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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High temperature and pressure regime soot: Physical, optical and chemical signatures from high explosive detonations

Submicron particles are formed during the detonation and deflagration of carbon-rich energetic materials known as high explosives (HE) that can be lofted and transported long distances in the atmosphere. Particles include refractory species like soot, a.k.a. black carbon (BC), and have impacts to climate and human health at local, regional, and global scales. Here, we contrast detonation soot aerosol physical, optical and chemical properties that are formed more efficiently in terms of emission factors and ratios with traditional combustion sources from biomass burning and fossil fuels. We utilize ambient aerosol techniques for the first time to identify soot from two well-known HE's (1) Comp B-3: 40% trinitrotoluene (TNT) and 60% 1,3,5-trinitro-1,3,5-triazinane (RDX) and (2) PBX 9501: 95% 1,3,5,7-tetranitro-1,3,5,7-tetrazoctane (HMX) with 2.5% Estane and a 2.5% mixture of bis(2,2-dinitropropyl)-acetal (BDNPA) and bis(2,2-dinitropropyl)-formal (BDNPF). Size distributions, optical and chemical properties are presented. Aggregate soot had small mobility diameters (<100 nm) and unique optical and chemical signatures depending on the explosive composite composition, detonation atmosphere (e.g., air, argon) and pressure. Single-scattering albedos (SSA) from 0.3 to 0.8 and absorption Angstrom exponents (AAE) from 0.5 to 2.0 measured across 405–870 nm wavelengths depended on the explosive composite composition and detonation conditions. Comp B-3 detonation soot was similar to other combustion soot sources, whereas PBX 9501 soot had high scattering relative to absorption. Carbonaceous chemical signatures included the presence of fullerenes that were distinct from ambient and laboratory-generated soot surrogates and the detection of trace metals, including aluminum, copper, iron, and zinc.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Time and space resolved diagnostics for plasma thermal-chemical instability of fuel oxidation in nanosecond plasma discharges

Here, an instability in a nanosecond pulsed dielectric barrier discharge plasma occurring in methane–oxygen–argon mixtures is experimentally observed and measured by 1D time-resolved in situ electric field measurements. This instability, which seems to be created by the positive feedback between plasma kinetics and plasma-assisted low temperature fuel oxidation, is studied using electric field induced second harmonic generation and direct ICCD imaging. The rapid formation of streamers from an originally uniform discharge appears to be caused by the chemical kinetics of plasma-assisted low temperature methane oxidation, which may be resulting in a new type of plasma instability: a thermal-chemical instability. The results also revealed that the occurrence of this possible thermal-chemical instability in a reactive flow drastically changes the plasma properties by forming multiple secondary discharges and possibly leads to micron-sized non-uniform electric distributions. Single shot uncalibrated measurements of the electric field of the micron sized streamers appears to show much greater strengths than the average electric field. Furthermore, one-dimensional data analysis shows the positive feedback loop between the streamers and the low temperature plasma assisted oxidation chemistry in the plasma thermal-chemical instability. The present finding advances the understanding plasma instability growth and provides a new way to control plasma uniformity in plasma-assisted combustion and plasma fuel reforming.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The Impact of Nuclear Physics Uncertainties on Galactic Chemical Evolution Predictions

Modeling the evolution of the elements in the Milky Way is a multidisciplinary and challenging task. In addition to simulating the ~13 billion years evolution of our Galaxy, chemical evolution simulations must keep track of the elements synthesized and ejected from every astrophysical site of interest (e.g., supernova, compact binary merger). The elemental abundances of such ejecta, which are a fundamental input for chemical evolution codes, are usually taken from theoretical nucleosynthesis calculations performed by the nuclear astrophysics community. Therefore, almost all chemical evolution predictions rely on the nuclear physics behind those calculations. In this proceedings article, we highlight the impact of nuclear physics uncertainties on galactic chemical evolution predictions. We demonstrate that nuclear physics and galactic evolution uncertainties both have a significant impact on interpreting the origin of neutron-capture elements in our Solar System. Those results serve as a motivation to create and maintain collaborations between the fields of nuclear astrophysics and galaxy evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A GALAH view of the chemical homogeneity and ages of stellar strings identified in Gaia

ABSTRACT The advent of Gaia has led to the discovery of nearly 300 elongated stellar associations (called ‘strings’) spanning hundreds of parsecs in length and mere tens of parsecs in width. These newfound populations present an excellent laboratory for studying the assembly process of the Milky Way thin disc. In this work, we use data from GALAH DR3 to investigate the chemical distributions and ages of 18 newfound stellar populations, 10 of which are strings and 8 of which are compact in morphology. We estimate the intrinsic abundance dispersions in [X/H] of each population and compare them with those of both their local fields and the open cluster (OC) M 67. We find that all but one of these groups are more chemically homogeneous than their local fields. Furthermore, half of the strings, namely Theias 139, 169, 216, 303, and 309, have intrinsic [X/H] dispersions that range between 0.01 and 0.07 dex in most elements, equivalent to those of many OCs. These results provide important new observational constraints on star formation and the chemical homogeneity of the local interstellar medium (ISM). We investigate each population’s Li and chemical clock abundances (e.g. [Sc/Ba], [Ca/Ba], [Ti/Ba], and [Mg/Y]) and find that the ages suggested by chemistry generally support the isochronal ages in all but six structures. This work highlights the unique advantages that chemistry holds in the study of kinematically related stellar groups.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The chemical characterization of halo substructure in the Milky Way based on APOGEE

Galactic haloes in a Λ-CDM universe are predicted to host today a swarm of debris resulting from cannibalized dwarf galaxies. The chemodynamical information recorded in their stellar populations helps elucidate their nature, constraining the assembly history of the Galaxy. Using data from APOGEE and Gaia , we examine the chemical properties of various halo substructures, considering elements that sample various nucleosynthetic pathways. The systems studied are Heracles, Gaia -Enceladus/Sausage (GES), the Helmi stream, Sequoia, Thamnos, Aleph, LMS-1, Arjuna, I’itoi, Nyx, Icarus, and Pontus. Abundance patterns of all substructures are cross-compared in a statistically robust fashion. Our main findings include: (i) the chemical properties of most substructures studied match qualitatively those of dwarf Milky Way satellites, such as the Sagittarius dSph. Exceptions are Nyx and Aleph, which are chemically similar to disc stars, implying that these substructures were likely formed in situ ; (ii) Heracles differs chemically from in situ populations such as Aurora and its inner halo counterparts in a statistically significant way. The differences suggest that the star formation rate was lower in Heracles than in the early Milky Way; (iii) the chemistry of Arjuna, LMS-1, and I’itoi is indistinguishable from that of GES, suggesting a possible common origin; (iv) all three Sequoia samples studied are qualitatively similar. However, only two of those samples present chemistry that is consistent with GES in a statistically significant fashion; (v) the abundance patterns of the Helmi stream and Thamnos are different from all other halo substructures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A comparative analysis of the chemical compositions of Gaia-Enceladus/Sausage and Milky Way satellites using APOGEE

ABSTRACT We use data from the 17th data release of the Apache Point Observatory Galactic Evolution Experiment (APOGEE 2) to contrast the chemical composition of the recently discovered Gaia Enceladus/Sausage system (GE/S) to those of 10 Milky Way (MW) dwarf satellite galaxies: LMC, SMC, Boötes I, Carina, Draco, Fornax, Sagittarius, Sculptor, Sextans, and Ursa Minor. Our main focus is on the distributions of the stellar populations of those systems in the [Mg/Fe]–[Fe/H] and [Mg/Mn]–[Al/Fe] planes, which are commonly employed in the literature for chemical diagnosis and where dwarf galaxies can be distinguished from in situ populations. We show that, unlike MW satellites, a GE/S sample defined purely on the basis of orbital parameters falls almost entirely within the locus of ‘accreted’ stellar populations in chemical space, which is likely caused by an early quenching of star formation in GE/S. Due to a more protracted history of star formation, stars in the metal-rich end of the MW satellite populations are characterized by lower [Mg/Mn] than those of their GE/S counterparts. The chemical compositions of GE/S stars are consistent with a higher early star formation rate (SFR) than MW satellites of comparable and even higher mass, suggesting that star formation in the early universe was strongly influenced by other parameters in addition to mass. We find that the direction of the metallicity gradient in the [Mg/Mn]–[Al/Fe] plane of dwarf galaxies is an indicator of the early SFR of the system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems

Abstract Understanding the nanoscale chemical speciation of heterogeneous systems in their native environment is critical for several disciplines such as life and environmental sciences, biogeochemistry, and materials science. Synchrotron-based X-ray spectromicroscopy tools are widely used to understand the chemistry and morphology of complex material systems owing to their high penetration depth and sensitivity. The multidimensional (4D+) structure of spectromicroscopy data poses visualization and data-reduction challenges. This paper reports the strategies for the visualization and analysis of spectromicroscopy data. We created a new graphical user interface and data analysis platform named XMIDAS (X-ray multimodal image data analysis software) to visualize spectromicroscopy data from both image and spectrum representations. The interactive data analysis toolkit combined conventional analysis methods with well-established machine learning classification algorithms (e.g. nonnegative matrix factorization) for data reduction. The data visualization and analysis methodologies were then defined and optimized using a model particle aggregate with known chemical composition. Nanoprobe-based X-ray fluorescence (nano-XRF) and X-ray absorption near edge structure (nano-XANES) spectromicroscopy techniques were used to probe elemental and chemical state information of the aggregate sample. We illustrated the complete chemical speciation methodology of the model particle by using XMIDAS. Next, we demonstrated the application of this approach in detecting and characterizing nanoparticles associated with alveolar macrophages. Our multimodal approach combining nano-XRF, nano-XANES, and differential phase-contrast imaging efficiently visualizes the chemistry of localized nanostructure with the morphology. We believe that the optimized data-reduction strategies and tool development will facilitate the analysis of complex biological and environmental samples using X-ray spectromicroscopy techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-resolution chemical patterns from negative tone resists for the integration of extreme ultraviolet patterns of metal-oxide resists with directed self-assembly of block copolymers

Extreme ultraviolet (EUV) lithography faces significant challenges in designing suitable resist materials that can provide adequate precision, while maintaining economically viable throughput. These challenges in resist materials have led to printing failures and high roughness in EUV patterns, compromising the performance of semiconductor devices. Integrating directed self-assembly (DSA) of block copolymers (BCPs) with EUV lithography offers a promising solution because, while the BCPs register to the EUV-defined chemical guiding pattern, the thermodynamically determined structures of the BCPs automatically rectify defects and roughness in the EUV pattern. Despite the superior resolution of metal-oxide EUV resists (MORs), their application to DSA is limited by the difficulty in converting them into chemical patterns that allow effective transfer of the rectified patterns of DSA films into inorganic materials. To address this challenge, this study introduces a novel strategy for fabricating chemical patterns using hydrogen silsesquioxane (HSQ), a high-resolution negative tone inorganic resist, as a model system for MORs. Initially, a sacrificial Cr pattern is generated from HSQ patterns via reactive ion etching. The sacrificial Cr pattern is converted into a chemical pattern by first grafting a water-soluble polyethylene oxide brush onto the substrate, then wet etching the Cr, and finally grafting nonpolar polystyrene brushes. Assembling polystyrene-block-poly(methyl methacrylate) on these patterns results in structures oriented and registered with the underlying pattern, achieving 24 nm full-pitch resolutions. In conclusion, this approach has the potential to integrate MOR patterns into the DSA process, thereby enabling the generation of high-quality sub-10 nm patterns with high-χ BCPs.

Atomic force microscopy↗
DOE OSTI2024

High-precision chemical quantum sensing in flowing monodisperse microdroplets

A method is presented for high-precision chemical detection that integrates quantum sensing with droplet microfluidics. Using nanodiamonds (ND) with fluorescent nitrogen-vacancy (NV) centers as quantum sensors, rapidly flowing microdroplets containing analyte molecules are analyzed. A noise-suppressed mode of optically detected magnetic resonance is enabled by pairing controllable flow with microwave control of NV electronic spins, to detect analyte-induced signals of a few hundredths of a percent of the ND fluorescence. Using this method, paramagnetic ions in droplets are detected with low limit-of-detection using small analyte volumes, with exceptional measurement stability over >10 3 s. In addition, these droplets are used as microconfinement chambers by co-encapsulating ND quantum sensors with various analytes such as single cells, suggesting wide-ranging applications including single-cell metabolomics and real-time intracellular measurements from bioreactors. Important advances are enabled by this work, including portable chemical testing devices, amplification-free chemical assays, and chemical imaging tools for probing reactions within microenvironments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Countering the future chemical weapons threat

After decades of difficult negotiations, the Chemical Weapons Convention (CWC) was adopted in 1993 and entered into force on 29 April 1997, banning the development, production, stockpiling, transfer, and use of chemical weapons (CW). As the CWC celebrates the 25th anniversary of its entry into force, it can document considerable success, much of it attributed to the CWC implementing body—the Organisation for the Prohibition of Chemical Weapons (OPCW). However, facing a volatile international security environment and an everchanging chemical industry, the OPCW must transform to meet its mission and remain an exemplar for multilateralism. As the next CWC review conference approaches in 2023, a next-generation OPCW 2.0 can be effective and credible only if it reinforces international norms against CW, anticipates future challenges posed by advancements in science and technology (S&T), incorporates more qualitative elements into the verification and compliance system, and keeps pace with technological change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Chemical Heterogeneity in PAN/LLZTO Composite Electrolytes by Synchrotron Imaging

Solid polymer/ceramic composite electrolytes are promising for batteries due to their attractive thermal stability and mechanical properties. Chemical heterogeneity in solid composite electrolytes, such as the inhomogeneity in the ceramic phase and salt distribution at the polymer/ceramic interface, is critical to the performance of solid composite electrolytes. However, such heterogeneity has not been well understood yet. In this work, we use Synchrotron-based X-ray fluorescence imaging (XRF) and Transmission X-ray Microscope (TXM) to image nanoscale chemical heterogeneity in polyacrylamide/Li 7+x La 3 Zr 2–x Ta x O 12 (LLZTO) composite electrolytes and investigate the effects of lithium salt, salt concentration, and plasticizer. We find that LLZTO particles show strong inter- and intra-particles chemical heterogeneities, and the off-stoichiometry of Zr in an LLZTO particle is unlikely to be only balanced by Ta substitution. Moreover, statistical analysis suggests that LiI tends to accumulate at the ceramic/polymer interface at a low concentration of 5 wt%, but no such tendency was observed in samples with 10 wt% LiI. However, composite electrolytes with LiTFSI show interfacial accumulation at both 5 wt% and 10 wt%. Furthermore, this report provides insight into element distributions in solid composite electrolytes, and we hope further researches can shed light on the connections between chemical heterogeneity and ionic transport pathway inside.

36 MATERIALS SCIENCE↗
DOE OSTI2024

RAIS Preliminary Remediation Goals for Chemicals

Chemical preliminary remediation goals (PRGs) (https://rais.ornl.gov/cgibin/prg/PRG_search?select=chem) are calculated by selecting the applicable chemical(s) together with the applicable media, land use, and exposure route combination. If “site-specific” is selected as the PRG type, the following page will show the equations and exposure parameters used for deriving the PRGs, and some of the parameter values may be changed as necessary. If “default” is selected as the PRG type, the RAIS will proceed directly to the results page. Multiple chemicals can be selected. Results can be downloaded in .xlsx or .pdf formats. Additionally, the session inputs for the PRG calculator can be saved for future use and recalled by the PRG calculator. The derivation of the selected PRG and the applicable equations and exposure parameters can be reviewed in more detail using the RAIS Chemical PRG Calculator User Guide (https://rais.ornl.gov/tools/rais_chemical_prg_guide.html).

Manning, Karessa [Oak Ridge National Laboratory (O↗
DOE OSTI2020

Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets

Abstract The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F 1 score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g., > 28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.

Idakwo, Gabriel↗
DOE OSTI2022

Comparative metabolomic analysis reveals shared and unique chemical interactions in sponge holobionts

Sponges are ancient sessile metazoans, which form with their associated microbial symbionts a complex functional unit called a holobiont. Sponges are a rich source of chemical diversity; however, there is limited knowledge of which holobiont members produce certain metabolites and how they may contribute to chemical interactions. To address this issue, we applied non-targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography mass spectrometry (GC-MS) to either whole sponge tissue or fractionated microbial cells from six different, co-occurring sponge species. Several metabolites were commonly found or enriched in whole sponge tissue, supporting the notion that sponge cells produce them. These include 2-methylbutyryl-carnitine, hexanoyl-carnitine and various carbohydrates, which may be potential food sources for microorganisms, as well as the antagonistic compounds hymenialdisine and eicosatrienoic acid methyl ester. Metabolites that were mostly observed or enriched in microbial cells include the antioxidant didodecyl 3,3'-thiodipropionate, the antagonistic compounds docosatetraenoic acid, and immune-suppressor phenylethylamide. This suggests that these compounds are mainly produced by the microbial members in the sponge holobiont, and are potentially either involved in inter-microbial competitions or in defenses against intruding organisms. This study shows how different chemical functionality is compartmentalized between sponge hosts and their microbial symbionts and provides new insights into how chemical interactions underpin the function of sponge holobionts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densities for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A combined biological and chemical flue gas utilization system towards carbon dioxide capture from coal-fired power plants (Final Report)

Photosynthetic algal cultivation has been intensively studied for CO 2 capture and utilization for several decades. The footprint for using algae to capture CO 2 emitted from carbon-intensive industrial processes (power plants, cement plants, and fermentation processes) is extremely large, which creates serious technical and economic hurdles that must be cleared if algal technologies are to be commercially implemented. Fortunately, algal biomass is rich in proteins, carbohydrates, and lipids, providing a good chemical source for organic absorbents and other value-added chemical feedstocks. In particular, amino acids from algal protein can be used to generate amino acid salt solutions that have been proven to be effective for capturing CO 2 . In order to take advantage of both algal cultivation and biomass utilization, the goal of the proposed project is to develop a combined biological and chemical system for coal-fired power plants for sequestering CO 2 in biological absorbents and generating value-added products. This approach significantly reduces the land and energy footprint of CO 2 capture, and minimizes capital and operational expenses. Three specific objectives are targeted: 1) optimizing the growth of the selected algal strain to maximize biomass accumulation from the coal-fired flue gas; 2) developing a cascade biomass utilization to produce amino acid absorbents, polyurethanes, biodiesel, and methane; and 3) conducting techno-economic analysis (TEA) and life cycle assessment (LCA) of the proposed process. Three key technical outcomes were achieved: (1) With the selected robust algal strain and unique photobioreactor design, long-term culture stability can be extended, and algal biomass productivity reached 0.5 g dry biomass/L/day year-round at a biomass concentration of 1.2 g/L in the pilot photobioreactor; (2) The biomass utilization process led to complete utilization of the algal biomass to produce amino acid salt absorbent, polyurethanes, and methane; and (3) The combined biological and chemical flue gas utilization process concluded a technically and economically feasible commercial-scale system that completely captures CO 2 in coal-fired flue gas with greatly reduced energy consumption.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Aerosol Chemical Speciation Monitor (ACSM) Composition-Dependent Collection Efficiency (CDCE) Value-Added Product Report

Aerosol particles influence the Earth’s radiation balance directly by absorbing and scattering light and indirectly by influencing cloud formation, properties, and lifetimes. Measurements of aerosol particle optical properties, mass loading, size distributions, microphysical properties, cloud formation properties, and chemical composition are important for understanding the aerosol life cycle and for validating earth system models that predict these quantities. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Aerosol Observing System (AOS) is a highly instrumented platform that measures many of these aerosol properties in situ. The Aerodyne aerosol chemical speciation monitor (ACSM) is a baseline instrument deployed in the AOS. The ACSM provides a quantitative measurement of aerosol particle chemical composition for non-refractory (operationally defined as components that evaporate on the 600ºC vaporizer) aerosol components in real time. Standard output is the mass concentration of particulate organics, nitrate, sulfate, chloride, and ammonium. One well-known limitation to the accuracy of the ACSM data is in evaluating the fraction of the ambient aerosol particles that are detected by the instrument. This quantity is referred to as the collection efficiency (CE) and is often less than unity. This is attributed to particles rebounding after impaction onto the heated vaporizer rather than being trapped, volatilized rapidly, and detected. Other factors, such as divergence of the aerosol particle beam, may also impact CE, but the particle rebound effect is the dominant factor. Scientists will sometimes assume CE = 0.5 (i.e., one half of sampled particles are detected) for ambient particles collected during field missions. However, parameterizations have been developed that express CE as a function of the measured chemical composition, referred to as the composition-dependent collection efficiency (CDCE). The physical explanation for a CDCE, supported by laboratory studies, is that rebound from the vaporizer is a function of the particle phase, with liquid-like particles “sticking” to the vaporizer and solid-like or crystalline particles “bouncing” from the vaporizer. Thus, particles with compositions are liquids-like under the measurement conditions (e.g., certain organics, acidic particles, particles enriched in nitrate) have CE close to unity while crystalline or solid particles (e.g., deliquesced ammonium sulfate) have lower CE. This value-added product (VAP) implements the procedure described by Middlebrook et al. (2012) to correct the ACSM data for the composition-dependent collection efficiency. Applying this parameterization improves the accuracy of the ACSM data and brings them into better agreement with other co-located aerosol measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Models and Strategies for Optimal Demand Side Management in the Chemical Industries

Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed the U.S. electricity market. Economic and environmental benefits notwithstanding, the presence of renewables has increased variability and uncertainty on the supply side of the grid. Managing demand, rather than generation – a strategy referred to as “demand response (DR)” – is an attractive approach for mitigating this imbalance. DR efforts aim to reduce electricity usage during peak demand times, lessening stress on the grid. Industrial users are particularly attractive entities for DR participation since they present large, localized loads that can provide significant relief on grid demand and –unlike other large loads, such as buildings – are minimally dependent on human needs and preferences. In this project, we accomplished three main objectives. (1) We developed data-driven low-order DR scheduling-relevant dynamic models of chemical processes. Concurrently, we studied the formulation and solution of the associated optimal DR production scheduling problems. (a) A prototype air separation unit (ASU) model was used to generate simulated operating data for initial modeling efforts, which enabled the later use of industrial data for data-driven modeling. (b) We utilized Hammerstein-Wiener (HW) and Finite Step Response (FSR) models to represent nonlinear plant dynamics. (c) The HW models were linearized using exact linearization so they could potentially be embedded in power system models, which are formulated as mixed integer linear programs (MILPs). (d) We solved DR optimization problems under uncertainty and found that even naïve predictions of electricity price and product demand led to significant cost savings benefits. (2) Our DR scheduling optimization problem formulations are amenable to real-time solution. (a) We utilized Lagrangian Relaxation (LR) to efficiently solve the optimization problem by decoupling subproblems linked by complicating constraints. (b) We have achieved computation times for the 3-day DR scheduling problem of an ASU as low as 1.88 minutes. (3) Our representations of the DR behavior of chemical process as grid-level batteries were embedded in power system models. (a) For a small-scale grid, we found that incorporating the dynamics of the chemical plant in the optimal power flow calculations resulted in better resource management leading to up to 15% and 46% cost reduction for the grid and chemical plant operations, respectively, during periods of power line congestion. We have published several works dedicated to modeling and solving DR optimization problems from the user side. These were published in top peer-reviewed journals and are summarized in this report. The most recent work (and papers in preparation) considers DR scheduling from the grid side. Future efforts will consider networked plants (e.g., air separation units operating on a common pipeline) for DR participation, which is expected to amplify the capabilities of industrial DR participants to perform load-shifting. Our consideration of uncertainty in DR has inspired future directions in this area as well: we plan to develop multistage methods to fully account for the effects of uncertainty in DR scheduling.

24 POWER TRANSMISSION AND DISTRIBUTION↗