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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Specific absorbed fractions for a revised series of the UF/NCI pediatric reference phantoms: internal photon sources

Assessment of radiation absorbed dose to internal organs of the body from the intake of radionuclides, or in the medical setting through the injection of radiopharmaceuticals, is generally performed based upon reference biokinetic models or patient imaging data, respectively. Biokinetic models estimate the time course of activity localized to source organs. The time-integration of these organ activity profiles are then scaled by the radionuclide S-value, which defines the absorbed dose to a target tissue per nuclear transformation in various source tissues. S-values are computed using established nuclear decay information (particle energies and yields), and a parameter termed the specific absorbed fraction (SAF). The SAF is the ratio of the absorbed fraction—fraction of particle energy emitted in the source tissue that is deposited in the target tissue—and the target organ mass. While values of the SAF may be computed using patient-specific or individual-specific anatomic models, they have been more widely available through the use of computational reference phantoms. In this study, we report on an extensive series of photon SAFs computed in a revised series of the University of Florida and the National Cancer Institute pediatric reference phantoms which have been modified to conform to the specifications embodied in the ICRP reference adult phantoms of Publication 110 (e.g. organs modeled, organ ID numbers, blood contribution to elemental compositions). Following phantom anatomical revisions, photon radiation transport simulations were performed using MCNPX v2.7 in each of the ten phantoms of the series—male and female newborn, 1 year old, 5 year old, 10 year old, and 15 year old—for 60 different tissues serving as source and/or target regions. A total of 25 photon energies were considered from 10 keV to 10 MeV along a logarithm energy grid. Detailed analyses were conducted of the relative statistical errors in the Monte Carlo target tissue energy deposition tallies at low photon energies and over all energies for source–target combinations at large intra-organ separation distances. Based on these analyses, various data smoothing algorithms were employed, including multi-point weighted data smoothing, and log–log interpolation at low energies (1 keV and 5 keV) using limiting SAF values based upon target organ mass to bound the interpolation interval. The final dataset is provided in a series of ten electronic supplemental files in MS Excel format. Finally, the results of this study were further used as the basis for assessing the radiative component of internal electron source SAFs as described in our companion paper (Schwarz et al 2021) for this same pediatric phantom series.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Specific absorbed fractions for a revised series of the UF/NCI pediatric reference phantoms: internal electron sources

Assessment of radiation absorbed dose to internal organs of the body from the intake of radionuclides, or in the medical setting through the injection of radiopharmaceuticals, is generally performed based upon reference biokinetic models or patient imaging data, respectively. Biokinetic models estimate the time course of activity localized to source organs. The time-integration of these organ activity profiles are then scaled by the radionuclide S-value, which defines the absorbed dose to a target tissue per nuclear transformation in various source tissues. S-values are computed using established nuclear decay information (particle energies and yields), and a parameter termed the specific absorbed fraction (SAF). The SAF is the ratio of the absorbed fraction—fraction of particle energy emitted in the source tissue that is deposited in the target tissue—and the target organ mass. While values of the SAF may be computed using patient-specific or individual-specific anatomic models, they have been more widely available through the use of computational reference phantoms. In this study, we report on an extensive series of photon SAFs computed in a revised series of the University of Florida and the National Cancer Institute pediatric reference phantoms which have been modified to conform to the specifications embodied in the ICRP reference adult phantoms of Publication 110 (e.g. organs modeled, organ ID numbers, blood contribution to elemental compositions). Following phantom anatomical revisions, photon radiation transport simulations were performed using MCNPX v2.7 in each of the ten phantoms of the series—male and female newborn, 1 year old, 5 year old, 10 year old, and 15 year old—for 60 different tissues serving as source and/or target regions. A total of 25 photon energies were considered from 10 keV to 10 MeV along a logarithm energy grid. Detailed analyses were conducted of the relative statistical errors in the Monte Carlo target tissue energy deposition tallies at low photon energies and over all energies for source–target combinations at large intra-organ separation distances. Based on these analyses, various data smoothing algorithms were employed, including multi-point weighted data smoothing, and log–log interpolation at low energies (1 keV and 5 keV) using limiting SAF values based upon target organ mass to bound the interpolation interval. The final dataset is provided in a series of ten electronic supplemental files in MS Excel format. Finally, the results of this study were further used as the basis for assessing the radiative component of internal electron source SAFs as described in our companion paper (Schwarz et al 2021) for this same pediatric phantom series.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Constraining the hadronic spectrum through QCD thermodynamics on the lattice

Fluctuations of conserved charges allow us to study the chemical composition of hadronic matter. A comparison between lattice simulations and the hadron resonance gas (HRG) model suggested the existence of missing strange resonances. To clarify this issue we calculate the partial pressures of mesons and baryons with different strangeness quantum numbers using lattice simulations in the confined phase of QCD. In order to make this calculation feasible, we perform simulations at imaginary strangeness chemical potentials. We systematically study the effect of different hadronic spectra on thermodynamic observables in the HRG model and compare to lattice QCD results. We show that, for each hadronic sector, the well-established states are not enough in order to have agreement with the lattice results. Additional states, either listed in the Particle Data Group booklet (PDG) but not well established, or predicted by the quark model (QM), are necessary in order to reproduce the lattice data. For mesons, it appears that the PDG and the quark model do not list enough strange mesons, or that, in this sector, interactions beyond those included in the HRG model are needed to reproduce the lattice QCD results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

The Misrepresentation of the Southern African Easterly Jet in Models and Its Implications for Aerosol, Clouds, and Precipitation Distributions

The southern African easterly jet (AEJ-S) is an important midtropospheric feature critical to understanding the tropical convective system over central Africa and the aerosol–cloud interactions over the southeast Atlantic Ocean. However, it remains unclear how well models represent the AEJ-S and its influence on aerosol transport, clouds, and precipitation distribution. Here, in this work, we use ground- and satellite-based observations and reanalysis datasets to assess the representation of AEJ-S in the Coupled Model Intercomparison Project phase 6 (CMIP6) models between September and October during the peak of midtropospheric winds, aerosol transport, clouds, and precipitation. We find that most CMIP6 models have difficulty accurately simulating the strength, position, and spatial distribution of the AEJ-S. Specifically, the AEJ-S is relatively weaker and at a slightly lower altitude in the ensemble of CMIP6 models than represented by observation and reanalysis datasets. To assess the influence of the misrepresented the AEJ-S on CMIP6-simulated aerosol, clouds, and precipitation distributions, we performed composite analyses using models with low and high biases based on the estimates of their midtropospheric easterly wind speed. We find that the misrepresentation of the AEJ-S in CMIP6 models is associated with the overestimation of clouds and precipitation over central Africa, the underestimation of clouds over the southeast Atlantic Ocean, and the limitation of aerosol transport over the continent or the deviation of its spatial distribution from the typical zonal transport over the Atlantic Ocean. Because aerosols, clouds, and precipitation are important components of the regional climate system, we conclude that accurate representation of the AEJ-S is essential over central Africa and the southeast Atlantic Ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Testing of Optimized Bubbler Configuration for HLW Melter

The principal objective of this work was to determine the glass production rate increase and ancillary effects of adding more bubbler outlets to the current WTP HLW melter baseline. This was accomplished through testing on the HLW Pilot Melter (DM1200) at VSL. The DM1200 unit was selected for these tests since it was used previously with several HLW waste streams including the four tank wastes proposed for initial processing at Hanford. This melter system was also used for the development and optimization of the present baseline WTP HLW bubbler configuration for the WTP HLW melter, as well as for MACT testing for both HLW and LAW. Specific objectives of these tests were to: Conduct DM1200 melter testing with the baseline WTP bubbling configuration and as augmented with additional bubblers. Conduct DM1200 melter testing to differentiate the effects of total bubbler air flow and bubbler distribution on glass production rate and cold cap formation. Collect melter operating data including processing rate, temperatures at a variety of locations within the melter plenum space, melt pool temperature, glass melt density, and melter pressure with the baseline WTP bubbling configuration and as augmented with additional bubblers. Collect melter exhaust samples to compare particulate carryover for different bubbler configurations. Analyze all collected data to determine the effects of adding more bubblers to the WTP HLW melter to inform decisions regarding future lid re-designs. The work used a high aluminum HLW stream composition defined by ORP, for which an appropriate simulant and high waste loading glass formulation were developed and have been previously processed on the DM1200.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Supplemental Characterization of Hanford LAW Phase 2, Outer Layer Matrix Glasses

Supplemental chemical composition data are provided for a series of simulated low-activity waste glasses designated as the Phase 2, Outer Layer study. The glasses were selected and fabricated by the Pacific Northwest National Laboratory in support of property/composition model development for operation of the Hanford Tank Waste Treatment and Immobilization Plant. Additional characterization of glasses in this series is described in SRNL-STI-2018-00150, “Sulfur Solubility Testing and Characterization of Hanford LAW Phase 2, Outer Layer Matrix Glasses.” The measured concentrations of SO 3 reported here are generally lower than those measured for the earlier versions of these sulfur saturated glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Data-driven learning of nonlocal models: from high-fidelity simulations to constitutive laws

We show that machine learning can improve the accuracy of simulations of stress waves in one-dimensional composite materials. We propose a data-driven technique to learn nonlocal constitutive laws for stress wave propagation models. The method is an optimization-based technique in which the nonlocal kernel function is approximated via Bernstein polynomials. The kernel, including both its functional form and parameters, is derived so that when used in a nonlocal solver, it generates solutions that closely match high-fidelity data. The optimal kernel therefore acts as a homogenized nonlocal continuum model that accurately reproduces wave motion in a smaller-scale, more detailed model that can include multiple materials. We apply this technique to wave propagation within a heterogeneous bar with a periodic microstructure. Several one-dimensional numerical tests illustrate the accuracy of our algorithm. The optimal kernel is demonstrated to reproduce high-fidelity data for a composite material in applications that are substantially different from the problems used as training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Compositional Variation Tests on DuraMelter 100 with LAW Sub-Envelope B1 Feed in Support of the LAW Pilot Melter, VSL-02R62N0-5, Rev. 0 (May 2003)

The principal objective of the DM100 tests was to demonstrate the robustness of the vitrification process with respect to feed and glass compositional changes resulting from variations in the simulant-to-additive ratio. The DM100-WV unit was selected for these tests; this melter was used for all of the Part B 1 tests on LAW Envelopes A, B, and C sulfate incorporation that were used to support the subsequent tests on the Pilot Melter as well as for the more recent LAW Sub-Envelope Al, A2, A3 and Cl tests. The same melter was selected for the present tests in order to maintain comparisons between the data sets. In addition, the somewhat smaller glass volume for the same melt surface area as compared to the DM100-BL melter means that more glass turnovers are achieved for a given test duration. The DM100 tests used nominal feed and feed with a 15% surplus in waste simulant. Previous Pilot Melter tests on Envelope A with 10% variations (but with sulfate removal) showed significant impacts on glass production rates that were ultimately corrected by adjustments in the sugar additions. Consequently, robustness with respect to throughput as well as sulfate tolerance are important motivations for these tests.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2025

Model form and sensitivity analysis of CALPHAD-based nucleation models in b-stabilized Ti alloys

Accurate prediction of α-phase nucleation and growth in β-stabilized titanium alloys is crucial for designing heat treatments to optimize mechanical properties in additively manufactured lightweight components. Ideally, predictions of nucleation and growth would incorporate both top-down observations of past experimental heat treatments and bottom-up modeling of phase transformations; however, the appropriate method of combining these information sources is not self-evident. Combining top-down and bottom-up information requires a unified form of model that can connect between spatiotemporal scales, as well as sets of fitting parameters that can be identified by each data source. The selection of which parameters to fit to which data source can be made based on expert opinion, or by performing a sensitivity analysis. In solid-solid nucleation, direct observation of the nucleation and growth process is challenging. Most data on the heat treatment-controlled phase transformations are not in-situ. To predict the process and outcome of the nucleation, growth and coarsening of precipitates, theoretical models of the nucleation pathway are used to bridge the gap. Many sources of uncertainty affect the modeling of this nucleation process. It can be influenced by small variations in the thermomechanical processing history, chemical composition, and initial microstructure. If molecular dynamics (MD) simulations are used to determine thermodynamic quantities and inform CALPHAD modeling, additional uncertainty can be introduced and accounted for using Bayesian methods. Top-down uncertainties require additional steps to quantify. The influence of nucleation model form on the sensitivity of predictions to input parameters and physical conditions is the focus of this study. Classical nucleation theory (CNT) allows modeling to formulate the nucleation as homogeneous or, more commonly, heterogeneous. Non-classical nucleation models are also increasingly explored as a means of reconciling top-down and bottom-up data. In this study, the sensitivity of the intragranular nucleation of α in a β-annealed, slow-cooled aging (BASCA) heat treatment of β-stabilized Ti5553 alloy is explored using CNT and both heterogeneous and homogeneous assumptions. The Kampmann-Wagner Numerical model of precipitate nucleation and growth is employed. Using open-source tools (pyCalphad and thermodynamic modeling of TiMo as a surrogate system, a sensitivity analysis is performed to measure variations in key parameters, including chemical driving force, interfacial energy, and diffusivity, as they relate to predictions of precipitate number density. The inclusion of top-down and bottom-up data in selection of nucleation model form is discussed.

Rodriguez Negron, A. M.↗
DOE OSTI2025

An FSI Reweighter for 𝑣 Interactions on Heavy Nuclei

Final State Interaction (FSI) processes in $\nu$--Pb differ significantly from those in neutrino--carbon ($\nu$--C), and MINER$\nu$A is particularly sensitive to these differences, especially in pion absorption. This work develops an FSI reweighting method for $\nu$--Pb (and C) interactions in MINER$\nu$A. We compare GENIE~2, the currently used generator version, with GENIE~3 predictions of the initial hadron energy as a function of FSI fate, and apply these weights to simulated observables. We identify changes in the FSI composition, providing a framework for estimating FSI-related systematic uncertainties and interpreting their effects in the data.

Moreno-Palacios, Oscar [Coll. William and Mary]↗
DOE OSTI2025

HD ADOPT: Heavy-Duty Vehicle Choice Model Documentation

HD ADOPT is a logit consumer vehicle choice and stock model that analyzes the Class 8 tractor market. The model projects future technology shares, fuel consumption, and greenhouse gas (GHG) emissions under input assumptions of technology progress, energy prices, and policies. ADOPT is distinguished from other vehicle choice models through inclusion of non-linear and heterogenous consumer preferences and characterization of the full range of market options rather than use of composite vehicles. In addition, ADOPT has integrated vehicle simulation capabilities that enable performance assessment and optimization of endogenous technology evolution. Optionally, the model is able to adjust this evolution to enforce compliance with fuel economy and GHG emissions regulations. Primary results include projection of technology shares and future in-use fleet energy demand, petroleum consumption, and GHG emissions. This enables analysis and comparison of future scenarios of technology improvements, economic conditions, and national policies. Recent new features for the HD modeling also enable examination of different on-board hydrogen fuel storage technologies from the lens of consumer preferences for vehicle cost and range. This report documents current ADOPT capabilities and methodologies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

MODELING SNR CASSIOPEIA A FROM THE SUPERNOVA EXPLOSION TO ITS CURRENT AGE: THE ROLE OF POST-EXPLOSION ANISOTROPIES OF EJECTA

The remnants of core-collapse supernovae (SNe) have complex morphologies that may reflect asymmetries and structures developed during the progenitor SN explosion. Here we investigate how the morphology of the supernova remnant Cassiopeia A (Cas A) reflects the characteristics of the progenitor SN with the aim of deriving the energies and masses of the post-explosion anisotropies responsible for the observed spatial distribution of Fe and Si/S. We model the evolution of Cas A from the immediate aftermath of the progenitor SN to the three-dimensional interaction of the remnant with the surrounding medium. The post-explosion structure of the ejecta is described by small-scale clumping of material and larger-scale anisotropies. The hydrodynamic multi-species simulations consider an appropriate post-explosion isotopic composition of the ejecta. The observed average expansion rate and shock velocities can be well reproduced by models with ejecta mass M {sub ej} ≈ 4 M {sub ⊙} and explosion energy E {sub SN} ≈ 2.3 × 10{sup 51} erg. The post-explosion anisotropies (pistons) reproduce the observed distributions of Fe and Si/S if they had a total mass of ≈0.25 M {sub ⊙} and a total kinetic energy of ≈1.5 × 10{sup 50} erg. The pistons produce a spatial inversion of ejecta layers at the epoch of Cas A, leading to the Si/S-rich ejecta physically interior to the Fe-rich ejecta. The pistons are also responsible for the development of the bright rings of Si/S-rich material which form at the intersection between the reverse shock and the material accumulated around the pistons during their propagation. Our result supports the idea that the bulk of asymmetries observed in Cas A are intrinsic to the explosion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Micropolar deep material network

This study extends the Deep Material Network (DMN), a physics-informed machine learning framework, to predict the homogenized mechanical response of composite materials with micropolar (Cosserat-type) constitutive behavior. This extension incorporates microstructure-dependent size effects, enabling accurate, efficient, and size-aware predictions for composites with complex internal architectures. While traditional, direct numerical simulation micropolar models effectively capture size effects by introducing extra local degrees of freedom, they bring significant computational challenges, particularly for multiscale analyses relevant to engineering applications. The micropolar DMN developed in this paper achieves high accuracy while significantly reducing computation time compared to micropolar direct numerical simulations. This advancement enables multiscale analyses and parameter studies that were previously impractical, such as high-cycle fatigue simulations and comprehensive investigations of internal length scale effects notably in size-dependent plastic response and the optimization of lattice structures. By uniting microstructure-sensitive modeling, physics-driven learning, and scalable surrogate modeling, the micropolar DMN paves the way for accelerated material design, large-scale parametric studies, and the reliable incorporation of size-dependent effects across a wide range of engineering applications, including optimization and next-generation composite design.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Predictive Engineering Tools for Injection-Molded Long-Carbon-Thermoplastic Composites: Weight and Cost Analyses

This project proposed to integrate, optimize and validate the fiber orientation and length distribution models previously developed and implemented in the Autodesk Simulation Moldflow Insight (ASMI) package for injection-molded long-carbon-fiber thermoplastic composites into a cohesive prediction capability. The current effort focused on rendering the developed models more robust and efficient for automotive industry part design to enable weight savings and cost reduction. The project goal has been achieved by optimizing the developed models, improving and integrating their implementations in ASMI, and validating them for a complex 3D LCF thermoplastic automotive part (Figure 1). Both PP and PA66 were used as resin matrices. After validating ASMI predictions for fiber orientation and fiber length for this complex part against the corresponding measured data, in collaborations with Toyota and Magna PNNL developed a method using the predictive engineering tool to assess LCF/PA66 complex part design in terms of stiffness performance. Structural three-point bending analyses of the complex part and similar parts in steel were then performed for this purpose, and the team has then demonstrated the use of stiffness-based complex part design assessment to evaluate weight savings relative to the body system target (≥ 35%) set in Table 2 of DE-FOA-0000648 (AOI #1). In addition, starting from the part-to-part analysis, the PE tools enabled an estimated weight reduction for the vehicle body system using 50 wt% LCF/PA66 parts relative to the current steel system. Also, from this analysis an estimate of the manufacturing cost including the material cost for making the equivalent part in steel has been determined and compared to the costs for making the LCF/PA66 part to determine the cost per “saved” pound.

42 ENGINEERING↗
DOE OSTI2021

Grand Unification of Quantum Algorithms

Quantum algorithms offer significant speed-ups over their classical counterparts for a variety of problems. The strongest arguments for this advantage are borne by algorithms for quantum search, quantum phase estimation, and Hamiltonian simulation, which appear as subroutines for large families of composite quantum algorithms. A number of these quantum algorithms have recently been tied together by a novel technique known as the quantum singular value transformation (QSVT), which enables one to perform a polynomial transformation of the singular values of a linear operator embedded in a unitary matrix. In the seminal GSLW’19 paper on the QSVT [Gilyén et al., ACM STOC 2019], many algorithms are encompassed, including amplitude amplification, methods for the quantum linear systems problem, and quantum simulation. Here, we provide a pedagogical tutorial through these developments, first illustrating how quantum signal processing may be generalized to the quantum eigenvalue transform, from which the QSVT naturally emerges. Paralleling GSLW’19, we then employ the QSVT to construct intuitive quantum algorithms for search, phase estimation, and Hamiltonian simulation, and also showcase algorithms for the eigenvalue threshold problem and matrix inversion. This overview illustrates how the QSVT is a single framework comprising the three major quantum algorithms, suggesting a grand unification of quantum algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Connecting Organic Aerosol Climate-Relevant Properties to Chemical Mechanisms of Sources and Processing

The research conducted on this project aimed to improve our understanding of secondary organic aerosol (SOA) formation in the atmosphere, and how the properties of the SOA impact climate through its size, phase state, and optical properties. The goal of this project was to demonstrate that the use of molecular composition information to mechanistically connect source apportionment and climate properties can improve the physical basis for simulation of SOA formation and properties in climate models. The research involved developing and improving methods to provide online measurements of the molecular composition of SOA under atmospherically relevant conditions and to apply this technology to controlled simulation chamber experiments and field measurements. The science we have completed with the methodology will impact the simulation of aerosol particles in climate models.

54 ENVIRONMENTAL SCIENCES↗