforts in fungal biotechnology aim to develop new concepts and technologies that convert renewable plant biomass into innovative biomaterials. Hereby, plant substrates become metabolized by filamentous fungi to transform them into new fungal-based materials. Current research is thus focused on both understanding and optimizing the biology and genetics underlying filamentous fungal growth and on the development of new technologies to produce customized fungal-based materials. Results This manuscript reports the production of stable pastes, composed of Fomes fomentarius mycelium, alginate and water with 71 wt.% mycelium in the solid content, for additive manufacturing of fungal-based composite materials. After printing complex shapes, such as hollow stars with up to 39 mm in height, a combination of freeze-drying and calcium-crosslinking processes allowed the printed shapes to remain stable even in the presence of water. The printed objects show low bulk densities of 0.12 ± 0.01 g/cm3 with interconnected macropores. Conclusions This work reports for the first time the application of mycelium obtained from the tinder fungus F. fomentarius for an extrusion-based additive manufacturing approach to fabricate customized light-weight 3D objects. The process holds great promise for developing light-weight, stable, and porous fungal-based materials that could replace expanded polystyrene produced from fossil resources.
Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation. While established methods for evaluating model performance exist, an intuitive understanding of the modeling and decision-making processes in models is nonetheless desired in many cases. In this work, we demonstrate several ways of incorporating model interpretability to the structure-agnostic Compositionally Restricted Attention-Based network, CrabNet. We show that CrabNet learns meaningful, material property-specific element representations based solely on the data with no additional supervision. These element representations can then be used to explore element identity, similarity, behavior, and interactions within different chemical environments. Chemical compounds can also be uniquely represented and examined to reveal clear structures and trends within the chemical space. Additionally, visualizations of the attention mechanism can be used in conjunction to further understand the modeling process, identify potential modeling or dataset errors, and hint at further chemical insights leading to a better understanding of the phenomena governing material properties. We feel confident that the interpretability methods introduced in this work for CrabNet will be of keen interest to materials informatics researchers as well as industrial practitioners alike.
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