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Analysis of Fiber Attrition and Mechanical Performance in Large-Format Additive Manufacturing of Long-Fiber Reinforced Polymer Composites
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Compositional inhomogeneity and its effect on the hardness of nanostructured refractory high-entropy alloys
The pronounced compositional inhomogeneity observed in refractory high-entropy alloys (HEAs), as opposed to non-refractory HEAs, has an important influence on their mechanical properties, thereby posing a significant challenge for the development of high-performance refractory HEAs. In this work, by combining transmission electron microscopy imaging, chemical analysis, and nano-hardness tests, we investigate the compositional inhomogeneities in a series of sintered nanostructured HEAs and elucidate their influence on the material's hardness. Further, we reveal that the compositional inhomogeneity of the sintered nanostructured HEAs is temperature- and component- dependent. By correlating the hardness of nanostructured HEAs to the evolving compositional inhomogeneity, our work demonstrates that the compositional inhomogeneity in nanostructured HEAs can be tuned by sintering temperature or alloying towards optimized microstructure and hardness.
An approach to identifying fibers and evolved compounds from flame resistant fabrics
Flame resistant (FR) fabrics have use in civilian, military, and industrial applications. Herein, this work describes the development of a methodology aimed at identifying the fiber composition of blended fabrics of unknown composition. Here, FR fabrics used in military uniforms, are studied using a combination of X-ray photoelectron spectroscopy (XPS), isothermal thermogravimetric analysis (TGA) and pyrolysis gas chromatography/mass spectrometry (Py-GC-MS). Elemental analysis of the fabrics using XPS yielded a preliminary determination of the composition of the polymer(s) and aided in the identification of FR additives. TGA and Py-GC-MS experiments were used for subsequent compound identification. In TGA the temperature of mass loss events was compared to reference materials, and in Py-GC-MS the pyrolysis products of the blended FR fabrics were compared to those from a series of potential parent fibers. It was possible to discern the composition of the parent fibers and the type of FR treatment added to fabrics because the thermal decomposition chemistry did not significantly change by blending the fibers to make fabrics.
ZPPR-15 Criticality Measurements
A reference critical configuration was established for each phase of the ZPPR-15 experimental program. The reference critical configuration for each phase corresponds to a configuration in which the measured excess reactivity was adjusted to a standard set of reporting conditions with an average reactor temperature of 293 K, a zero interface gap and all operational control rods parked 30 inches from the axial centerline of the core. The ZPPR staff published the measured excess reactivity and the statistical uncertainty in the measurement. In reality, the published uncertainty for each reference critical configuration is only a small component of the total uncertainty for that configuration. Additional uncertainties related measurement technique, configuration geometry and material compositions exist. The geometry and material composition uncertainties are the dominant uncertainties for ZPPR-15. A full uncertainty analysis was performed for each ZPPR-15 reference critical configuration. The significant uncertainties related to measurement technique, configuration geometry and material compositions were quantified, and a total uncertainty was derived for each reference critical configuration. The ZPPR-15 experimental records were used to create detailed as-built Monte Carlo models for the reference critical configurations established in ZPPR-15A loading 15, ZPPR-15B loading 88, ZPPR-15C loading 166 and ZPPR-15D loading 185. The as-built models neglect the small, non-uniform gap between matrix halves at full closure, any structures outside the matrix tubes and minor components, e.g., impurities, in some materials. The biases associated with the neglected items were quantified. The experimental value of keff and its associated uncertainty for each reference critical configuration were adjusted to account for biases introduced by items that are neglected in the as-built model for that configuration.
Prediction of crystal structures and motifs in the Fe–Mg–O system at Earth’s core pressures
Abstract Fe, Mg, and O are among the most abundant elements in terrestrial planets. While the behavior of the Fe–O, Mg–O, and Fe–Mg binary systems under pressure have been investigated, there are still very few studies of the Fe–Mg–O ternary system at relevant Earth’s core and super-Earth’s mantle pressures. Here, we use the adaptive genetic algorithm (AGA) to study ternary Fe x Mg y O z phases in a wide range of stoichiometries at 200 GPa and 350 GPa. We discovered three dynamically stable phases with stoichiometries FeMg 2 O 4 , Fe 2 MgO 4, and FeMg 3 O 4 with lower enthalpy than any known combination of Fe–Mg–O high-pressure compounds at 350 GPa. With the discovery of these phases, we construct the Fe–Mg–O ternary convex hull. We further clarify the composition- and pressure-dependence of structural motifs with the analysis of the AGA-found stable and metastable structures. Analysis of binary and ternary stable phases suggest that O, Mg, or both could stabilize a BCC iron alloy at inner core pressures.
Thermodynamic modeling of the Pd–Zn system with uncertainty quantification and its implication to tailor catalysts
Pd–Zn intermetallic catalysts show encouraging combinations of activity and selectivity on well-defined active site ensembles. Thermodynamic description of the Pd–Zn system, delineating phase boundaries and enumerating site occupancies within intermediate alloy phases, is essential to determining the ensembles of Pd–Zn atoms as a function of composition and temperature. Combining the present extensive first-principles calculations based on density functional theory (DFT) and available experimental data, the Pd–Zn system was remodeled using the CALculation of PHAse Diagrams (CALPHAD) approach. High throughput modeling tools with uncertainty quantification, i.e., ESPEI and PyCalphad, were incorporated in the phase analysis. Here, the site occupancies across the γ phase composition region were given special attention. A four-sublattice model was used for the γ phase owing to its four Wyckoff positions, i.e., the outer tetrahedral (OT) site 8c, the inner tetrahedral (IT) site 8c, the octahedral (OH) site 12e, and the cuboctahedral (CO) site 24g. The site fractions of Pd and Zn calculated from the present thermodynamic model show the occupancy preference of Pd in the OT and OH sublattices in agreement with experimental observations. The force constants obtained from DFT-based phonon calculations further supports the tendency of Pd occupying the OH sublattice compared with the IT and CO sublattice. The catalytic ensembles changing from Pd monomers (Pd 1 ) to trimers (Pd 3 ) on the surface of γ phase are attributed to the increase of Pd occupancy in the OH sublattice.
Fuels Characterization for National Research Council Canada 2-m Pool Fire Test Series
This report provides a detailed analysis of the physical and chemical properties of three liquid hydrocarbon fuels: heptane, Bakken crude, and a diluted bitumen, that were subsequently tested in a series of 2-m pool fire experiments at Sandia National Laboratories for the National Research Council Canada. Properties such as relative density, vapor pressure (VPCR x ), composition, and heat of combustion were evaluated. The heptane analysis, with relative density = 0.69 (at 15°C), confirmed that the material tested was consistent with high-purity (>99%) n-heptane. The Bakken crude, with a relative density = 0.81 (at 15°C), exhibited a vapor pressure by VPCR 0.2 (37.8°C) in the range 120-157 kPa. The dilbit, with a relative density = 0.92 (at 15°C) exhibited a vapor pressure by VPCR 0.2 (37.8°C) in the range 85-98 kPa. Solids remaining in the test pans after the pool fires were also collected and weighed. No detectable solids were left after the heptane burns. In contrast, the crude oils left some brittle, black solid residue. On average, dilbit pool fires left about 40 more residue by mass than Bakken pool fires for equivalent mass of fuel feed.
Self-Repairing Polymer-Modified Cements for High Temperature Geothermal and Fossil Energy Applications
In this work five novel polymer-cement composite formulations were prepared and evaluated as potential cementitious material alternatives to conventional wellbore cement. These cement composites were cured at 200 °C and their mechanical properties, including compressive strength, Young modulus, shear bond strength to steel casing, and self-healing and re-adhering (to steel) capability. Thermal stability was also evaluated by curing these cement materials at 200 °C for up to one month followed by determining their mineralogy and chemical composition by X-ray diffraction spectroscopy, 13C NMR, and total organic carbon. Permeability analysis was performed before and after healing a longitudinal fracture on unmodified cements and polymer-cement composites with the later showing lower (2nd/1st) permeability ratios with respect to base cements. Furthermore, a reduction in permeability of up to 80X on average with respect to their unmodified (base) cement, was observed in two polymer-cement formulations suggesting that the introduction of these polymers bring about self-healing capability. Two of the best performing polymer-cement composites were exposed to 30-day curing period at 200 °C showing that their self-healing capability is maintained. The composite stability is associated to the fact that the polymer material is stable as demonstrated by total organic carbon and NMR spectroscopy. These advanced polymer-cement composites with higher ductility and self-healing capability could be used as alternative wellbore cement materials for geothermal and fossil energy applications.
Chemical short-range order in derivative Cr–Ta–Ti–V–W high entropy alloys from the first-principles thermodynamic study
The development of high-entropy alloys (HEAs) focuses on exploring compositional regions in multi-component systems with all alloy elements in equal or near-equal atomic concentrations. Initially it was based on the main idea that high mixing configurational entropy contributions to the alloy free energy could promote the formation of a single solid solution phase. By using the ab-initio based Cluster Expansion (CE) Hamiltonian model constructed for the quinary bcc Cr–Ta–Ti–V–W system in combination with Monte Carlo (MC) simulations, we show that the phase stability and chemical short-range order (SRO) of the equiatomic quinary and five sub-quaternary systems, as well as their derivative alloys, can dramatically change the order–disorder transition temperatures (ODTT) as a function of alloy compositions. In particular, it has been found, that the equiatomic quaternary Ta–Ti–V–W and Cr–Ta–Ti–W alloys had the lowest order–disorder transition temperature (500 K) among all the analysed equiatomic compositions. In all investigated alloy systems, the strongest chemical ordering has been observed between Cr and V, which led to the conclusion that decreasing the concentration of either Cr or V might be beneficial in terms of decreasing the ODTT. It also predicts that increasing concentration of Ti significantly decreases the ODTT. Our analysis of chemical SRO as a function of alloy composition allows to understand the microstructure evolution of HEAs as a function of temperature in excellent agreement with available experimental observations. Importantly, our free energy of mixing and SRO calculations predict that the origin of precipitates formed by Cr- and V-rich in the sub-quaternary Cr–Ta–V–W system is driven by the thermodynamics. The modelling results are in an excellent agreement with experimental observation of Cr and V segregation in the W 0.38 Ta 0.36 Cr 0.15 V 0.11 alloy which in turns shows an exceptional radiation resistance.
Imprecise global sensitivity analysis using bayesian multimodel inference and importance sampling
Global Sensitivity Analysis (GSA) aims to understand the relative importance of uncertain input variables to model response. Conventional GSA involves calculating sensitivity (Sobol’) indices for a model with known model parameter distributions. However, model parameters are affected by aleatory and epistemic uncertainty, with the latter often caused by lack of data. In this paper, we propose a new framework to quantify uncertainty in probability model-form and model parameters resulting from small datasets and integrate these uncertainties into Sobol’ index estimates. First, the process establishes, through Bayesian multimodel inference, a set of candidate probability models and their associated probabilities. Imprecise Sobol’ indices are calculated from these probability models using an importance sampling reweighting approach. This results in probabilistic Sobol’ indices, whose distribution characterizes uncertainty in the sensitivity resulting from small dataset size. The imprecise Sobol’ indices thus provide a measure of confidence in the sensitivity estimate and, moreover, can be used to inform data collection efforts targeted to minimize the impact of uncertainties. Through an example studying the parameters of a Timoshenko beam, we show that these probabilistic Sobol’ indices converge to the true/deterministic Sobol’ indices as the dataset size increases and hence, distribution-form uncertainty reduces. The approach is then applied to assess the sensitivity of the out-of-plane properties of an E-glass fiber composite material to its constituent properties. This second example illustrates the approach for an important class of materials with wide-ranging applications when data may be lacking for some input parameters.
Uncertainty quantification for competing failure mechanisms in unidirectionally reinforced carbon–carbon composites
Microstructure-informed finite element models play a key role in the carbon–carbon composite design process. Variability in manufacturing process parameters and experimental limitations introduce model parameter uncertainty. This study quantifies the effect of model parameter uncertainty on transverse tensile fracture behavior and proposes a methodology to predict the failure mode based on competing microscale damage mechanisms. Finite element simulations incorporate fiber–matrix interface debonding with cohesive zones and matrix damage with a smeared crack band approach in a unidirectional carbon–carbon composite. Results from a variance-based global sensitivity analysis identifies interfacial and matrix damage parameters as the primary source of variability in fracture behavior. Sobol’ indices indicate that matrix and cohesive zone strengths contribute 94% of the variance in the effective ultimate stress. A local analysis elucidates the relationship between these constituent strength parameters and failure mode by estimating the probability of cohesive, matrix, and mixed-mode dominated failure. Based on the results for 4000 simulations, 93% exhibit mixed-mode or interfacial dominated failure, which underscores the crucial role of fiber–matrix interface debonding in the transverse tensile failure of carbon–carbon composites. These uncertainty quantification results facilitate more efficient model calibration and provide a framework for microstructure-informed failure predictions in the face of manufacturing-induced uncertainty.
Magnesium production by molten salt electrolysis with liquid tin cathode and multiple effect distillation
Low-cost clean primary production of magnesium metal is important for its use in many applications, from light-weight structural components to energy technologies. This work describes new experiments and cost and emissions analysis for a magnesium metal production process. The process combines molten salt electrolysis of MgO using MgF₂-CaF₂ electrolyte and a reactive liquid tin cathode, with gravity-driven multiple effect thermal system (G-METS) distillation to separate out the magnesium product, and re-use of the tin. Electrolysis experiments with carbon anodes showed current yield above 90%, while a yttria-stabilized zirconia solid oxide membrane (SOM) anode experiment showed 84% current yield. G-METS distillation is an important component of the envisioned process. It can potentially lower costs and energy use considerably compared with conventional magnesium distillation. Techno-economic analysis including detailed mass and energy balances shows that this electrolyte composition could lower costs by utilizing CaO, which is the primary impurity in MgO, as the Hall-Héroult process uses the sodium impurity in alumina. Analysis options include: raw material types (magnesite rock vs. brine or seawater), drying and calcining using electricity vs. natural gas, and carbon vs. SOM anode type. Using SOM inert anodes results in a cost premium around 10%–15%, mostly due to higher electrical energy usage resulting from membrane resistance, and reduces GHG emissions by approximately 1 kg CO₂/kg Mg product. Capital and operating cost estimates, and cradle to gate greenhouse gas (GHG) emissions analysis under several raw material and process technology scenarios, show comparable costs and emissions to those of aluminum production.
Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition
Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes ( 2 H and 18 O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.
Abundance of Major Cell Wall Components in Natural Variants and Pedigrees of Populus trichocarpa
The rapid analysis of biopolymers including lignin and sugars in lignocellulosic biomass cell walls is essential for the analysis of the large sample populations needed for identifying heritable genetic variation in biomass feedstocks for biofuels and bioproducts. In this study, we reported the analysis of cell wall lignin content, syringyl/guaiacyl (S/G) ratio, as well as glucose and xylose content by high-throughput pyrolysis-molecular beam mass spectrometry (py-MBMS) for >3,600 samples derived from hundreds of accessions of Populus trichocarpa from natural populations, as well as pedigrees constructed from 14 parents (7 × 7). Partial Least Squares (PLS) regression models were built from the samples of known sugar composition previously determined by hydrolysis followed by nuclear magnetic resonance (NMR) analysis. Key spectral features positively correlated with glucose content consisted of m/z 126, 98, and 69, among others, deriving from pyrolyzates such as hydroxymethylfurfural, maltol, and other sugar-derived species. Xylose content positively correlated primarily with many lignin-derived ions and to a lesser degree with m/z 114, deriving from a lactone produced from xylose pyrolysis. Models were capable of predicting glucose and xylose contents with an average error of less than 4%, and accuracy was significantly improved over previously used methods. The differences in the models constructed from the two sample sets varied in training sample number, but the genetic and compositional uniformity of the pedigree set could be a potential driver in the slightly better performance of that model in comparison with the natural variants. Broad-sense heritability of glucose and xylose composition using these data was 0.32 and 0.34, respectively. In summary, we have demonstrated the use of a single high-throughput method to predict sugar and lignin composition in thousands of poplar samples to estimate the heritability and phenotypic plasticity of traits necessary to develop optimized feedstocks for bioenergy applications.
Apolipoprotein A-I modulates HDL particle size in the absence of apolipoprotein A-II
Human high-density lipoproteins (HDLs) are a complex mixture of structurally related nanoparticles that perform distinct physiological functions. We previously showed that human HDL containing apolipoprotein A-I (APOA1) but not apolipoprotein A-II (APOA2), designated LpA-I, is composed primarily of two discretely sized populations. Here, we isolated these particles directly from human plasma by antibody affinity chromatography, separated them by high-resolution size-exclusion chromatography and performed a deep molecular characterization of each species. The large and small LpA-I populations were spherical with mean diameters of 109 Å and 91 Å, respectively. Unexpectedly, isotope dilution MS/MS with [ 15 N]-APOA1 in concert with quantitation of particle concentration by calibrated ion mobility analysis demonstrated that the large particles contained fewer APOA1 molecules than the small particles; the stoichiometries were 3.0 and 3.7 molecules of APOA1 per particle, respectively. MS/MS experiments showed that the protein cargo of large LpA-I particles was more diverse. Human HDL and isolated particles containing both APOA1 and APOA2 exhibit a much wider range and variation of particle sizes than LpA-I, indicating that APOA2 is likely the major contributor to HDL size heterogeneity. We propose a ratchet model based on the trefoil structure of APOA1 whereby the helical cage maintaining particle structure has two “settings”—large and small—that accounts for these findings. This understanding of the determinants of HDL particle size and protein cargo distribution serves as a basis for determining the roles of HDL subpopulations in metabolism and disease states.
Strain-Mediated Sn Incorporation and Segregation in Compositionally Graded Ge 1– x Sn x Epilayers Grown by MBE at Different Temperatures
We investigated the process of Sn incorporation and surface segregation for compositionally graded Ge 1–x Sn x epilayers grown on high-quality Ge (001) substrates. Here, the growth resulted in pseudomorphic GeSn layers with a ~6% maximal Sn fraction at a constant substrate temperature. The maximal fraction of Sn was increased to 9.0% when the growth temperature was continuously lowered while increasing the Sn flux. The analysis of surface droplets and SIMS profiles of elemental composition give evidence of Sn rejection during the growth, potentially associated with a critical energy of elastic strain. The intentional reduction of the coherent strain by decreasing the Sn flux near the sample surface has been shown to trap a higher fraction of Sn in the Ge 1–x Sn x layer and lower surface segregation. Our results demonstrate that strain relief by misfit dislocations in the compositionally graded layer is inhibited, which leads to Sn segregation. Specifically, the compressive strain in the graded Ge 1–x Sn x epilayer is effectively “zero” near the interface with the Ge substrate and increases up to about –1.5 × 10 –2 near the surface. Thus, although the nucleation of a dislocation may reduce the compressive strain for the top region of the epilayer, it is not beneficial for the bottom region.
A Versatile Fiber Coating Process for Efficient Fabrication of Multifunctional Composites
The objective of this research is to demonstrate the versatility of a dip coating process for the efficient integration of piezoelectric barium titanate (BaTiO3) microparticles on a wide variety of fibers to design passive self-sensing composites. The microparticles were deposited on glass, aramid, and basalt fiber weaves through the proposed dip coating technique. A computational framework is established to predict the deposition thickness on the fiber surfaces from the given microparticle concentration, size, coating velocity, and coating fluid viscosity. The deposition quality assessment was performed through scanning electron microscope imaging and subsequent image analysis. BaTiO3-coated fibers were directly used in composite preparation. After fabrication, the BaTiO3-enhanced composites were subjected to high-voltage poling. Finally, their passive self-sensing properties were characterized through experimental studies. The results show the adaptability of the proposed coating process to integrate BaTiO3 microparticles within different types of fiber-reinforced composites enabling passive self-sensing to attain subsurface damage characterization.