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L-Lactide (LA) was polymerized with neat tin(II) 2-ethylhexanoate (SnOct2) in toluene at 115 °C at low concentration with variation of the LA/Cat ratio. Cyclic polylactides (cPLAs) with number average molecular weights (Mn) between 7 000 and 17 000 were obtained. MALDI-TOF mass spectrometry also revealed the formation of a few percent of linear chains. Crystalline cPLAs with Mn around 9 000 and 14 000 were annealed at 140 °C in the presence of ScOct2 or dibutyl-2-stanna-1,3-dithiolane (DSTL). Simultaneously, crystallites of extended linear chains and crystallites of extended cycles were formed regardless of the catalyst, indicating that transesterification reaction proceeded different for linear chains and for cycles, governed by thermodynamic control. The formation of extended chain crystallites with low dispersity indicates the existence of symproportionation of short and long chains. A complementary experiment was carried out with a PLA ethyl ester composed mainly of linear chains with a small fraction of cycles
AbstractThis research aims to detect and analyze critical internal and surface defects in metal components manufactured by powder bed fusion of metals using a laser beam (PBF‐LB/M). The aim is to assess their impact on the fatigue behavior. Therefore, a combination of methods, including image processing of micro‐computed tomography (CT) scans, fatigue testing, and machine learning, was applied. A workflow was established to contribute to the nondestructive assessment of component quality and mechanical properties. Additionally, this study illustrates the application of machine learning to address a classification problem, specifically the categorization of pores into gas pores and lack of fusion pores. Although it was shown that internal defects exhibited a reduced impact on fatigue behavior compared with surface defects, it was noted that surface defects exert a higher influence on fatigue behavior. A machine learning algorithm was developed to predict the fatigue life using surface defect features as input parameters.
Ceramic additive manufacturing (AM) requires a complex process chain with various post-processing steps that require expensive machines and special expertise. The key to further market penetration is AM that makes it possible to integrate into an already established ceramic process chain. Most successful AM technologies for ceramics are, however, based on processes that initially have been developed for polymeric materials. For ceramics AM, polymers or precursors are loaded with ceramic particles. This strategy facilitates the entry into AM, however the introduction of organic additives into the ceramic process chain represents a considerable technological challenge to ultimately obtain a ceramic component after additive shaping. In the present communication, two technologies based on ceramic suspensions will be introduced, the “layerwise slurry deposition” (LSD) and “laser induced slip casting” (LIS) technology. Both technologies take advantage of the high packing densities reached by conventional slip casting and moreover enable the processing of fines, even nanoparticles.
A key aspect in the development of multilayer inductors is the magnetic permeability of the ferrite layers. Here, the effects of different processing steps on the permeability of a NiCuZn ferrite is investigated. Dry pressed, tape cast, and co‐fired multilayer samples are analyzed. An automated data pipeline is applied to structure the acquired experimental data according to a domain ontology based on PMDco (Platform MaterialDigital core ontology). Example queries to the ontology show how the determined process‐property correlations are accessible to non‐experts and thus how suitable data for component design can be identified. It is demonstrated how the inductance of co‐fired multilayer inductors is reliably predicted by simulations if the appropriate input data corresponding to the manufacturing process is used.This article is protected by copyright. All rights reserved.
This data article presents a set of primary, analyzed, and digitalized mechanical testing datasets for nine copper alloys. The mechanical testing methods including the Brinell and Vickers hardness, tensile, stress relaxation, and low-cycle fatigue (LCF) testing were performed according to the DIN/ISO standards. The obtained primary testing data (84 files) mainly contain the raw measured data along with the testing metadata of the processes, materials, and testing machines. Five secondary datasets were also provided for each testing method by collecting the main meta- and measurement data from the primary data and the outputs of data analyses. These datasets give materials scientists beneficial data for comparative material selection analyses by clarifying the wide range of mechanical properties of copper alloys, including Brinell and Vickers hardness, yield and tensile strengths, elongation, reduction of area, relaxed and residual stresses, and LCF fatigue life. Furthermore, both the primary and secondary datasets were digitalized by the approach introduced in the research article entitled “Toward a digital materials mechanical testing lab” [1]. The resulting open-linked data are the machine-processable semantic descriptions of data and their generation processes and can be easily queried by semantic searches to enable advanced data-driven materials research.
Energy harvested from intermittent sources can be stored in supercapacitors for high-power delivery with long cycling stability. Binary layered double hydroxide (LDH) materials have great potential for hybrid supercapacitor applications owing to their mixed and tunable charges and layered structure. This study presents carnation flower-like, 3D micro-structured NiMn-LDH prepared by a facile single-step hydrothermal synthesis using hexamethylenetetramine to produce hydroxides. The 3D structure was assembled from ultrathin 2D NiMn-LDH nanosheets, and the largest interlayer spacing was obtained by optimizing synthesis parameters, such as Ni:Mn molar ratio and reaction temperature, ensuring a fast diffusion and thus the best energy storage performance. The optimized NiMn-LDH electrode delivered a high specific capacity of 612 C/g with an excellent rate capability of 67% at 20 A/g in a three-electrode test. An asymmetric device assembled using NiMn-LDH and reduced graphene oxide as positive and negative electrodes provided a high energy density of 60.0 Wh/kg and power density of 17.7 kW/kg with 90.4% capacity retention after 10,000 charge–discharge cycles. This superior result highlights the potential industrial applications, such as portable electronics and trams.
AbstractTwo‐photon polymerization (2PP) additive manufacturing (AM) utilizes feedstocks of ceramic nanoparticles of a few nanometers in diameter, enabling the fabrication of highly accurate technical ceramic design with structural details as small as 500 nm. The performance of these materials is expected to differ from conventional AM ceramics, as nanoparticles and three‐dimensional printing at high resolution introduce new microstructural aspects. This study applies 2PP‐AM of yttria‐stabilized zirconia to investigate the mechanical response behavior under compressive load, probing the influence of smallest structural units induced by the line packing during the printing process, design of sintered microblocks, and sintering temperature and thereby microstructure. We find a dissipative mechanical response enhanced by sintering at lower temperatures than conventional. The pursued 2PP‐AM approach yields a microstructured material with an increased number of grain boundaries that proposedly play a major role in facilitating energy dissipation within the here printed ceramic material. This microplastic response is further triggered by the filigree structures induced by hollow line packing at the order of the critical defect size of ceramics. Together, these unique aspects made accessible by the 2PP‐AM approach contribute to a heterogeneous nano‐ and microstructure, and hint toward opportunities for tailoring the mechanical response in future ceramic applications.
Automated computational workflows are a powerful concept that can improve the usability and reproducibility of simulation and data processing approaches. Although used very successfully in bioinformatics, workflow environments in materials science are currently commonly applied in the field of atomistic simulations. This work showcases the integration of a discrete element method (DEM) simulation of powder pressing in the convenient SimStack workflow environment. For this purpose, a Workflow active Node (WaNo) was developed to generate input scripts for the DEM solver using LIGGGHTS Open Source Discrete Element Method Particle Simulation code. Combining different WaNos in the SimStack framework makes it possible to build workflows and loop over different simulation or evaluation conditions. The functionality of the workflows is explained, and the added user value is discussed. The procedure presented here is an example and template for many other simulation methods and issues in materials science and engineering.
Addressing a strategy for publishing open and digital research data, this article presents the approach for streamlining and automating the process of storage and conversion of research data to those of semantically queryable data on the web. As the use case for demonstrating and evaluating the digitalization process, the primary datasets from low‐cycle‐fatigue testing of several copper alloys are prepared. The fatigue test ontology (FTO) and ckan.kupferdigital data management system are developed as two main prerequisites of the data digitalization process. FTO has been modeled according to the content of the fatigue testing standard and by reusing the basic formal ontology, industrial ontology foundry core ontology, and material science and engineering ontology. The ckan.kupferdigital data management system is also constructed in such a way that enables the users to prepare the protocols for mapping the datasets into the knowledge graph and automatically convert all the primary datasets to those machine‐readable data which are represented by the web ontology language. The retrievability of the converted digital data is also evaluated by querying the example competency questions, confirming that ckan.kupferdigital enables publishing open data that can be highly reused in the semantic web.
AbstractThe correlation between altered extracellular pH and various pathological conditions, including cancer, inflammation and metabolic disorders, is well known. Bulk pH measurements cannot report the extracellular pH value at the cell surface. However, there is a limited number of suitable tools for measuring the extracellular pH of cells with high spatial resolution, and none of them are commonly used in laboratories around the world. In this study, a versatile ratiometric nanosensor for the measurement of extracellular pH was developed. The nanosensor consists of biocompatible polystyrene nanoparticles loaded with the pH-inert reference dye Nile red and is surface functionalized with a pH-responsive fluorescein dye. Equipped with a targeting moiety, the nanosensor can adhere to cell membranes, allowing direct measurement of extracellular pH at the cell surface. The nanosensor exhibits a sensitive ratiometric pH response within the range of 5.5–9.0, with a calculated pKa of 7.47. This range optimally covers the extracellular pH (pHe) of most healthy cells and cells in which the pHe is abnormal, such as cancer cells. In combination with the nanosensors ability to target cell membranes, its high robustness, reversibility and its biocompatibility, the pHe nanosensor proves to be well suited for in-situ measurement of extracellular pH, even over extended time periods. This pH nanosensor has the potential to advance biomedical research by improving our understanding of cellular microenvironments, where extracellular pH plays an important role.