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    From microfluidics to hierarchical hydrogel materials
    (Amsterdam [u.a.] : Elsevier Science, 2023) Weigel, Niclas; Li, Yue; Fery, Andreas; Thiele, Julian
    Over the past two decades, microfluidics has made significant contributions to material and life sciences, particularly via the design of nano-, micro- and mesoscale materials such as nanoparticles, micelles, vesicles, emulsion droplets, and microgels. Unmatched in control over a multitude of material parameters, microfluidics has also shed light on fundamental aspects of material design such as the early stages of nucleation and growth processes as well as structure evolution. Exemplarily, polymer hydrogel particles can be formed via microfluidics with exact control over size, shape, functionalization, compartmentalization, and mechanics that is hardly found in any other processing method. Interestingly, the utilization of microfluidics for material design largely focuses on the fabrication of single entities that act as reaction volume for organic and cell-free biosynthesis, cell mimics, or local environment for cell culturing. In recent years, however, hydrogel design has shifted towards structures that integrate a large variety of functions, e.g., to address the demands for sensing tasks in a complex environment or more closely mimicking architecture and organization of tissue by multiparametric cultures. Hence, this review provides an overview of recent literature that explores microfluidics for fabricating hydrogel materials that go well beyond common length scales as well as the structural and functional complexity of microgels necessary to produce hierarchical hydrogel structures. We focus on examples that utilize microfluidics to design microgel-based assemblies, on microfluidically made polymer microgels for 3D bioprinting, on hydrogels fabricated by microfluidics in a continuous fashion, like fibers, and on hydrogel structures that are shaped by microchannels.
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    Microbiome-based biotechnology for reducing food loss post harvest
    (Amsterdam [u.a.] : Elsevier Science, 2022) Wassermann, Birgit; Abdelfattah, Ahmed; Cernava, Tomislav; Wicaksono, Wisnu; Berg, Gabriele
    Microbiomes have an immense potential to enhance plant resilience to various biotic and abiotic stresses. However, intrinsic microbial communities respond to changes in their host's physiology and environment during plant's life cycle. The potential of the inherent plant microbiome has been neglected for a long time, especially for the postharvest period. Currently, close to 50% of all produced fruits and vegetables are lost either during production or storage. Biological control of spoilage and storage diseases is still lacking sufficiency. Today, novel multiomics technologies allow us to study the microbiome and its responses on a community level, which will help to advance current classic approaches and develop more effective and robust microbiome-based solutions for fruit and vegetable storability, quality, and safety.
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    A novel approach to fabricate load-bearing Ti6Al4V-Barium titanate piezoelectric bone scaffolds by coupling electron beam melting and field-assisted sintering
    (Amsterdam [u.a.] : Elsevier Science, 2022) Riaz, Abdullah; Polley, Christian; Lund, Henrik; Springer, Armin; Seitz, Hermann
    A critical-size bone defect in load-bearing areas is a challenging clinical problem in orthopaedic surgery. Titanium alloy (Ti6Al4V) scaffolds have advantages because of their biomechanical stability but lack electrical activity, which hinders their further use. This work is focused on the fabrication of Ti6Al4V-Barium Titanate (BaTiO3) bulk composite scaffolds to combine the biomechanical stability of Ti6Al4V with electrical activity through BaTiO3. For the first time, a hollow cylindrical Ti6Al4V is additively manufactured by electron beam melting and combined with piezoelectric BaTiO3 powder for joint processing in field-assisted sintering. Scanning electron microscope images on the interface of the Ti6Al4V-BaTiO3 composite scaffold showed that after sintering, the Ti6Al4V lattice structure bounded with BaTiO3 matrix without its major deformation. The Ti6Al4V-BaTiO3 scaffold had average piezoelectric constants of (0.63 ± 0.12) pC/N directly after sintering due to partial dipole alignment of the BaTiO3 tetragonal phase, which increased to (4.92 ± 0.75) pC/N after a successful corona poling. Moreover, the nanoindentation values of Ti6Al4V exhibited an average hardness and Young's modulus of (5.9 ± 0.9) GPa and (130 ± 14) GPa, and BaTiO3 showed (4.0 ± 0.6) GPa and (106 ± 10) GPa, respectively. It reveals that the Ti6Al4V is the harder and stiffer part in the Ti6Al4V-BaTiO3 composite scaffold. Such a scaffold has the potential to treat critical-size bone defects in load-bearing areas and guide tissue regeneration by physical stimulation.
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    Machine learning for additive manufacturing: Predicting materials characteristics and their uncertainty
    (Amsterdam [u.a.] : Elsevier Science, 2023) Chernyavsky, Dmitry; Kononenko, Denys Y.; Han, Jun Hee; Kim, Hwi Jun; van den Brink, Jeroen; Kosiba, Konrad
    Additive manufacturing (AM) is known for versatile fabrication of complex parts, while also allowing the synthesis of materials with desired microstructures and resulting properties. These benefits come at a cost: process control to manufacture parts within given specifications is very challenging due to the relevance of a large number of processing parameters. Efficient predictive machine learning (ML) models trained on small datasets, can minimize this cost. They also allow to assess the quality of the dataset inclusive of uncertainty. This is important in order for additively manufactured parts to meet property specifications not only on average, but also within a given variance or uncertainty. Here, we demonstrate this strategy by developing a heteroscedastic Gaussian process (HGP) model, from a dataset based on laser powder bed fusion of a glass-forming alloy at varying processing parameters. Using amorphicity as the microstructural descriptor, we train the model on our Zr52.5Cu17.9Ni14.6Al10Ti5 (at.%) alloy dataset. The HGP model not only accurately predicts the mean value of amorphicity, but also provides the respective uncertainty. The quantification of the aleatoric and epistemic uncertainty contributions allows to assess intrinsic inaccuracies of the dataset, as well as identify underlying physical phenomena. This HGP model approach enables to systematically improve ML-driven AM processes.
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    Laser powder bed fusion of a superelastic Cu-Al-Mn shape memory alloy
    (Amsterdam [u.a.] : Elsevier Science, 2021) Babacan, N.; Pauly, S.; Gustmann, T.
    Dense and crack-free specimens of the shape memory alloy Cu71.6Al17Mn11.4 (at.%) were produced via laser powder bed fusion across a wide range of process parameters. The microstructure, viz. grain size, can be directly tailored within the process and with it the transformation temperatures (TTs) shifted to higher values by raising the energy input. The microstructure, and the superelastic behavior of additively manufactured samples were assessed by a detailed comparison with induction melted material. The precipitation of the α phase, which inhibit the martensitic transformation, were not observed in the additively manufactured samples owing to the high intrinsic cooling rates during the fabrication process. Fine columnar grains with a strong [001]-texture along the building direction lead to an enhanced yield strength compared to the coarse-grained cast samples. A maximum recoverable strain of 2.86% was observed after 5% compressive loading. The first results of our approach imply that laser powder bed fusion is a promising technique to directly produce individually designed Cu-Al-Mn shape memory parts with a pronounced superelasticity at room temperature.
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    Controlled synthesis of mussel-inspired Ag nanoparticle coatings with demonstrated in vitro and in vivo antibacterial properties
    (Amsterdam [u.a.] : Elsevier Science, 2021) Wang, Xiaowei; Xu, Kehui; Cui, Wendi; Yang, Xi; Maitz, Manfred F.; Li, Wei; Li, Xiangyang; Chen, Jialong
    The in-situ formation of silver nanoparticles (AgNPs) via dopamine-reduction of Ag+ has been widely utilized for titanium implants to introduce antibacterial properties. In previous studies, the preparation of AgNPs has focused on controlling the feeding concentrations, while the pH of the reaction solution was ignored. Herein, we systematically determined the influence of various pH (4, 7, 10) and Ag+ concentrations (0.01, 0.1 mg/mL) on the AgNPs formation, followed by the evaluation of the antibacterial properties in vitro and in vivo. The results revealed that an alkaline environment was favourable for AgNP formation and resulted in more particles. Although the AgNPs bearing Ti had lower biocompatibilities, it was significantly improved after 7 days of mineralization in simulated body fluid. The outstanding antibacterial property of the AgNPs was well maintained after one day and seven days of implantation. Moreover, 3D micro-CT modelling showed that the pH 10/0.1 group exhibited remarkable osteogenesis, which may be due to their strong antibacterial properties and ability to promote mineralization. Therefore, we have demonstrated that the solution pH was as important as the feeding Ag+ concentration in determining AgNP formation, and it has paved the way for developing various AgNP-loaded surfaces that could meet different antibacterial needs.
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    Bayesian approach for auroral oval reconstruction from ground-based observations
    (Amsterdam [u.a.] : Elsevier Science, 2022) Wagner, D.; Neuhäuser, R.; Arlt, R.
    Naked eye observations of aurorae might be used to obtain information on the large-scale magnetic field of the Earth at historic times. Their abundance may also help bridge gaps in observational time-series of proxies for solar activity such as the sunspot number or cosmogenic isotopes. With information derived from aurora observations like observing site, time of aurora sighting and position on the sky we can reconstruct the auroral oval. Since aurorae are correlated with geomagnetic indices like the Kp index, it is possible to obtain information about the terrestrial magnetic field in the form of the position of the magnetic poles as well as the magnetic disturbance level. Here we present a Bayesian approach to reconstruct the auroral oval from ground-based observations by using two different auroral oval models. With this method we can estimate the position of the magnetic poles in corrected geomagnetic coordinates as well as the Kp index. The method is first validated on synthetic observations before it is applied to four modern geomagnetic storms between 2003 and 2017 where ground-based reports and photographs were used to obtain the necessary information. Based on the four modern geomagnetic storms we have shown, that we are able to reconstruct the pole location with an average accuracy of ≈2° in latitude and ≈11° in longitude. The Kp index can be inferred with a precision of one class. The future goal is to employ the method to historical storms, where we expect somewhat higher uncertainties, since observations may be less accurate or not favorably distributed.
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    Greenhouse gas emissions from broiler manure treatment options are lowest in well-managed biogas production
    (Amsterdam [u.a.] : Elsevier Science, 2020) Kreidenweis, Ulrich; Breier, Jannes; Herrmann, Christiane; Libra, Judy; Prochnow, Annette
    The production of broiler meat has increased significantly in the last decades in Germany and worldwide, and is projected to increase further in the future. As the number of animals raised increases, so too does the amount of manure produced. The identification of manure treatment options that cause low greenhouse gas emissions becomes ever more important. This study compares four treatment options for broiler manure followed by field spreading: storage before distribution, composting, anaerobic digestion in a biogas plant and production of biochar. For these options potential direct and indirect greenhouse gas emissions were assessed for the situation in Germany. Previous analyses have shown that greenhouse gas balances of manure management are often strongly influenced by a small number of processes. Therefore, in this study major processes were represented with several variants and the sensitivity of model results to different management decisions and uncertain parameters was assessed. In doing so, correlations between processes were considered, in which higher emissions earlier on in the process chain reduce emissions later. The results show that biogas production from broiler manure leads to the lowest greenhouse gas emissions in most of the analysed cases, mainly due to the emission savings related to the substitution of mineral fertilizers and the production of electricity. Pyrolysis of the manure and subsequent field spreading as a soil amendment can lead to similarly low emissions due to the long residence time of the biochar, and may even be the better option than poorly managed biogas production. Composting is the treatment option resulting in highest emissions of greenhouse gases, due to high ammonia volatilization, and is likely worse than untreated storage in this respect. These results are relatively insensitive to the length of transport required for field spreading, but high uncertainties are associated with the use of emission factors.
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    In situ powder X-ray diffraction during hydrogen reduction of MoO3 to MoO2
    (Amsterdam [u.a.] : Elsevier Science, 2022) Burgstaller, M.; Lund, H.; O'Sullivan, M.; Huppertz, H.
    The hydrogen reduction of molybdenum trioxide to molybdenum dioxide is not yet fully understood as evident by continuous scientific interest. Especially the effect of the potassium content on the reduction process has not yet been considered. We prepared several samples of molybdenum trioxide containing varying amounts of potassium by addition of potassium molybdate (K2MoO4). In situ powder X-ray diffraction experiments were then conducted to study the hydrogen reduction of these samples. We especially focused on the influence of the alkali content and on gaining insight into the importance of the intermediary product γ-Mo4O11. During the reduction process, MoO2 is formed from the reduction of MoO3, which then reacts with the starting material to form γ-Mo4O11. With increasing potassium content, the reduction rate is decreased and the fractional content of γ-Mo4O11 built up during the reduction process is increased. As evident from bulk sample reduction, this results in a significant increase in the grain size visualized via scanning electron microscopy. Our investigations once again underline the importance of γ-Mo4O11 on the morphology of the resulting MoO2 powder.
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    Changing seasonal temperature offers a window of opportunity for stricter climate policy
    (Amsterdam [u.a.] : Elsevier Science, 2022) Pfeifer, Lena; Otto, Ilona M.
    Environmental catastrophes, including the increased severity and frequency of climate extremes, can act as “windows of opportunities” that challenge citizens’ mental models and motivate them to engage in reflective processes, challenging their pre-conceived ideas. Less well understood is whether experiencing changing weather conditions, common in mid-latitudes, can have a similar effect and increase the citizens’ concerns about climate change and their willingness to accept more stringent climate policies. In this paper, we investigate the effects of changing seasonal temperature on the perceived seriousness of climate change and willingness to mitigate climate change. We use data from four yearly waves of a spatially explicit representative population survey in Germany and weather records from the postal code areas in which they live. To our knowledge, this study is the first analysis to link individual perceptions towards climate change and different mitigation options with seasonal temperature changes at specific locations in Europe. The analyzed perceptions were strongly influenced by socio-demographic characteristics and broader societal changes, as well as individual experiences of seasonal temperatures. The results show that experienced seasonal temperature change influences personal climate change concerns as well as the willingness to mitigate climate change, although with a weaker effect. The results indicate that it is the absolute temperature variation experienced that is important, rather than whether it is getting colder or warmer than usual. Considering the influences identified in this study can offer a window of opportunity for more stringent and targeted climate change policy.