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Extraterrestrial Radiation Chemistry and Molecular Astronomy

Astronomical observations of both solar system and interstellar regions have revealed a rich chemical inventory that includes most classes of organic molecules and selected inorganics. For example, gas-phase ethylene glycol and SOz have been observed by astronomers, while solidphase detections include OCS, H2O2 , and the cyanate anion.' All of these are found in environments that are, by earthly standards, exceedingly hostile: temperatures of 10 - 100 K, miniscule densities, and near-ubiquitous ionizing-radiation fields. Beyond the simplest chemical species, these conditions have made it difficult-to-impassible to account for the observed molecular abundances using gas-phase chemistry, suggesting solid-phase reactions play an important role. In extraterrestrial environments, cosmic rays, UV photons, and magnetospheric radiation all drive chemical reactions, even at cryogenic temperatures. To study this chemistry, radiation astrochemists conduct experiments on icy materials, frozen under vacuum and exposed to sources such as keV electrons and MeV protons. Compositional changes usually are followed with IR spectroscopy and, in selected cases, more-sensitive mass-spectral techniques. This talk will review some recent results on known and suspected extraterrestrial molecules and ions. Spectra and reaction pathways will be presented, and predictions made for interstellar chemistry and the chemistry of selected solar system objects. Some past radiation-chemical contributions, and future needs, will be explored.

Hudson, Reggie L.↗

A Study on Advanced Lithium-Based Battery Cell Chemistries to Enhance Lunar Exploration Missions

NASAs Exploration Technology Development Program (ETDP) Energy Storage Project conducted an advanced lithium-based battery chemistry feasibility study to determine the best advanced chemistry to develop for the Altair Lunar Lander and the Extravehicular Activities (EVA) advanced Lunar surface spacesuit. These customers require safe, reliable batteries with extremely high specific energy as compared to state-of-the-art. The specific energy goals for the development project are 220 watt-hours per kilogram (Wh/kg) delivered at the battery-level at 0 degrees Celsius ( C) at a C/10 discharge rate. Continuous discharge rates between C/5 and C/2, operation between 0 and 30 C and 200 cycles are targeted. Electrode materials that were considered include layered metal oxides, spinel oxides, and olivine-type cathode materials, and lithium metal, lithium alloy, and silicon-based composite anode materials. Advanced cell chemistry options were evaluated with respect to multiple quantitative and qualitative attributes while considering their projected performance at the end of the available development timeframe. Following a rigorous ranking process, a chemistry that combines a lithiated nickel manganese cobalt oxide Li(LiNMC)O2 cathode with a silicon-based composite anode was selected as the technology that can potentially offer the best combination of safety, specific energy, energy density, and likelihood of success.

Reid, Concha M.↗

Step-by-Step Simulation of Radiation Chemistry Using Green Functions for Diffusion-Influenced Reactions

Radiolytic species are formed approximately 1 ps after the passage of ionizing radiation through matter. After their formation, they diffuse and chemically react with other radiolytic species and neighboring biological molecules, leading to various oxidative damage. Therefore, the simulation of radiation chemistry is of considerable importance to understand how radiolytic species damage biological molecules [1]. The step-by-step simulation of chemical reactions is difficult, because the radiolytic species are distributed non-homogeneously in the medium. Consequently, computational approaches based on Green functions for diffusion-influenced reactions should be used [2]. Recently, Green functions for more complex type of reactions have been published [3-4]. We have developed exact random variate generators of these Green functions [5], which will allow us to use them in radiation chemistry codes. Moreover, simulating chemistry using the Green functions is which is computationally very demanding, because the probabilities of reactions between each pair of particles should be evaluated at each timestep [2]. This kind of problem is well adapted for General Purpose Graphic Processing Units (GPGPU), which can handle a large number of similar calculations simultaneously. These new developments will allow us to include more complex reactions in chemistry codes, and to improve the calculation time. This code should be of importance to link radiation track structure simulations and DNA damage models.

Plante, Ianik↗

Tropospheric Bromine Chemistry: Implications for Present and Pre-industrial Ozone and Mercury

We present a new model for the global tropospheric chemistry of inorganic bromine (Bry) coupled to oxidant-aerosol chemistry in the GEOS-Chem chemical transport model (CTM). Sources of tropospheric Bry include debromination of sea-salt aerosol, photolysis and oxidation of short-lived bromocarbons, and transport from the stratosphere. Comparison to a GOME-2 satellite climatology of tropospheric BrO columns shows that the model can reproduce the observed increase of BrO with latitude, the northern mid-latitudes maximum in winter, and the Arctic maximum in spring. This successful simulation is contingent on the HOBr + HBr reaction taking place in aqueous aerosols and ice clouds. Bromine chemistry in the model decreases tropospheric ozone mixing ratios by <1-8 nmol/mol (6.5% globally), with the largest effects in the northern extratropics in spring. The global mean tropospheric OH concentration decreases by 4 %. Inclusion of bromine chemistry improves the ability of global models (GEOS-Chem and p-TOMCAT) to simulate observed 19th-century ozone and its seasonality. Bromine effects on tropospheric ozone are comparable in the present-day and pre-industrial atmospheres so that estimates of anthropogenic radiative forcing are minimally affected. Br atom concentrations are 40% higher in the pre-industrial atmosphere due to lower ozone, which would decrease by a factor of 2 the atmospheric lifetime of elemental mercury against oxidation by Br. This suggests that historical anthropogenic mercury emissions may have mostly deposited to northern mid-latitudes, enriching the corresponding surface reservoirs. The persistent rise in background surface ozone at northern mid-latitudes during the past decades could possibly contribute to the observations of elevated mercury in subsurface waters of the North Atlantic.

bromine↗

Sample Acquisition and Analytical Chemistry Challenges to Verifying Compliance to Aviators Breathing Oxygen (ABO) Purity Specification

NASA has been developing and testing two different types of oxygen separation systems. One type of oxygen separation system uses pressure swing technology, the other type uses a solid electrolyte electrochemical oxygen separation cell. Both development systems have been subjected to long term testing, and performance testing under a variety of environmental and operational conditions. Testing these two systems revealed that measuring the product purity of oxygen, and determining if an oxygen separation device meets Aviator's Breathing Oxygen (ABO) specifications is a subtle and sometimes difficult analytical chemistry job. Verifying product purity of cryogenically produced oxygen presents a different set of analytical chemistry challenges. This presentation will describe some of the sample acquisition and analytical chemistry challenges presented by verifying oxygen produced by an oxygen separator - and verifying oxygen produced by cryogenic separation processes. The primary contaminant that causes gas samples to fail to meet ABO requirements is water. The maximum amount of water vapor allowed is 7 ppmv. The principal challenge of verifying oxygen produced by an oxygen separator is that it is produced relatively slowly, and at comparatively low temperatures. A short term failure that occurs for just a few minutes in the course of a 1 week run could cause an entire tank to be rejected. Continuous monitoring of oxygen purity and water vapor could identify problems as soon as they occur. Long term oxygen separator tests were instrumented with an oxygen analyzer and with an hygrometer: a GE Moisture Monitor Series 35. This hygrometer uses an aluminum oxide sensor. The user's manual does not report this, but long term exposure to pure oxygen causes the aluminum oxide sensor head to bias dry. Oxygen product that exceeded the 7 ppm specification was improperly accepted, because the sensor had biased. The bias is permanent - exposure to air does not cause the sensor to return to its original response - but the bias can be accounted for by recalibrating the sensor. After this issue was found, continuous measurements of water vapor in the oxygen product were made using an FTIR. The FTIR cell is relatively large, so response time is slow - but moisture measurements were repeatable and accurate. Verifying ABO compliance for oxygen produced by commercial cryogenic processes has a different set of sample acquisition and analytical chemistry challenges. Customers want analytical chemists to conserve as much as possible. Hygrometers are not exposed to hours of continuous flow of oxygen, so they don't bias, but small amounts of contamination in valves can cause a "fail". K bottles are periodically cleaned and recertified - after cleaning residual moisture can cause a "fail". Operators let bottle pressure drop to room pressure, introduce outside air into the bottle, and the subsequent fill will "fail". Outside storage of K‐bottles has allowed enough in‐leakage, so contents will "fail".

Graf, John↗

Impact of Improvements in Volcanic Implementation on Atmospheric Chemistry and Climate in the GISS-E2 Model

The representation of volcanic eruptions in climate models introduces some of the largest errors when evaluating historical simulations, partly due to the crude model parameterizations. We will show preliminary results from the Goddard Institute for Space Studies (GISS)-E2 model comparing traditional highly parameterized volcanic implementation (specified Aerosol Optical Depth, Effective Radius) to deploying the full aerosol microphysics module MATRIX and directly emitting SO2 allowing us the prognosically determine the chemistry and climate impact. We show a reasonable match in aerosol optical depth, effective radius, and forcing between the full aerosol implementation and reconstructions/observations of the Mt. Pinatubo 1991 eruption, with a few areas as targets for future improvement. This allows us to investigate not only the climate impact of the injection of volcanic aerosols, but also influences on regional water vapor, O3, and OH distributions. With the skill of the MATRIX volcano implementation established, we explore (1) how the height of the injection column of SO2 influence atmospheric chemistry and climate response, (2) how the initial condition of the atmosphere influences the climate and chemistry impact of the eruption with a particular focus on how ENSO and QBO and (3) how the coupled chemistry could mitigate the climate signal for much larger eruptions (i.e. the 1258 eruption, reconstructed to be approximately 10x Pinatubo). During each sensitivity experiment we assess the impact on profiles of water vapor, O3, and OH, and assess how the eruption impacts the budget of each.

aerosols↗

Ice Chemistry in Interstellar Dense Molecular Clouds, Protostellar Disks, and Comets

Despite the low temperatures (T less than 20K), low pressures, and low molecular densities found in much of the cosmos, considerable chemistry is expected to occur in many astronomical environments. Much of this chemistry happens in icy grain mantles on dust grains and is driven by ionizing radiation. This ionizing radiation breaks chemical bonds of molecules in the ices and creates a host of ions and radicals that can react at the ambient temperature or when the parent ice is subsequently warmed. Experiments that similar these conditions have demonstrated a rich chemistry associated with these environments that leads to a wide variety of organic products. Many of these products are of considerable interest to astrobiology. For example, the irradiation of simple ices has been shown to abiotically produce amino acids, nucleobases, quinones, and amphiphiles, all compounds that play key roles in modern biochemistry. This suggests extraterrestrial chemistry could have played a role in the origin of life on Earth and, by extension, do so on planets in other stellar systems.

Ice Irradiation↗

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗

Sensitivity Analysis of the Change of Renal Stone Occurrence Rates in Astronauts Using Urine Chemistries

Changes in urine chemistry, during and post-flight, potentially alter the likelihood of renal stones in astronauts. Although much is known about the effects of space flight on urine chemistry, no inflight incidences of renal stones in US astronauts exist and the question How much does this risk change with space flight? remains difficult to accurately quantify. Previous work by our group has illustrated the application of multi-factor deterministic and probabilistic modeling to assess the change in predicted likelihood of renal stone. Utilizing 1517 astronaut urine chemistries to inform the renal stone occurrence rate forecasting model, we performed a sensitivity analysis on urine chemistry components for their influence on predictions of renal stone size and rate of renal stone occurrence.

physiological response↗

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry is a high-dimensionality, large-data problem and thus may be suited to machine-learning algorithms. We show here the potential of a random forest regression algorithm to replace the gas-phase chemistry solver in the GEOS-Chem chemistry model. In this proof-of-concept study, we used one month of model output to train random forest regression models to predict the concentrations of each long-lived chemical species after integration based upon the physical and chemical conditions before the chemical integration. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for very long-lived species and the absolute concentration for shorter lived species. The skill of the machine learning algorithm is further improved by using a family approach for NO and NO2 rather than treating them independently.By replacing the numerical integrator with the random forest algorithm and running this model for one month, we find that the model is able to reproduce many of the features of the reference chemistry simulation. Replacing the integration methodology with a machine learning algorithm has the potential to be substantially faster. There are a wide range of applications for such an approach, e.g. to generate boundary conditions, for use in air quality forecasts or chemical data assimilation systems, etc.

Keller, Christoph A.↗

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗

Investigation of Magmatic Activities on Early Mars Using Igneous Mineral Chemistry in Gale Crater, Mars

One objective of rover missions is exploring the geological context of the surroundings. Over the years, igneous petrology and sedimentology have been disconnected, the first investigating magmatic processes and volcanic activities, and the second seeking environmental conditions in the past and assessing the habitability of the planet. Although different, one is related to the other: igneous rocks are altered and broken down, leading to the formation of sedimentary rocks, which can in turn be used to back out the nature of their magmatic source. The Curiosity rover that landed in the 3.7 Gyr old impact crater Gale is traveling through sedimentary rocks. About fifty float rocks have been observed, and several of them with ambiguous texture and composition have been classified as igneous or sedimentary depending on studies such as Jake_M. The composition of several unambiguous igneous rocks has been analyzed [4- 6] but their heterogeneity at a larger instrumental (measurement size < 2 cm) scale prevents the measurement of a bulk composition as performed on Earth. An original approach avoiding these two last issues is to consider igneous mineral chemistry analyzed within igneous and sedimentary rocks to assess magmatic processes that could have formed them. Most Curiosity data are used to explore ancient environmental conditions, and a significant number of compositional analyses are under-explored for constraining magmatic activities. We will present how we can make use of sedimentary data for investigating igneous processes in the vicinity of Gale crater. Geological Context: We focus on the first 750 martian days, corresponding to measurements in a coherent lacustrine sedimentary unit called Bradbury, because all sedimentary rocks were sourced from the same watershed and appear to have a consistent source with minimal alteration [2-3]. Igneous detrital minerals including feldspar and pyroxene, are observed in sedimentary rocks. Monte Carlo models showed that minimal cation loss is observed based on the composition of all Bradbury rocks, implying negligible weathering [3]. Although clay minerals are detected in few rocks [7], chemical compositions of rocks can be explained by a mixture of primary igneous minerals [3]. Variation of composition within Bradbury rocks can be explained by mineral sorting and one distinct source component. While a common magmatic source is suggested, Bradbury sediments likely come from several volcanic eruptions from a single magmatic chamber [9- 10]. The occurrence of alkali minerals like sanidine and K-rich rocks throughout Bradbury supports the presence of a potassic component, likely trachytic, while plagioclase and a mafic composition suggest a basaltic component [8-9]. Instruments: Mineral chemistry can be estimated by three instruments onboard Curiosity. The CheMin instrument enables detection of mineral assemblages using X-ray diffraction (XRD). Using Rietveld refinement, each mineral is identified according to their 1D XRD pattern [11]. Note that distinction between pyroxene minerals is challenging with the CheMin instrument due to overlapping peaks on XRD patterns and low angular resolution of the instrument [12]. Then, using least square regression and optimization algorithms based on unit-cell parameters, mineral chemistry has been estimated by [11]. Plagioclase composition has been estimated using the NaAlSi3O8- CaAl2Si2O8 system and alkali feldspar is based on the NaAlSi3O8-KAlSi3O8 system (stars in Fig.1). Two mudstone samples (John Klein and Cumberland) and one sandstone sample (Windjana) were analyzed by CheMin at Yellowknife Bay and Kimberley, respectively. Figure 1. Ternary diagrams of feldspars (top) and pyroxene (bottom) quadrilateral. Stars correspond to CheMin composition and the gray patches to ChemCam composition. The colored dots are the composition of feldspar and pyroxene that crystallized during fractional crystallization at FMQ+1 of a melt extracted at distinct melting degree during the adiabatic ascent of a primitive mantle composition, without any water (left panels) and with 0.5 wt.% of water (right panels) at distinct pressure. The ChemCam instrument enables the analysis of the chemical compositions of rocks at hundreds of micrometer scale (350-550 μm) using laser induced breakdown spectroscopy (LIBS), which may provide the composition of minerals when they are larger than the beam spot (>550 μm) [13]. Within >5000 LIBS points, we performed a typical stoichiometric filtering allowing us to distinguish 56 feldspar and 10 pyroxene mineral compositions (grey patches in Fig. 1). Finally, the Alpha Particle X-ray Spectrometer (APXS) analyzes the composition of rocks with a 1.6 cm diameter spot size. Monte Carlo mass balance modeling allowed [3] to decipher a feldspar range varying between An30 and An40 (Fig. 1). Discussion: Although there could be a more complex history and other ways to form the whole compositional range of igneous minerals analyzed within the Bradbury formation, we are presenting here simple magmatic pathways commonly occurring on Earth using the thermodynamical softwares pMELTS and rhyoliteMELTS [14]. The objective is to find reasonable igneous processes that produce minerals that parallel the compositions of feldspar and pyroxene analyzed by the Curiosity rover. As commonly observed for mid-ocean ridge basalts, the adiabatic ascent of a primitive mantle composition [15] partially melting at 2 GPa has been modeled, followed by the extraction of a liquid at distinct degrees of partial melting, which undergoes fractional crystallization at an oxygen fugacity +1 log unit above the fayalite-magnetite-quartz (FMQ) buffer within the crust (0.02-0.4 GPa) with H2O = 0- 0.5 wt. %. These latter conditions correspond to those recorded within igneous clasts from the Noachian martian breccia NWA 7034 and paired and within Gale igneous rocks (colored dots in Fig. 1) [16-17]. To check the reliability of these 2-step models, we also tested fractional crystallization at similar conditions (FMQ+1; P=0.02-0.4 GPa; H2O = 0-0.5 wt. %) of starting compositions corresponding to that of magmas with distinct melting degrees obtained from isobaric experiments at 2 GPa [18]. Mineral compositions obtained from both models are similar. As shown on Fig. 1, the whole range of observed feldspar compositions cannot be reproduced by fractionation of one magma only. Indeed, while alkali feldspar and Na-plagioclase likely crystallized from fractional crystallization of a low-degree melt (here <15%), plagioclase and pyroxene can only be formed by fractional crystallization of a higher degree melt (here >19%). The corresponding liquid descent lines are broadly in agreement with compositions estimated by ChemCam corresponding to float igneous rocks (Fig. 2) [4-6]. Figure 2. Silica versus alkali content. Lines show the liquid lines of descent from magmas with distinct degrees of melting. Gray patches represents the composition of Gale igneous rocks [4-6]. Trachytic to rhyolitic magmas crystallize alkali feldspar, and andesite to dacite magmas likely form plagioclase. Therefore, at least two starting magmas at distinct melting degrees, which could easily come from a single mantle source, are necessary to explain the whole compositional range of feldspar and pyroxene analyzed within Bradbury rocks. Conclusion: Because rocks from the Bradbury formation are likely originating from the same magmatic source with minimal weathering as supported by several studies using different approaches, igneous mineral chemistry analyzed by CheMin and ChemCam allows us to back out reasonable magmatic pathways that could have crystallized them. Fractional crystallization of at least two starting magmas originating from distinct melting degrees of a single mantle source can explain the whole range of feldspar and pyroxene composition. Both alkaline and sub-alkaline liquids can be produced, with compositions corresponding to those of the igneous rocks analyzed by ChemCam within the Bradbury formation, highlighting the complexity of Mars magmatism.

Payre, V.↗

Tropospheric Ozone in the NASA GEOS Model: Effects of Satellite NO2 Data Assimilation and Improvements in Background NOx Chemistry

Tropospheric ozone is a major atmospheric oxidant, the primary source of the hydroxyl radical, and a greenhouse gas. In the troposphere, it is produced from the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (NOx=NO+NO2) or has its origins in the stratosphere. The tropospheric ozone burdens in global atmospheric chemistry models disagree by a factor of 1.5, indicating an incomplete understanding of ozone sources and sinks. Here we examine the tropospheric ozone simulation in NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS model with the GEOS-Chem chemistry mechanism (version 14.2), using observations from satellites, aircraft, ozonesondes, and surface sites. This modeling framework is used to produce GMAO’s GEOS Composition Forecasts (GEOS-CF), and it has been updated extensively with new emission inventories, improved model physics, satellite data assimilation capability, and an up-to-date chemical mechanism that includes tropospheric halogen chemistry and NOx recycling from particulate nitrate. Earlier versions of the model using GEOS-Chem version 12.0 showed an underestimate in tropospheric ozone in the northern hemisphere. We evaluate the ozone simulation in the updated model, focusing on the effects of assimilating satellite NO2 data from the Ozone Monitoring Instrument (OMI), constraining stratospheric ozone to satellite data, and updates to the NOx chemistry. We will examine changes in the vertical profiles of tropospheric ozone over the US in support of the Tropospheric Emissions: Monitoring of Pollution (TEMPO), and discuss implications for the tropospheric ozone budget.

V. Shah↗

Tropospheric Ozone in the NASA GEOS Model: Effects of Satellite Data Assimilation and Improvements in Background NO x Chemistry

Tropospheric ozone is a major atmospheric oxidant, the primary source of the hydroxyl radical, and a greenhouse gas. In the troposphere, it is produced from the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (NO x =NO+NO 2 ) or has its origins in the stratosphere. The tropospheric ozone burdens in global atmospheric chemistry models disagree by a factor of 1.5, indicating an incomplete understanding of ozone sources and sinks. Here we examine the tropospheric ozone simulation in NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS model with the GEOS-Chem chemistry mechanism (version 14.2), using observations from satellites, aircraft, ozonesondes, and surface sites. This modeling framework is used to produce GMAO’s GEOS Composition Forecasts (GEOS-CF), and it has been updated extensively with new emission inventories, improved model physics, satellite data assimilation capability, and an up-to-date chemical mechanism that includes tropospheric halogen chemistry and NOx recycling from particulate nitrate. Earlier versions of the model using GEOS-Chem version 12.0 showed an underestimate in tropospheric ozone in the northern hemisphere. We evaluate the ozone simulation in the updated model, focusing on the effects of assimilating satellite NO 2 data from the Ozone Monitoring Instrument (OMI), constraining stratospheric ozone to satellite data, and updates to the NOx chemistry. We will examine changes in the vertical profiles of tropospheric ozone over the US in support of the Tropospheric Emissions: Monitoring of Pollution (TEMPO), and discuss implications for the tropospheric ozone budget.

Viral Shah↗

Effect of Water Chemistry on Crack Growth Rate in Neutron Irradiated X-750 and XM-19

The Advanced Test Reactor (ATR) National Science User Facility (NSUF) was used to study the effect of neutron damage on crack growth rates (CGR) under simulated boiling water reactor (BWR) water chemistry conditions using a fast neutron spectrum to accelerate the damage process. Nickel based Alloy X-750 and XM-19 were irradiated in the ATR center flux trap (CFT), with this experiment marking the first civilian project to utilize this position within the reactor. 0.4T-CT specimens were used to study crack growth rates for two sample fluences (~1.9 x 1020 n/cm2 and ~9.5 x 1020 n/cm2, E > 1MeV)) in both normal water chemistry (NWC) at 2.5 ppm O2 and hydrogen water chemistry (HWC) at ~80 ppb H2. CGR testing was conducted at a temperature of 288 °C. A decrease in CGR was observed for both alloys with the average ratio (CGR in NWC/CGR in HWC) being a factor of 281 for the XM-19 samples and a factor of 27.8 for the X-750 samples. Observed CGRs in this study showed little difference compared to non-irradiated samples (the non-irradiated XM-19 was 20% cold worked), suggesting limited sensitivity of these susceptible alloys to irradiation effects. Detailed results of CGR response to varying environmental electrochemical potential (ECP) and water purity will be presented in the context of crack growth mitigation in different water chemistries and the change in material behavior relative to non-irradiated samples of the material.

36 MATERIALS SCIENCE↗

Anion Exchange Ionomers: Impact of Chemistry on Thin-Film Properties

Ionomer thin-films (i.e., 20–100 nm) on supports serve as model systems to understand ionomer-catalyst interfacial behavior as well as the confinement-driven deviation in properties from bulk membranes. While ionomer thin-films have been examined for proton exchange ionomers, the thin-film properties of anion exchange ionomers (AEIs) remain largely unexplored. More importantly, delineating the convoluted impact of chemistry and confinement on thin-film morphology and hydration is of interest to advancing the field on functional ionic interfaces. In this work, these aspects are studied by using AEIs of different backbones (perfluorinated, aliphatic, and aromatic) and side chains (various lengths, and single versus dual functional groups). Quartz-crystal microbalance and spectroscopic ellipsometry are used to analyze density and coupled with calculated free volume fraction of thin-films to provide insights on their gas transport properties. AEI side-chain's chemical character plays a key role in how confinement modulates hydration (in thin-film versus bulk). Overall, the results elucidate the effects of backbone, side-chain chemistry versus anion/cation type in the confinement-driven changes in thin-film morphology and swelling. This study also provides new insights for tuning AEI transport functionalities at interfaces via chemistry, which can benefit the design and development of electrode-ionomers for alkaline membrane-based energy systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Advancing Electrolyte Solution Chemistry and Interfacial Electrochemistry of Divalent Metal Batteries

Divalent metal (Mg, Ca, etc.) battery chemistries potentially provide a sustainable long-term technical solution for large-scale energy storage because of high natural abundance of divalent metal elements in the earth crust. Good progress has been made on materials especially electrolyte development in the past years; however, significant challenges exist, particularly the very limited fundamental understanding on electrolyte solution chemistry and interfacial electrochemistry. In this perspective, we review and discuss key discoveries and understanding of divalent battery chemistry with a focus on electrolyte-dependent interfacial electrochemistry of divalent metal anodes. A concise review of electrolyte development, operando studies of the electrified interfaces and unique charge-transfer process is provided; the knowledge gaps and future research directions are discussed.

25 ENERGY STORAGE↗

The MolSSI QCArchive project: An open-source platform to compute, organize, and share quantum chemistry data

The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python-based software infrastructure that powers automated computation and storage of quantum chemistry (QC) results. The MolSSI-hosted central server provides the computational molecular sciences community a location to freely access tens of millions of QC computations for machine learning, methodology assessment, force-field fitting, and more through a Python interface. Facile, user-friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at the website. The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of QC computations for individuals or groups of researchers at any scale. The QCArchiveInfrastructure is open-source (BSD-3C), code repositories can be found at github, and releases can be downloaded via PyPI and Conda. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry Data Science > Computer Algorithms and Programming

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗