Search Results
Nitrous oxide emissions from winter oilseed rape cultivation
Winter oilseed rape (Brassica napus L., WOSR) is the major oil crop cultivated in Europe. Rapeseed oil is predominantly used for production of biodiesel. The framework of the European Renewable Energy Directive requires that use of biofuels achieves GHG savings of at least 50% compared to use of fossil fuel starting in 2018. However, N2O field emissions are estimated using emission factors that are not specific for the crop and associated with strong uncertainty. N2O field emissions are controlled by N fertilization and dominate the GHG balance of WOSR cropping due to the high global warming potential of N2O. Thus, field experiments were conducted to increase the data basis and subsequently derive a new WOSR-specific emission factor. N2O emissions and crop yields were monitored for three years over a range of N fertilization intensities at five study sites representative of German WOSR production. N2O fluxes exhibited the typical high spatial and temporal variability in dependence on soil texture, weather and nitrogen availability. The annual N2O emissions ranged between 0.24 kg and 5.48 kg N2O-N ha−1 a−1. N fertilization increased N2O emissions, particularly with the highest N treatment (240 kg N ha−1). Oil yield increased up to a fertilizer amount of 120 kg N ha−1, higher N-doses increased grain yield but decreased oil concentrations in the seeds. Consequently oil yield remained constant at higher N fertilization. Since, yield-related emission also increased exponentially with N surpluses, there is potential for reduction of the N fertilizer rate, which offers perspectives for the mitigation of GHG emissions. Our measurements double the published data basis of annual N2O flux measurements in WOSR. Based on this extended dataset we modeled the relationship between N2O emissions and fertilizer N input using an exponential model. The corresponding new N2O emission factor was 0.6% of applied fertilizer N for a common N fertilizer amount under best management practice in WOSR production (200 kg N ha−1 a−1). This factor is substantially lower than the linear IPCC Tier 1 factor (EF1) of 1.0% and other models that have been proposed. © 2017
The impact of chemical short-range order on the thermophysical properties of medium- and high-entropy alloys
The unusual behavior observed in the coefficient of thermal expansion and specific heat capacity of CrFeNi, CoCrNi, and CoCrFeNi medium/high-entropy alloys is commonly referred to as the K-state effect. It is shown to be independent of the Curie temperature, as demonstrated by temperature-dependent magnetic moment measurements. CoCrFeNi alloy is chosen for detailed characterization; potential reasons for the K-state effect such as texture, recrystallization, and second-phase precipitation are ruled out. An examination of the electronic structure indicates the formation of a pseudo-gap in the Density of States, which suggests a specific chemical interaction between Ni and Cr atoms upon alloying. Hybrid Monte Carlo/Molecular Dynamic (MC/MD) simulations indicate the presence of non-negligible chemical short-range order (CSRO). Local lattice distortions are shown to be negligible, although deviations around Cr and Ni elements from those expected in a fully disordered structure are experimentally observed by X-ray absorption spectroscopy. The determined bonding distances are in good agreement with MC/MD calculations. A mechanism is proposed to explain the anomalies and calorimetric experiments and their results are used to validate the mechanism.
Experimental and numerical characterization of imperfect additively manufactured lattices based on triply periodic minimal surfaces
Lattices based on triply periodic minimal surfaces (TPMS) are attracting increasing interest in seminal industries such as bone tissue engineering due to their excellent structure-property relationships. However, the potential can only be exploited if their structural integrity is ensured. This requires a fundamental understanding of the impact of imperfections that arise during additive manufacturing. Therefore, in the present study, the structure-property relationships of eight TPMS lattices, including their imperfections, are investigated experimentally and numerically. In particular, the focus is on biomimetic network TPMS lattices of the type Schoen I-WP and Gyroid, which are fabricated by laser powder bed fusion from the biocompatible alloy Ti-42Nb. The experimental studies include computed tomography measurements and compression tests. The results highlight the importance of process-related imperfections on the mechanical performance of TPMS lattices. In the numerical work, firstly the as-built morphology is artificially reconstructed before finite element analyses are performed. Here, the reconstruction procedure previously developed by the same authors is used and validated on a larger experimental matrix before more advanced calculations are conducted. Specifically, the reconstruction reduces the numerical overestimation of stiffness from up to 341% to a maximum of 26% and that of yield strength from 66% to 12%. Given a high simulation accuracy and flexibility, the presented procedure can become a key factor in the future design process of TPMS lattices.
Thermal annealing to influence the vapor sensing behavior of co-continuous poly(lactic acid)/polystyrene/multiwalled carbon nanotube composites
With the main purpose of being used as vapor leakage detector, the volatile organic compound (VOC) vapor sensing properties of conductive polymer blend composites were studied. Poly(lactic acid)/polystyrene/multi-walled carbon nanotube (PLA/PS/MWCNT) based conductive polymer composites (CPCs) in which the polymer components exhibit different interactions with the vapors, were prepared by melt mixing. CPCs with a blend composition of 50/50 wt% resulted in the finest co-continuous structure and selective MWCNT localization in PLA. Therefore, these composites were selected for sensor tests. Thermal annealing was applied aiming to maintain the blend structure but improving the sensing reversibility of CPC sensors towards high vapor concentrations. Different sensing protocols were applied using acetone (good solvent for PS and PLA) and cyclohexane (good solvent for PS but poor solvent for PLA) vapors. Increasing acetone vapor concentration resulted in increased relative resistance change (Rrel) of CPCs. Saturated cyclohexane vapor resulted in lower response than nearly saturated acetone vapor. The thermal annealing at 150 °C did not change the blend morphology but increased the PLA crystallinity, making the CPC sensors more resistant to vapor stimulation, resulting in lower Rrel but better reversibility after vapor exposure.
Ammonia and greenhouse gas emissions from slurry storage : A review
Storage of slurry is an important emission source for ammonia (NH3), nitrous oxide (N2O), methane (CH4), carbon dioxide (CO2) and hydrogen sulfide (H2S) from livestock production. Therefore, this study collected published emission data from stored cattle and pig slurry to determine baseline emission values and emission changes due to slurry treatment and coverage of stores. Emission data were collected from 120 papers yielding 711 records of measurements conducted at farm-, pilot- and laboratory-scale. The emission data reported in a multitude of units were standardized and compiled in a database. Descriptive statistics of the data from untreated slurry stored uncovered revealed a large variability in emissions for all gases. To determine baseline emissions, average values based on a weighting of the emission data according to the season and the duration of the emission measurements were constructed using the data from farm-scale and pilot-scale studies. Baseline emissions for cattle and pig slurry stored uncovered were calculated. When possible, it was further distinguished between storage in tanks without slurry treatment and storage in lagoons which implies solid-liquid separation and biological treatment. The baseline emissions on an area or volume basis are: for NH3: 0.12 g m−2 h-1 and 0.15 g m−2 h-1 for cattle and pig slurry stored in lagoons, and 0.08 g m−2 h-1 and 0.24 g m−2 h-1 for cattle and pig slurry stored in tanks; for N2O: 0.0003 g m−2 h-1 for cattle slurry stored in lagoons, and 0.002 g m−2 h-1 for both slurry types stored in tanks; for CH4: 0.95 g m-3 h-1 and 3.5 g m-3 h-1 for cattle and pig slurry stored in lagoons, and 0.58 g m-3 h-1 and 0.68 g m-3 h-1 for cattle and pig slurry stored in tanks; for CO2: 6.6 g m−2 h-1 and 0.3 g m−2 h-1 for cattle and pig slurry stored in lagoons, and 8.0 g m−2 h-1 for both slurry types stored in tanks; for H2S: 0.04 g m−2 h-1 and 0.01 g m−2 h-1 for cattle and pig slurry stored in lagoons. Related to total ammoniacal nitrogen (TAN), baseline emissions for tanks are 16% and 15% of TAN for cattle and pig slurry, respectively. Emissions of N2O and CH4 relative to nitrogen (N) and volatile solids (VS) are 0.13% of N and 0.10% of N and 2.9% of VS and 4.7% of VS for cattle and pig slurry, respectively. Total greenhouse gas emissions from slurry stores are dominated by CH4. The records on slurry treatment using acidification show a reduction of NH3 and CH4 emissions during storage while an increase occurs for N2O and a minor change for CO2 as compared to untreated slurry. Solid-liquid separation causes higher losses for NH3 and a reduction in CH4, N2O and CO2 emissions. Anaerobically digested slurry shows higher emissions during storage for NH3 while losses tend to be lower for CH4 and little changes occur for N2O and CO2 compared to untreated slurry. All cover types are found to be efficient for emission mitigation of NH3 from stores. The N2O emissions increase in many cases due to coverage. Lower CH4 emissions occur for impermeable covers as compared to uncovered slurry storage while for permeable covers the effect is unclear or emissions tend to increase. Limited and inconsistent data regarding emission changes with covering stores are available for CO2 and H2S. The compiled data provide a basis for improving emission inventories and highlight the need for further research to reduce uncertainty and fill data gaps regarding emissions from slurry storage.
Influence of annealing on microstructure and mechanical properties of ultrafine-grained Ti45Nb
Beta-Ti alloys have been intensively investigated in the last years because of their favorable low Young's moduli, biocompatibility and bio-inertness, making these alloys interesting candidates for implant materials. Due to their low mechanical strength, efforts are currently devoted to increasing it. A promising way to improve the strength is to tailor the microstructure using severe plastic deformation (SPD). In this investigation high pressure torsion was used to refine the microstructure of a Ti-45wt.%Nb alloy inducing a grain size of ~50 nm. The main focus of the subsequent investigations was devoted to the thermal stability of the microstructure. Isochronal heat-treatments performed for 30 min in a temperature range up to 500 °C caused an increase of hardness with a peak value at 300 °C before the hardness decreased at higher temperatures. Simultaneously, a distinct temperature-dependent variation of the Young's modulus was also measured. Tensile tests revealed an increase in strength after annealing compared to the SPD-state. Microstructural investigations showed that annealing causes the formation of α-Ti. The findings suggest that the combination of severe plastic deformation with subsequent heat treatment provides a feasible way to improve the mechanical properties of SPD-deformed β-Ti alloys making them suitable for higher strength applications.
A promising approach to low electrical percolation threshold in PMMA nanocomposites by using MWCNT-PEO predispersions
Electrical conductive poly(methyl methacrylate) (PMMA) nanocomposites with low percolation threshold are very challenging to be prepared. Here, we show that the miscibility between poly(ethylene oxide) (PEO) as matrix for predispersions of multi-walled carbon nanotubes (MWCNTs) and PMMA represents an efficient approach to achieve very low electrical percolation threshold. PMMA/PEO-MWCNTs nanocomposites were prepared by a two-step solution casting method involving pre-mixing of MWCNTs with PEO and then mixing of PEO-MWCNTs with PMMA, resulting in a PMMA/PEO ratio of 80/20 wt%. The electrical percolation threshold (EPT) value was determined to be ~ 0.07 wt% which is significantly lower than most of the reported EPT values in the literature for PMMA/CNT composites. The very low electrical percolation threshold was attributed to the effectual role of PEO in self-assembly of secondary structures of nanotubes into an electrically conductive network. This was further confirmed by transmission electron microscopy and by comparing the obtained EPT value with the prediction of the excluded volume model in which statistical percolation threshold is defined based on uniform distribution of high-aspect ratio sticks in a matrix. Moreover, based on UV–Vis measurements and linear viscoelastic rheological measurements, optical and rheological percolation thresholds were obtained at nearly 0.01 wt% and 0.5 wt%, respectively.
Design of biomimetic collagen matrices by reagent-free electron beam induced crosslinking: Structure-property relationships and cellular response
Novel strategies to mimic mammalian extracellular matrix (ECM) in vitro are desirable to study cell behavior, diseases and new agents in drug delivery. Even though collagen represents the major constituent of mammalian ECM, artificial collagen hydrogels with characteristic tissue properties such as network size and stiffness are difficult to design without application of chemicals which might be even cytotoxic. In our study we investigate how high energy electron induced crosslinking can be utilized to precisely tune collagen properties for ECM model systems. Constituting a minimally invasive approach, collagen residues remain intact in the course of high energy electron treatment. Quantification of the 3D pore size of the collagen network as a function of irradiation dose shows an increase in density leading to decreased pore size. Rheological measurements indicate elevated storage and loss moduli correlating with an increase in crosslinking density. In addition, cell tests show well maintained viability of NIH 3T3 cells for irradiated collagen gels indicating excellent cellular acceptance. With this, our investigations demonstrate that electron beam crosslinked collagen matrices have a high potential as precisely tunable ECM-mimetic systems with excellent cytocompatibility.
A novel approach to fabricate load-bearing Ti6Al4V-Barium titanate piezoelectric bone scaffolds by coupling electron beam melting and field-assisted sintering
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.
Machine learning for additive manufacturing: Predicting materials characteristics and their uncertainty
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.