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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Counterions under a Surface-Adsorbed Cationic Surfactant Monolayer: Structure and Thermodynamics

The surface adsorption of ionic surfactants is fundamental for many widespread phenomena in life sciences and for a wide range of technological applications. However, direct atomic-resolution structural experimental studies of noncrystalline surface-adsorbed films are scarce. Thus, even the most central physical aspects of these films, such as their charge density, remain uncertain. Consequently, theoretical models based on contradicting assumptions as for the surface films' ionization are widely used for the description and prediction of surface thermodynamics. We employ X-ray reflectivity to obtain the angstrom Ångström-scale surface-normal structure of surface-adsorbed films of the cationic surfactant cetyltrimethylammonium bromide (CTAB) in aqueous solutions at several different temperatures and concentrations. In conjunction with published neutron reflectivity data, we determine the surface-normal charge distribution due to the dissociated surfactants' headgroups. The distribution appears to be inconsistent with the Gouy-Chapman model yet consistent with a compact Stern layer model of condensed counterions. Here, the experimental surfactant adsorption thermodynamics conforms well to classical, Langmuir and Kralchevsky, adsorption models. Furthermore, the Kralchevsky model correctly reproduces the observed condensation of counterions, allowing the values of the adsorption parameters to be resolved, based on the combination of the present data and the published surface tension measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine learning interatomic potential for silicon-nitride (Si 3 N 4 ) by active learning

Silicon nitride (Si 3 N 4 ) is an extensively used material in the automotive, aerospace, and semiconductor industries. However, its widespread use is in contrast to the scarce availability of reliable interatomic potentials that can be employed to study various aspects of this material on an atomistic scale, particularly its amorphous phase. In this work, we developed a machine learning interatomic potential, using an efficient active learning technique, combined with the Gaussian approximation potential (GAP) method. Our strategy is based on using an inexpensive empirical potential to generate an initial dataset of atomic configurations, for which energies and forces were recalculated with density functional theory (DFT); thereafter, a GAP was trained on these data and an iterative re-training algorithm was used to improve it by learning on-the-fly. When compared to DFT, our potential yielded a mean absolute error of 8 meV/atom in energy calculations for a variety of liquid and amorphous structures and a speed-up of molecular dynamics simulations by 3–4 orders of magnitude, while achieving a first-rate agreement with experimental results. Our potential is publicly available in an open-access repository.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Implementation of new mixture rules has a substantial impact on combustion predictions for H 2 and NH 3

Complex-forming reactions comprise a substantial fraction of all important combustion reactions and are central to combustion behavior. Despite being often called “pressure-dependent” reactions, their rate constants depend on not only the pressure but also the composition. While modern combustion codes allow arbitrarily high accuracy in treating pressure dependence, recent work has consistently demonstrated dramatic failures of essentially all available treatments of mixture dependence. In situations where mixture dependence is treated at all, it is inevitably treated through specification of pressure-dependent rate constants for a set of pure bath gases, which are then combined to estimate the rate constant in a mixture via a “mixture rule.” While there had been a generally unquestioning confidence in these mixture rules, they had, in reality, been scarcely tested until the last decade, when comparisons against master equation calculations revealed order-of-magnitude errors for important pressure-dependent reactions. New mixture rules, based on the reduced pressure, have recently been proposed and shown to reproduce master equation calculations for broad classes of complex-forming reactions very accurately. Here, in this work, we present an implementation of one such new mixture rule (“LMR-R”) in Cantera and then use it to enable simulations that use new high-accuracy ab initio data for individual bath gases (for the first time, since codes previously could not accommodate the complex bath gas dependence). Demonstrations focus on combustion of H 2 and NH 3 , where (1) high-accuracy ab initio data are available and (2) the impact is expected to be large due to the high fractions of efficient colliders (e.g., H 2 O and NH 3 ) in the burned and unburned gases. Indeed, we find the impact of this treatment to be substantial and may explain previous modeling difficulties for these important carbon-free fuels, particularly for NH 3 , whose extraordinarily high third-body efficiency (~20) is often omitted from kinetic models.

Ammonia↗
DOE OSTI2024

Personalized whole-brain neural mass models reveal combined Aβ and tau hyperexcitable influences in Alzheimer's disease.

Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, mechanistic evidence in humans remains scarce, requiring improved non-invasive techniques and integrative models. We introduce personalized AD computational models built on whole-brain Wilson-Cowan oscillators and incorporating resting-state functional MRI, amyloid-β (Aβ) and tau-PET from 132 individuals in the AD spectrum to evaluate the direct impact of toxic protein deposition on neuronal activity. This subject-specific approach uncovers key patho-mechanistic interactions, including synergistic Aβ and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP) and grey matter atrophy obtained through voxel-based morphometry. Furthermore, reconstructed EEG proxy quantities show the hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with Aβ-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental activation phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

RADAI: A Large-Scale Realistic Dataset for Radiation Detection Algorithm Development

Open, realistic datasets are essential for developing and benchmarking radiation detection algorithms, yet they remain scarce. The Radiological Anomaly Detection and Identification (RADAI) project was develop to create datasets that meet the training and testing needs for sophisticated radiation detection algorithms. The RADAI dataset is a large-scale synthetic resource that integrates high-fidelity Monte Carlo simulations with realistic urban scenarios to capture both background variability and source signatures. RADAI models construction-material NORM, people and vehicles, urban clutter, and dynamic environmental effects such as cosmic-ray and rain-induced transients, and they provide list-mode detector data with motion and response modeling suitable for algorithm training and evaluation. The RADAI project resulted in three publicly-released complementary datasets together with an online scoring portal for standardized performance assessment and an open software toolkit that supports data access, augmentation, model development, and evaluation. These resources enable reproducible comparisons across methods and promote rigorous studies at the scale required by contemporary machine learning. By grounding algorithm development in realistic, well-documented conditions, RADAI supports progress toward more robust detection, identification, and localization in complex urban environments.

Ghawaly, James M. [Division of Computer Science an↗
DOE OSTI2025

Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions

Predicting the binding affinity of ligands to protein pockets is key in the drug design pipeline. The flexibility of ligand-pocket motifs arises from a range of attractive and repulsive electronic interactions during binding. Accurately accounting for all interactions requires robust quantum-mechanical (QM) benchmarks, which are scarce for ligand-pocket systems. Additionally, disagreement between “gold standard” Coupled Cluster (CC) and Quantum Monte Carlo (QMC) methods casts doubt on many benchmarks for larger non-covalent systems. We introduce the “QUantum Interacting Dimer” (QUID) benchmark framework containing 170 non-covalent (non-)equilibrium systems modeling chemically and structurally diverse ligand-pocket motifs. Symmetry-adapted perturbation theory shows that QUID broadly covers non-covalent binding motifs and energetic contributions. Robust binding energies are obtained using complementary CC and QMC methods, achieving agreement of 0.5 kcal/mol. The benchmark data analysis reveals that several dispersion-inclusive density functional approximations provide accurate energy predictions, though their atomic van der Waals forces differ in magnitude and orientation. Contrarily, semiempirical methods and empirical force fields require improvements in capturing non-covalent interactions (NCIs) for out-of-equilibrium geometries. The wide span of NCIs, highly accurate interaction energies, and analysis of molecular properties take QUID beyond the “gold standard” for QM benchmarks of ligand-protein systems.

Puleva, Mirela [University of Luxembourg, Luxembou↗
DOE OSTI2020

Pore-scale evidence of ion exclusion during methane hydrate growth and evolution of hydrate pore-habit in sandy sediments

Hydrate-bearing sediments constitute complex hydrological systems, within which the pore habit of hydrate significantly affects all physical properties. However, there is scarce pore-scale evidence of the evolution of hydrate growth in porous media and the influence of water salinity in the micro-morphology of hydrate. In this paper, we used X-ray computed micro-tomography (CT) to monitor three experiments of methane hydrate growth in sand partially saturated with either NaBr or KI brine under excess-gas conditions. Gas, brine and hydrate are observed to co-exist at the pore-scale during the evolution of hydrate pore-habit towards three-phase equilibrium. The experiments and data analysis reveal that (1) hydrate forms as a porous medium mixed with inclusions of brine and gas, where hydrate and brine evolve gradually into separate phases as hydrate cages exclude salt ions, (2) hydrate growth mobilizes water over fairly long distances resulting in heterogeneous hydrate distribution, and (3) hydrate can exhibit interconnecting pore-habit at local hydrate saturations higher than ~50%, even if grown under excess-gas conditions. The results imply that hydrate micro-morphology and pore habit evolve during hydrate formation and, we suggest, so do the physical properties of hydrate-bearing sediments. Furthermore, our findings provide new insights into the micro-morphology of hydrate and evolution of the properties of hydrate-bearing sediments synthesized in the laboratory and in natural systems.

Clathrate hydrate↗
DOE OSTI2025

Tomographic assessment of peening for metallurgical bonding in cold spray

It is widely asserted that peening densifies cold spray coatings and improves particle–particle and particle–substrate bonding, yet systematic studies analyzing the evolution of bonding due to peening particle strikes are scarce. We explore peening at the level of individual particle impacts, by creating two-particle Cu-on-Cu stacks using laser-induced particle impact testing. Exploring impact velocities that range from the onset of bonding at the “critical velocity”, V cr , to its peak near 1.4⋅V cr , we perform full 3D tomography of the particle stacks to characterize the bonding state at the particle-substrate interface. Direct labeling of non-bonded patches and the total particle-substrate interface permits quantification and comparison of bonding in single impacts with bonding after subsequent impacts to reveal the origins of the peening effect. The data reveal a surprising finding: although a second impact directly atop the first particle increases the area of the particle-substrate interface (~40% area increase), there is no improvement in metallurgical bonding. Analysis of these data suggest that not only area expansion (and associated oxide rarefication), but also stress-induced extrusion of metal at that interface needs to occur to achieve metal-on-metal contact to see an enhancement in bonding. Furthermore, our analysis suggests that the subsequent impacting particle needs to have ~70% more kinetic energy to produce additional bonding; this implies that particle and velocity distributions within a spray may be helpful, and that peening may rely on unaligned impacts.

Bonding↗
DOE OSTI2022

Range shifts in a foundation sedge potentially induce large Arctic ecosystem carbon losses and gains

Foundation species have disproportionately large impacts on ecosystem structure and function. As a result, future changes to their distribution may be important determinants of ecosystem carbon (C) cycling in a warmer world. We assessed the role of a foundation tussock sedge (Eriophorum vaginatum) as a climatically vulnerable C stock using field data, a machine learning ecological niche model, and an ensemble of terrestrial biosphere models (TBMs). Field data indicated that tussock density has decreased by ~0.97 tussocks per m2 over the past ~38 years on Alaska's North Slope from ~1981 to 2019. This declining trend is concerning because tussocks are a large Arctic C stock, which enhances soil organic layer C stocks by 6.9% on average and represents 745 Tg C across our study area. By 2100, we project that changes in tussock density may decrease the tussock C stock by 41% in regions where tussocks are currently abundant (e.g. -0.8 tussocks per m2 and -85 Tg C on the North Slope) and may increase the tussock C stock by 46% in regions where tussocks are currently scarce (e.g. +0.9 tussocks per m2 and +81 Tg C on Victoria Island). These climate-induced changes to the tussock C stock were comparable to, but sometimes opposite in sign, to vegetation C stock changes predicted by an ensemble of TBMs. Our results illustrate the important role of tussocks as a foundation species in determining future Arctic C stocks and highlight the need for better representation of this species in TBMs..

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

An experimental and kinetic modeling study of the pyrolysis of isoprene, a significant biogenic hydrocarbon in naturally occurring vegetation fires

Isoprene dominates the carbon flux emitted by vegetation and constitutes 40% of non-methane biogenic emissions worldwide. Despite pyrolysis experiments at temperatures above 1000 K showing a link between isoprene combustion and aromatic species formation, comprehensive mechanistic research on isoprene is scarce in the literature. Here, in this work, we carry out an experimental and theoretical study to build, for the first time, a chemical kinetic model describing isoprene pyrolysis. The formation of polycyclic aromatic hydrocarbon (PAH) precursor species, often observed in vegetation fire plumes, is partially explained by isoprene pyrolysis experiments and theoretical modeling. Molecular dynamics (MD) simulations unveil reaction pathways from allylic isoprenyl radicals to allene and cyclopentadiene (CPD) intermediates, two relevant species detected in the experiments. Rate constants for these identified pathways are calculated using variational transition state theory to update the kinetic model, which is validated against single-pulse shock tube (SPST), and jet-stirred reactor (JSR) experimental data in the temperature range of 850–1690 K. The kinetic model presents satisfactory agreement with the SPST experimental data, and a reaction pathway analysis shows that association of propargyl radicals results in benzene formation. The JSR pathway analysis also identifies the prominent reactions for CPD, benzene, styrene, and toluene formation. Our model does not reproduce the CPD experimental profiles, indicating that additional studies are necessary. Overall, our findings advance the understanding of isoprene pyrolysis and its related atmospheric pollutants in naturally occurring vegetation fires where smoldering and oxygen-deficient combustion processes are present.

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

Evaluating adaptive and predictive power management strategies for optimizing visualization performance on supercomputers

Power is becoming an increasingly scarce resource on the next generation of supercomputers, and should be used wisely to improve overall performance. One strategy for improving power usage is hardware overprovisioning, i.e., systems with more nodes than can be run at full power simultaneously without exceeding the system-wide power limit. With this study, we compare two strategies for allocating power throughout an overprovisioned system – adaptation and prediction – in the context of visualization workloads. While adaptation has been suitable for workloads with more regular execution behaviors, it may not be as suitable on visualization workloads, since they can have variable execution behaviors. This study considers a total of 104 experiments, which vary the rendering workload, power budget, allocation strategy, and node concurrency, including tests processing data sets up to 1 billion cells and using up to 18,432 cores across 512 nodes. Overall, we find that prediction is a superior strategy for this use case, improving performance up to 27% compared to an adaptive strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Global protein turnover quantification in Escherichia coli reveals cytoplasmic recycling under nitrogen limitation

Protein turnover is critical for proteostasis, but turnover quantification is challenging, and even in well-studied E. coli, proteome-wide measurements remain scarce. Here, we quantify the turnover rates of ~3200 E. coli proteins under 13 conditions by combining heavy isotope labeling with complement reporter ion quantification and find that cytoplasmic proteins are recycled when nitrogen is limited. We use knockout experiments to assign substrates to the known cytoplasmic ATP-dependent proteases. Surprisingly, none of these proteases are responsible for the observed cytoplasmic protein degradation in nitrogen limitation, suggesting that a major proteolysis pathway in E. coli remains to be discovered. Lastly, we show that protein degradation rates are generally independent of cell division rates. Thus, we present broadly applicable technology for protein turnover measurements and provide a rich resource for protein half-lives and protease substrates in E. coli, complementary to genomics data, that will allow researchers to study the control of proteostasis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Crystallographic Study of Product Phases of Carbothermic Reduction and Nitridation of Hafnium Dioxide

Details of the carbothermic reduction/nitridation to synthesize hafnium nitride (HfN) and hafnium carbide (HfC) are scarce in the literature. Therefore, this current study was carried out to evaluate two pathways for synthesizing these two refractory materials: direct nitridation and carbothermic reduction/nitridation. Two mixtures of hafnium dioxide and carbon with C/HfO 2 molar ratios of 2.15 and 3.1 were nitridized directly using flowing nitrogen gas at elevated temperatures (1300–1700 °C). The 3.1 C/HfO 2 molar ratio mixture was also carbothermically reduced under flowing argon gas to synthesize HfC, which was converted into HfN by introducing a nitridation step under both N 2(g ) and N 2(g) -10% H 2(g) . X-ray diffraction results showed the formation of HfN at 1300 and 1400 °C and HfC 1–y N y at ≥1400 °C under direct nitridation of samples using a C/HfO 2 molar ratio of 2.15. These phase analysis data together with lower lattice strain and greater crystallite sizes of HfC 1–y N y that formed at higher temperatures suggested that the HfC 1–y N y phase is preferred over HfN at those temperatures. Carbothermic reduction of 3.1 C/HfO 2 molar ratio samples under an inert atmosphere produced single-phased HfC with no significant levels of dissolved oxygen. In conclusion, carbothermic reduction nitridation made two phases of different carbon levels (HfC 1–y N y and HfC 1–y 'N y ', where y' < y), while direct nitridation produced a single HfC 1–y N y phase under both N 2 and N 2 -10% H 2 cover gas environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The timing of lunar solidification and mantle overturn recorded in ferroan anorthosite 62237

Ferroan anorthosite suite (FAS) rocks are widely interpreted to represent primordial lunar crust. Despite their importance in pinpointing the timing of lunar crust formation, robust chronological investigations for this rock type are scarce. Here, we report the Ar-Ar, Rb-Sr, and Sm-Nd isotopic systematics for the FAS troctolitic anorthosite 62237. The Ar-Ar isotopic system has been reset by a thermal event at 3710 ± 48 Ma, and the Rb-Sr isotopic systematics has been disturbed such that a Rb-Sr isochron age cannot be determined. However, an internal isochron for the Sm-Nd isotopic system has yielded an age of 4350 ± 73 Ma (MSWD = 2.0) with an initial ε 143 Nd CHUR of -0.53 ± 0.26. The mineral and whole-rock fractions of 62237 plot on the same internal isochron as FAS sample 60025. The combined datasets define an age of 4372 ± 35 Ma (MSWD = 4.0) with an initial ε 143 Nd CHUR of -0.17 ± 0.22. Literature Sm-Nd data for FAS and Mg-suite whole-rocks also plot on the 60025-62237 isochron. The coherence of data from both FAS and Mg-suite rocks examined thus far suggests that both rock suites formed contemporaneously from identical, or nearly identical, sources. In addition, the concordance of FAS and Mg-suite ages suggests that primordial crust solidification either involved both magmatic suites, or that Mg-suite magmatism was contemporaneous with FAS magmatism within resolution of the Sm-Nd chronometer. The ages for FAS and Mg-suite also coincide with the formation ages of the mare basalt source regions and urKREEP. Ferroan anorthosite suite rocks and urKREEP are thought to represent primordial LMO solidification products, whereas Mg-suite and the mare basalt source regions are argued to represent mixtures of various LMO crystallization products that were formed during density-driven overturn of the LMO. The concordance of ages implies that the 4372 ± 35 Ma Sm-Nd isochron records the age of mantle overturn, and that overturn occurred during, or shortly after, solidification of the LMO.

58 GEOSCIENCES↗
DOE OSTI2022

Construction and characterization of a genome-scale ordered mutant collection of Bacteroides thetaiotaomicron

Ordered transposon-insertion collections, in which specific transposon-insertion mutants are stored as monocultures in a genome-scale collection, represent a promising tool for genetic dissection of human gut microbiota members. However, publicly available collections are scarce and the construction methodology remains in early stages of development. Here, we describe the assembly of a genome-scale ordered collection of transposon-insertion mutants in the model gut anaerobe Bacteroides thetaiotaomicron VPI-5482 that we created as a resource for the research community. We used flow cytometry to sort single cells from a pooled library, located mutants within this initial progenitor collection by applying a pooling strategy with barcode sequencing, and re-arrayed specific mutants to create a condensed collection with single-insertion strains covering >2500 genes. To demonstrate the potential of the condensed collection for phenotypic screening, we analyzed growth dynamics and cell morphology. We identified both growth defects and altered cell shape in mutants disrupting sphingolipid synthesis and thiamine scavenging. Finally, we analyzed the process of assembling the B. theta condensed collection to identify inefficiencies that limited coverage. We demonstrate as part of this analysis that the process of assembling an ordered collection can be accurately modeled using barcode sequencing data. We expect that utilization of this ordered collection will accelerate research into B. theta physiology and that lessons learned while assembling the collection will inform future efforts to assemble ordered mutant collections for an increasing number of gut microbiota members.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Active analog tuning of the phase of light in the visible regime by bismuth-based metamaterials

The active and analog tuning of the phase of light by metamaterials is needed to boost the switching performance of photonic devices. However, demonstrations of this type of tuning in the pivotal visible spectral region are still scarce. Herein, we report the active analog tuning of the phase of visible light reflected by a bismuth (Bi)-based metamaterial, enabled by a reversible solid-liquid transition. This metamaterial, fabricated by following a lithography-free approach, consists of twodimensional assemblies of polydisperse plasmonic Bi nanostructures embedded in a refractory and transparent aluminum oxide matrix. The analog tuning of the phase is achieved by the controlled heating of the metamaterial to melt a fraction of the nanostructures. A maximum tuning of 320° (1.8 π) is observed upon the complete melting of the nanostructures at 230°C. This tuning is reversible by cooling to 25°C. In addition, it presents a wide hysteretic character due to liquid Bi undercooling. This enables the phase achieved by this analog approach to remain stable over a broad temperature range upon cooling and until resolidification occurs around 100°C. Therefore, Bi-based metamaterials are endowed with analog optical memory capabilities, which are appealing for a wide range of applications, including optical data storage with enhanced information density or bistable photonic switching with a tunable “on” state.

36 MATERIALS SCIENCE↗
DOE OSTI2023

One Health Approach to Tackle Microbial Contamination on Poultries—A Systematic Review

This study reports the search of available data published regarding microbial occupational exposure assessment in poultries, following the PRISMA methodology. Air collection through filtration was the most frequently used. The most commonly used passive sampling method was material collection such as dust, cages, soils, sediment, and wastewater. Regarding assays applied, the majority of studies comprised culture-based methods, but molecular tools were also frequently used. Screening for antimicrobial susceptibility was performed only for bacteria; cytotoxicity, virological and serological assays were also performed. Most of the selected studies focused on bacteria, although fungi, endotoxins, and β-glucans were also assessed. The only study concerning fungi and mycotoxins reported the carcinogenic mycotoxin AFB1. This study gives a comprehensive overview of microbial contamination in the poultry industry, emphasizing this setting as a potential reservoir of microbial pathogens threatening human, animal, and environmental health. Additionally, this research helps to provide a sampling and analysis protocol proposal to evaluate the microbiological contamination in these facilities. Few articles were found reporting fungal contamination in poultry farms worldwide. In addition, information concerning fungal resistance profile and mycotoxin contamination remain scarce. Overall, a One Health approach should be incorporated in exposure assessments and the knowledge gaps identified in this paper should be addressed in further research.

Gomes, Bianca (ORCID:0000000262646072)↗
DOE OSTI2020

Predictions of the glass transition temperature and viscosity of organic aerosols from volatility distributions

Abstract. Volatility and viscosity are important properties of organic aerosols (OA), affecting aerosol processes such as formation, evolution, and partitioning of OA. Volatility distributions of ambient OA particles have often been measured, while viscosity measurements are scarce. We have previously developed a method to estimate the glass transition temperature (Tg) of an organic compound containing carbon, hydrogen, and oxygen. Based on analysis of over 2400 organic compounds including oxygenated organic compounds, as well as nitrogen- and sulfur-containing organic compounds, we extend this method to include nitrogen- and sulfur-containing compounds based on elemental composition. In addition, parameterizations are developed to predict Tg as a function of volatility and the atomic oxygen-to-carbon ratio based on a negative correlation between Tg and volatility. This prediction method of Tg is applied to ambient observations of volatility distributions at 11 field sites. The predicted Tg values of OA under dry conditions vary mainly from 290 to 339 K and the predicted viscosities are consistent with the results of ambient particle-phase-state measurements in the southeastern US and the Amazonian rain forest. Reducing the uncertainties in measured volatility distributions would improve predictions of viscosity, especially at low relative humidity. We also predict the Tg of OA components identified via positive matrix factorization of aerosol mass spectrometer (AMS) data. The predicted viscosity of oxidized OA is consistent with previously reported viscosity of secondary organic aerosols (SOA) derived from α-pinene, toluene, isoprene epoxydiol (IEPOX), and diesel fuel. Comparison of the predicted viscosity based on the observed volatility distributions with the viscosity simulated by a chemical transport model implies that missing low volatility compounds in a global model can lead to underestimation of OA viscosity at some sites. The relation between volatility and viscosity can be applied in the molecular corridor or volatility basis set approaches to improve OA simulations in chemical transport models by consideration of effects of particle viscosity in OA formation and evolution.

54 ENVIRONMENTAL SCIENCES↗