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Compositions and processes are disclosed for forming hydrophobic coatings and lubricant-infused surface coatings. Coatings may be applied to various substrates without prior chemical or temperature treatment of the substrates and over large and irregular surfaces. Coatings are self-healing, antifouling, and have enhanced lifetimes.
SF-23-026 This software computes effective transport properties of composite materials involving fibers and nanoparticles using a random-walk algorithm that efficiently scales to an arbitrary number of processes and cores. Effective transport properties (thermal, electrical) are key to bridge the microstructure of complex materials with its macroscopic behavior. Traditional approaches either use effective medium approximations (closed mathematical expressions that are approximation for certain conditions) or continuum simulation models such as finite element or finite volume, which require the generation of a mesh for each configuration explored. cowalker leverages the equivalence between laplacian or heat equation-based models and random walks to compute the asymptotic transport properties from an ensemble of first sojourn times of a random walker moving through the composite material. This allows us to directly define a composite material as a collection of particles and use algorithms developed for molecular dynamics to quickly compute the intersection of the walker with the different interfaces in the material. cowalker is developed in C++, and it relies on the GNU Scientific Library for random generation. cowalker is currently delivered as source code, so the GSL library is not included in cowalker's distribution. A more userfriendly version, cowalker.jl is currently in development and will be released as part of cowalker.
hashin_shtrikman_mp is a tool for composites designers who have desired composite properties in mind, but who do not yet have an underlying formulation. The library utilizes the tightest theoretical bounds on the effective properties of composite materials with unspecified microstructure – the Hashin-Shtrikman bounds – to identify candidate theoretical materials, find real materials that are close to the candidates, and determine the optimal volume fractions for each of the constituents in the resulting composite. Its features include (i) leveraging of materials in the Materials Project database, (ii) integration with the Materials Project API, (iii) use of genetic machine-learning, (iv) agnosticism to underlying microstructure, and (v) ultimate engineering application, make it a tool with much broader applications than its predecessors.
This project aimed to develop a predictive, artificial intelligence/machine learning-based model to identify glass compositions satisfying specified property requirements. Such a model would provide a systematic approach for narrowing down the nearly infinite range of possible compositions for glasses and minimize unnecessary experimental trial and error. A large empirical data set for training and testing the algorithm was obtained from the SciGlass database. It contains glass compositions and corresponding property data from a wide range of literature sources. However, the currently available form of this data, recently released under an open database license, is not conducive to easy querying and use. The data structure was deciphered and a customized parsing code developed to make this data more usable for the current and future work. Neural network models were developed and trained on viscosity data from the database and demonstrated potential for improving prediction accuracy over a traditional regression model.
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The U.S. Department of Energy’s (DOE) Office of River Protection (ORP) requested Pacific Northwest National Laboratory (PNNL) to support the River Protection Project vitrification in an effort to support operations upon completion of startup activities (DOE 2012). This work was performed under the PNNL project titled “ORP Glass Support Work.” One task of this project—Enhanced Hanford Waste Glass Models—is the subject of this report. A previous task focused on generating property-composition data and models for the Hanford site low-activity waste (LAW) glasses with lower waste loadings, which are relevant to the commissioning of the LAW vitrification facility. The current task has the long-term objective of expanding the Hanford site LAW glass database and property composition models for the balance of the Hanford site tank waste treatment and immobilization mission. During the balance of the mission, LAW glasses with higher waste loadings will be produced. This report presents the glass compositions and glass property data developed in Phase 2 of the enhanced Hanford LAW glass property data development effort. When this effort is complete, enhanced LAW glass property models will be developed. Section 1.1 summarizes the status of the LAW glass composition regions and waste loading constraints prior to the data development effort documented in this report. Section 1.2 summarizes the LAW Phase 2 glass composition region and test matrix. Section 1.3 documents the quality assurance program used in performing the work discussed in this report.
Abstract. Water-soluble organic carbon (WSOC) in the cryosphere has an important impact on the biogeochemistry cycling and snow–ice surface energy balance through changes in the surface albedo. This work reports on the chemical characterization of WSOC in 28 representative snowpack samples collected across a regional area of northern Xinjiang, northwestern China. We employed multimodal analytical chemistry techniques to investigate both bulk and molecular-level composition of WSOC and its optical properties, informing the follow-up radiative forcing (RF) modeling estimates. Based on the geographic differences and proximity of emission sources, the snowpack collection sites were grouped as urban/industrial (U), rural/remote (R), and soil-influenced (S) sites, for which average WSOC total mass loadings were measured as 1968 ± 953 ng g−1 (U), 885 ± 328 ng g−1 (R), and 2082 ± 1438 ng g−1 (S), respectively. The S sites showed the higher mass absorption coefficients at 365 nm (MAC365) of 0.94 ± 0.31 m2 g−1 compared to those of U and R sites (0.39 ± 0.11 m2 g−1 and 0.38 ± 0.12 m2 g−1, respectively). Bulk composition of WSOC in the snowpack samples and its basic source apportionment was inferred from the excitation–emission matrices and the parallel factor analysis featuring relative contributions of one protein-like (PRLIS) and two humic-like (HULIS-1 and HULIS-2) components with ratios specific to each of the S, U, and R sites. Additionally, a sample from site 120 showed unique pollutant concentrations and spectroscopic features remarkably different from all other U, R, and S samples. Molecular-level characterization of WSOC using high-resolution mass spectrometry (HRMS) provided further insights into chemical differences among four types of samples (U, R, S, and 120). Specifically, many reduced-sulfur-containing species with high degrees of unsaturation and aromaticity were uniquely identified in U samples, suggesting an anthropogenic source. Aliphatic/protein-like species showed the highest contribution in R samples, indicating their biogenic origin. The WSOC components from S samples showed high oxygenation and saturation levels. A few unique CHON and CHONS compounds with high unsaturation degree and molecular weight were detected in the 120 sample, which might be anthraquinone derivatives from plant debris. Modeling of the WSOC-induced RF values showed warming effects of 0.04 to 0.59 W m−2 among different groups of sites, which contribute up to 16 % of that caused by black carbon (BC), demonstrating the important influences of WSOC on the snow energy budget.
This report summarizes and analyzes the data collected on a fourth test matrix of 25 low-activity waste glass compositions intended to expand the composition-property database and to validate the Vienna et al. (2020) property-composition models. The 25 low-activity waste glass compositions were statistically designed to be within the composition region of the 2020 models. The analyses performed on these glasses include chemical composition (for target compositional verification), density, viscosity, electrical conductivity, crystal fraction, canister centerline cooling with crystal identification, product consistency test response, vapor hydration test response, and sulfur solubility. This report discusses the results obtained from these tests.
A laminate comprising a first ply, a second ply, and a plurality of interfacial modifiers arranged between opposing faces of the first ply and the second ply. A chemical composition of an interfacial modifier of the plurality of interfacial modifiers can be selected based on a resulting bond strength between the interfacial modifier of the plurality of interfacial modifiers and at least one of the first ply or the second ply. The plurality of interfacial modifiers can be arranged in a pattern to selectively modify toughness of a portion of the laminate based on the bond strength of the interfacial modifier of the plurality of interfacial modifiers.
(Part of the data collected for this work and discussed in this report was subject to data quality and bias issues. All the affected tests have subsequently been repeated, as directed by the Waste Treatment Plant Project, and new statistical analyses have been performed on the revised data set. A subsequently-issued report supercedes this report and describes the revised data, together with the revised composition-property models (Kot et al. 2004). The reader should refer to the new report for discussion of the revised data set and composition-property (TCLP cadmium release) models. None of the data for the spike glasses, designed for Case 1 and Case 2 COPCs testing, were affected by this issue.)
In this study, we evaluated the pelleting characteristics of southern yellow pine (SYP), switchgrass (SG), and their blends for thermochemical conversion processes, such as pyrolysis and gasification. Using a pilot-scale ring-die pellet mill, we specifically assessed the impact of blend moisture, length-to-diameter (L/D) ratio in the pellet die, and ratio of pine to SG on the physico-chemical properties of the resulting pellets. We found that an increase in pine content by 25–50% marginally affected the bulk density; however, it also led to an increase in calorific value by 7% and a decrease in ash content by 72%. A moisture content of 25% (wet basis) and an L/D ratio of 5 resulted in poor pellet durability at <90% and bulk density values of <500 kg/m 3 , but increasing the L/D ratio to 9 and lowering the moisture content to 20% (w.b.) improved the pellet durability to >90% and the bulk density to >500 kg/m 3 . Blends with ≥50% pine content resulted in lower energy consumption, while a lower L/D ratio resulted in higher pelleting energy. Based on these findings, we successfully demonstrated the high-moisture pelleting of 2.5 ton of pine top residues blended with SG at 60:40 and 50:50 ratios. The quality of the pellets was monitored off-line and at-line by near infrared (NIR) spectroscopy. Multivariate models constructed by combining the NIR data and the pelleting process variables could successfully predict the pine content (R 2 = 0.99), higher heating value (R 2 = 0.98), ash (R 2 = 0.95), durability (R 2 = 0.94), and bulk density (R 2 = 0.86) of the pellets. Thus, we established how blending and densification of SYP and SG biomass could improve feedstock specifications and that NIR spectroscopy can effectively monitor the pellet properties during the high-moisture pelleting process.
Novel compositionally complex borides, (Hf,Zr,Nb,Ti)B 2 and (Hf,Zr,Nb,Ti)B 2 ‒LaB 6 , were fabricated using spark plasma sintering process. (Hf,Zr,Nb,Ti)B 2 ‒LaB 6 exhibits a dual-phase microstructure, in which (Hf,Zr,Nb,Ti)B 2 is a primary phase with the hexagonal structure and LaB 6 is a secondary phase with a cubic structure. The mechanical properties of both (Hf,Zr,Nb,Ti)B 2 and (Hf,Zr,Nb,Ti)B 2 ‒LaB 6 are comparable, with a combination of high hardness and moderate fracture toughness. Thermal diffusivity and conductivity of (Hf,Zr,Nb,Ti)B 2 are much lower than the individual transition metal borides but are significantly increased by the addition of LaB 6 . Herein, it is implied that the thermal properties of boride ceramics can be controlled through the appropriate design of principal metal element compositions.
This SPP project with Novus was to investigate tribological properties of candidate ceramic composites. Systematic tribological bench tests and surface characterization were performed to determine composition-property relationships of several Zr-Ti-B based ceramic composites of different compositions. The lowest friction coefficient was measured with Ti0.6Zr0.4B2 composition tested in the hydraulic oil. Surface characterization suggests a formation of ZDDP tribofilm in the worn region when tested with a hydraulic oil. In all experiments, the ceramic composites did not have measurable wear, but caused material loss of the steel countersurface.
Abstract Intercropping (or polyculture) is gaining a lot of interest as it increases yields, allows better utilization of resources (water and nutrients), and provides better land management. The objective of this study is to understand the briquetting characteristics of woody and herbaceous biomass blends, which might result from intercropping. Tests were conducted on blends of lodgepole pine, switchgrass, and corn stover biomass using three hammer mill screen sizes (i.e., 4.8, 12.7, and 19.05 mm), three blend moistures (12%, 15%, and 18%, w.b.), and two‐ and three‐blend ratios (i.e., 1:1 and 1:1:1) using a pilot‐scale hydraulic briquette press. The briquette properties, such as unit and bulk density, durability rating after 5 days of storage (as they tend to attain stable density), and the energy consumption of the process were measured. The results indicate that moisture content of 12% and 15% (w.b.) and smaller hammer mill screen size of 4.8 mm increased the unit and bulk densities to >750 and >430 kg/m 3 and moisture content (15% and 18% (w.b.)) and hammer mill screen size of 12.7 and 19.05 mm produced briquettes with durability rating values of >95%. The higher moisture content of 18% (w.b.) and the hammer mill screen size of 12.7 and 19.05 mm had the highest energy consumption. The proximate and ultimate analysis showed that energy content improved by blending lodgepole pine with both corn stover and switchgrass and also showed increased calorific value and reduced ash content. Scanning electron microscopy micrographs showed interlocking of particles and cracks in the briquettes. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd
Generative modeling (GM) has been increasingly used for the inverse design and optimization of materials, yet its application to molten salt mixtures remains unexplored despite how a successful approach to the inverse design of molten salts would contribute to efficiently exploiting their customizability and unlocking their advantages in applications, such as energy production and energy storage. This work presents a workflow for the inverse design of molten salts with targeted density values, addressing the challenge of representing these complex mixtures in GM. A dataset of critically evaluated molten salt densities is used to train a variational autoencoder coupled with a predictive deep neural network, which then can be used to generate new molten salt compositions with desired density values. The effectiveness of the approach is demonstrated by designing mixtures with distinct densities and validating the predicted values using ab initio molecular dynamics simulations.
Abstract. Atmospheric high-viscosity organic particles (HVOPs) were observed in samples of ambient aerosols collected in April and May 2016 in the Southern Great Plains of the United States. These particles were apportioned as either airborne soil organic particles (ASOPs) or tar balls (TBs) from biomass burning based on spetro-microscopic imaging and assessments of meteorological records of smoke and precipitation data. Regardless of their apportionment, the number fractions of HVOPs were positively correlated (R2=0.85) with increased values of absorption Ångström exponent (AAE) measured in situ for ambient aerosol at the site. Extending this correlation to 100 % HVOPs yields an AAE of 2.6, similar to previous literature reports of the class of light-absorbing organic particles known as brown carbon (BrC). One out of the three samples investigated had a significant number of ASOPs, while the other two samples contained TBs. Although there are chemical similarities between ASOPs and TBs, they can be distinguished based on composition inferred from near-edge absorption X-ray fine structure (NEXAFS) spectroscopy. ASOPs were distinguished from TBs based on their average -COOH/C=C and -COOH/COH peak ratios, with ASOPs having lower ratios. NEXAFS spectra of filtered soil organic brine particles nebulized from field samples of standing water deposited after rain were consistent with ASOPs when laboratory particles were generated by bubble bursting at the air–organic brine interface. However, particles generated by nebulizing the bulk volume of soil organic brine had a particle composition different from ASOPs. These observations are consistent with the raindrop generation mechanism responsible for ASOP emissions in the area of study. In contrast, nebulized samples carry with them higher fractions of soil inorganics dissolved in the bulk volume of soil brine, which are not aerosolized by the raindrop mechanism. Our results support the bubble bursting mechanism of particle generation during rainfall resulting in the ejection of soil organics into the atmosphere. In addition, our results show that ASOPs may only be atmospherically relevant during times when suitable emission conditions are met.
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