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NASA NTRS2022

Updraft Dynamics and Microphysics: On the Added Value of the Cumulus Thermal Reference Frame in Simulations of Aerosol–Deep Convection Interactions

One fundamental question about atmospheric moist convection processes that remains debated is whether or under what conditions a relevant variability in background aerosol concentrations may have a significant dynamical impact on convective clouds and their associated precipitation. Furthermore, current climate models must parameterize both the microphysical and the cumulus convection processes, but this is usually implemented separately, whereas in nature there is a strong coupling between them. As a first step to improve our understanding of these two problems, we investigate how aerosol concentrations modify key properties of updrafts in eight large-eddy permitting regional simulations of a case study of scattered convection over Houston, Texas, in which convection is explicitly simulated and microphysical processes are parameterized. Dynamical and liquid-phase microphysical responses are investigated using two different reference frames: static cloudy-updraft grid cells versus tracked cumulus thermals. In both frameworks we observe the expected microphysical responses to higher aerosol concentrations, such as higher cloud number concentrations and lower rain number concentrations. In terms of the dynamical responses, both frameworks indicate weak impacts of varying aerosol concentrations relative to the noise between simulations over the observationally derived range of aerosol variability for this case study. On the other hand, results suggest that thermals are more selective than cloudy-updraft grid cells in terms of sampling the most active convective air masses. For instance, vertical velocity from thermals is significantly higher at upper levels than when sampled from cloudy-updraft grid points, and several microphysical variables have higher average values in the cumulus thermal framework than in the cloudy-updraft framework. In addition, the thermal analysis is seen to add rich quantitative information about the rates and covariability of microphysical processes spatially and throughout tracked thermal lifecycles, which can serve as a stronger foundation for improving subgrid-scale parameterizations.

Daniel Hernandez-Deckers↗
DOE OSTI2022

A 194nW Energy-Performance-Aware loT SoC Employing a 5.2nW 92.6% Peak Efficiency Power Management Unit for System Performance Scaling, Fast DVFS and Energy Minimization

A self-powered IoT system-on-chip (SoC) reduces power to sub-μw and employs multiple power-management techniques to trade-off ultra-low power (ULP), higher performance, smaller energy harvester footprint, and longer operating lifetime. Minimum Energy Point Tracking (MEPT) [1]–[4] keeps an SoC operating at the minimum energy point (MEP) to enhance system lifetime. Previous sample-and-hold MEPT schemes need frequent voltage comparisons and a high-frequency clock that increases power [2]. Current-ratio-based MEPT relies on specialized CMOS technology for body-bias tuning [3]. A switched-capacitor-based MEPT can achieve energy minimization at a targeted performance [4], but it uses a 30MHz clock witμW power consumption and low power efficiency. For ULP IoT applications, SoCs need to have ultra-low quiescent power, high efficiency for energy delivery, performance scaling based on available energy, and energy minimization to increase system lifetime. In this work, we propose an ULP IoT SoC with a triple-mode power management unit (PMU) that integrates energy-performance scaling, event-driven fast DVFS, and MEPT features to improve the system energy efficiency, as shown in Fig. 13.8.1. This work achieves a minimum 194nW power consumption for the SoC and 5.2nW quiescent power for the PMU with a 92.6% peak efficiency and >10 4 dynamic range. The timing waveform in Fig. 13.8.1 (bottom), demonstrates the transition of the three modes including energy aware (EA), performance aware (PA), and MEPT based on event priority and input voltage level which reflects the energy availability. As such, the system energy consumption and performance could be well-balanced based on both the input and output conditions.

self-powered IoT system-on-chip (SoC)↗
NASA NTRSDate not supplied

One-Carbon Metabolism and SANS 2022 Update

Spaceflight Associated Neuro-ocular Syndrome, or SANS, affects a subset of astronauts (1, 2), and biochemical evidence has documented differences in those astronauts (3). Specifically, they had higher circulating concentrations of metabolites of the one-carbon metabolic pathway (1C), including homocysteine, and these concentrations were higher before flight (3). After ruling out many potential confounding factors in these otherwise healthy individuals (e.g., sex, kidney function, vitamin status, coffee consumption), a study of genetics was warranted. In an initial pilot effort, we documented a genetic predisposition to develop ophthalmic changes after long-duration space flight (4). That is, from a limited study of 5 single-nucleotide polymorphisms (SNPs), we found that the G allele for the MTRR A66G SNP was associated with a greater risk of choroidal folds and cotton-wool spots after flight, and the C allele for SHMT1 C1420T was protective against optic disc edema (4). These data provide a potential pathway for understanding why some individuals develop SANS, while others do not. The initial pilot study of 5 SNPs yielded striking findings, but the 1C pathway is far more complex. An effort was undertaken to examine more than 500 1C SNPs to see if a broader examination could help illuminate this association. That work is ongoing. The astronaut findings led us to advocate for the inclusion of 1C pathway genetic and biochemistry testing on other SANS-related projects, noting that genetics might help identify responders, non-responders, or outliers. The first such effort yielded evidence of an association of specific forms of the MTRR and SHMT-1 SNPs and vitamin B12 status with end-tidal CO2 after acute carbon dioxide exposure (5). The second such effort led to the identification that individuals exposed to strict head-down tilt and CO2 for 30-d who developed optic disc edema also had risk alleles for the two SNPs described above (6). Additionally, we identified a clinical population with many characteristics either attributed or purported to be involved in the ocular changes seen in affected astronauts: women with polycystic ovary syndrome (PCOS). PCOS is a condition of androgen excess and anovulatory menstrual cycles. The shared characteristics and clinical findings between SANS and PCOS generally include higher circulating homocysteine concentrations, increased retinal nerve fiber layer thickness, increased androgen concentrations (or responses), and altered carbohydrate metabolism. To our knowledge, no study has examined whether women with PCOS have asymptomatic ophthalmic anomalies observed in astronauts with SANS. While researchers have evaluated the one-carbon metabolism pathway polymorphisms of PCOS patients, and initial studies show an association with certain one-carbon polymorphisms, none have looked at the set of SNPs identified in our studies that are associated with ophthalmic changes in astronauts. Accordingly, we designed a study to evaluate the association of one-carbon pathway SNPs and ophthalmic findings in patients with PCOS and/or IIH compared to controls. Subjects provided blood samples for vitamin and one carbon biochemistry analyses, an extensive analysis of >500 SNPs associated with one carbon metabolism and had eye examinations and ocular imaging. Data analysis are underway. The data collected to date have shown associations between one carbon pathway biochemistry and genetics and incidence of SANS. The mechanisms for SANS has yet to be identified, although many hypotheses exist. Based on our data, we have developed (8, 9) and expanded (6) a multi-hit hypothesis for how these seemingly disparate findings could be linked. While intriguing, the hypothesis represents the starting point for further research. We aim to clarify the relationship between B-vitamin status and genetics with regard to the risk of SANS. Ultimately, understanding the mechanism(s) behind this will provide a means to predict, prevent, or treat these ophthalmologic pathologies in astronauts, and terrestrial populations.

S M Smith↗
DOE OSTI2024

Time-Resolved X-ray Emission Spectroscopy and Resonant Inelastic X-ray Scattering Spectroscopy of Laser Irradiated Carbon

The existence of liquid carbon as an intermediate phase preceding the formation of novel carbon materials has been a point of contention for several decades. Experimental observation of such a liquid state requires nonthermal melting of solid carbon materials at various laser fluences and pulse properties. Reflectivity experiments performed in the mid-1980s reached opposing conclusions regarding the metallic or insulating properties of the purported liquid state. Time-resolved X-ray absorption studies showed shortening of C–C bonds and increasing diffraction densities, thought to evidence a liquid or glassy carbon state, respectively. Nevertheless, none of these experiments provided information on the electronic structure of the proposed liquid state. Herein, we report the results of time-resolved resonant inelastic X-ray scattering (RIXS) and time-resolved X-ray emission spectroscopy (XES) studies on amorphous carbon (a-C) and ultrananocrystalline diamond (UNCD) as a function of delay time between the irradiating pulse and X-ray probe. For both a-C and UNCD, we attribute decreases in RIXS or XES signals to transition blocking, relaxation, and finally, ablation. Increased signal at 20 ps following the irradiation of the UNCD is attributed to the probable formation of nanoscale structures in the ablation plume. Further, differences in the amount of signal observed between a-C and UNCD are explained by the difference in sample thickness and, specifically, incomplete melting of the UNCD film. Comparisons to spectral simulations based on MD trajectories at extreme conditions indicate that the carbon state in our experiments is crystalline. Normal mode analysis confirmed that symmetrical bending or stretching of the C–C bonds in the diamond lattice results in XES spectra with small intensity differences. Overall, we observed no evidence of melting to a liquid state, as determined by the lack of changes in the spectral properties for up to 100 ps delays following the melting pulses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Trail Creek in Taylor River Watershed, Colorado 2024-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Trail Creek. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format TR-X-Y, where TR refers to Trail Creek, X is the treatment block identifier, and Y is the location identifier. Specifically, TR-ASCC1 is the control treatment block under the Adaptive Silviculture for Climate Change (ASCC) project, and TR-ASCC2 is the clear-cut treatment block. TR-ASCC-EHSn is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and TR-ASCC-ERTn (upslope n=1) are ecohydrology sites along the electrical resistivity tomography transects. The sample and location information can be found in metadata.csv, and the data from the soil sensors will be included in a future data version when the observation period becomes sufficiently long for data analysis. Sampling and Measurements Each sample falls into one of the two sampling methods – (1) intact cores or (2) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. The intact cores were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2024

Sulfur speciation in dacitic melts using X-ray absorption near-edge structure spectroscopy of the S K -edge (S-XANES): Consideration of radiation-induced changes and the implications for sulfur in natural arc systems

Abstract The synchrotron technique of micro X-ray absorption near-edge structure spectroscopy at the sulfur K-edge (S-XANES) provides a unique opportunity to measure the proportion of different oxidation states of sulfur (S) in silicate glasses. Although applied extensively in the analysis of basaltic silicate glasses, few S-XANES studies have investigated variations in S oxidation states with fO2 in felsic silicate glasses. In addition, no study has systematically compared the S-XANES results obtained from the same samples at different photon flux densities to quantify the relationship between exposure time and changes in S speciation in silicate glass, as has been done for Fe and V. This study evaluates observed differences in S speciation measured in experimentally produced H2O-saturated dacitic glasses over a range of reducing to oxidizing conditions (from log fO2 = ΔFMQ-0.7 to ΔFMQ+3.3; FMQ is the fayalite-magnetite-quartz mineral redox buffer) and equilibrated at 1000 °C and 300 MPa. S-XANES spectra were collected at three different photon flux densities using three microspectroscopy beamlines. As is observed in S-XANES analyses of basaltic silicate glasses, beam-induced changes to the S6+/ΣS are observed as a function of photon flux density and beam exposure time. Our results demonstrate that silicate glasses of dacitic composition undergo beam-induced photo-reduction in samples equilibrated at ΔFMQ > +1.75 and photo-oxidation if equilibrated at ΔFMQ < +1. The time required to observe beam-induced changes in the spectra varies as a function of flux density, and our study establishes an upper photon density limit at ~1.0 × 1012 photons/µm2. The S6+/ΣS calculated from spectra collected below this absorbed photon limit at intermediate flux densities (~1–4 × 109 photons/s per µm2) are affected by beam damage, as no conditions were found to be completely free of beam-induced changes. However, the S6+/ΣS ratios calculated below the limit at intermediate flux densities are consistent with thermodynamic constraints, demonstrating that S6+/ΣS ratios calculated from S-XANES spectra can be considered reliable for estimating the oxygen fugacity. Our results carry important implications for the S budget of felsic magmas and dissolution mechanisms in evolved melts. While our results from all three flux densities show the presence of S4+ dissolved in relatively oxidized (ΔFMQ > +1.75) dacitic glass, even in the spectra exposed to the lowest photon densities, we are unable to rule out the possibility that the S4+ signal is the result of instantaneous X-ray irradiation induced beam damage using S-XANES alone. When our spectra are compared to S-XANES spectra from basaltic silicate glasses, important differences exist in the solubility of S2– and S6+ between dacitic silicate melts, pointing to differences in solubility mechanisms as melt composition changes. This study highlights the need for further investigation into beam damage systematics, presence of S4+, and the solubility mechanisms of different oxidation states of S as silicate melt composition changes.

Geochemistry & Geophysics↗
DOE OSTI2026

UCB-GLOBES: An open-access mass spectral database of identified and unidentified atmospheric organic compounds

Chemical characterization of atmospheric organic aerosols using gas chromatography with 70 eV electron ionization mass spectrometry (GC/EI-MS) has been used for decades in advancing molecular marker detection and identification, though primarily through suspect screening and/or targeted analyses. To advance non-targeted analyses of environmental samples, we have catalogued approximately 27 000 mass spectra (MS) of the trimethylsilyl derivatives of semi-volatile organic aerosol (OA) analytes in the open-access University of California Berkeley Goldstein Library of Organic Biogenic Environmental Spectra (UCB-GLOBES). Analytes were observed in ambient samples from the U.S. and the Central Amazon and/or laboratory simulations of secondary OA (SOA) formation. These samples are representative of OA under urban and biomass burning influences as well as SOA derived from biogenic precursors (e.g., isoprene, monoterpenes, sesquiterpenes) and biomass burning intermediates. MS are documented in UCB-GLOBES without regard to known chemical identity, annotated with extensive metadata such as sample source/experimental conditions, any structural information gained from MS analyses, and predicted chemical properties such as average carbon oxidation state and carbon number. UCB-GLOBES MS are compatible for importing into the NIST MS Search program, and we have also provided a Jupyter Notebook for MS visualization and comparisons. We demonstrate the utility of UCB-GLOBES through MS reanalyses of prior analytes observed in ambient data, finding a 20 % reduction in the number of analytes assigned to OA source categories reliant solely on time series correlation and an overall 11 % increase in new MS-based OA source categorization for the Southeast U.S. For 1513 analytes observed previously in the Central Amazon, we found 375 MS matches using UCB-GLOBES vs. 136 MS matches during prior analyses, representing a 14 % gain in newly confirmed or newly categorized OA species. While OA from laboratory oxidation experiments in UCB-GLOBES are highly diverse chemically, on average only 29 % of UCB-GLOBES MS have a mass spectral match to another MS entry in UCB-GLOBES and/or in databases of known compounds (i.e. NIST MS Database, Adams Essential Oil, MANE Flavor and Fragrance Company). This indicates that roughly 70 % of UCB-GLOBES MS are unique thus far, not observed more than once among the laboratory oxidation samples and ambient data in UCB-GLOBES MS. Further, only 18 % can be positively identified using these databases or known authentic standards. This points to a large gap between these laboratory simulations and ambient OA. Overall, the UCB-GLOBES database can be utilized for improving confidence in OA source categorization and/or identification, novel chemical marker discovery, tracking chemical diversity, de novo structure and properties prediction, and improving MS search and matching algorithms. This can ultimately inform future research priorities for the chemical characterization of atmospheric organic samples.

Mass spectrometry↗
NASA NTRS2001

Application of Computer Tomography for Life Detection

Perhaps one of the most fundamentally difficult challenges facing those who would search for life is that of scale determination. Spatial scales of life on Earth range over more than 15 orders of magnitude in mass and volume, and more than 8 orders of magnitude in 2 dimensional space. If the distribution of life is sparse in comparison to the background on which it is found, then the choice of the right scale is critical to finding that life. But how does one identify the proper scale? To put this in other words, how does one recognize the "haystacks" in which the needles (biosignatures and evidence of life) might be most profitably searched for? The problem is further exacerbated when conditions get extreme because much of the life moves from the clement surface environment into the pores and more clement environments inside of rocks, minerals and soils. Once encased in their lithic homes, these microbes become nearly impossible to study by standard techniques because of the opacity of the rocks. It is this problem that we propose to address in the work proposed here. Computer Tomography (CT) has been a very valuable tool in medicine, where the best resolution available has typically been of the order of about 0.5 mm. However, to adapt the approach for life detection of microbial endoliths, the resolution needs to be moved to the micrometer and even submicrometer levels. Thus for the studies proposed here, we begin with a commercially available instrument that can yield resolution of approximately 10 micrometers. The rational for this is twofold: first, this is the "state of the art" in laboratory instruments; and second, that while the usual size of a microbial cell is about 1 micron, microorganisms tend to live in communities that usually exceed the 10 micrometer size range. The resolution also depends on the sample size itself, so having a small lab instrument into which small samples can be placed will be beneficial to the resolution. We have now used several different CT systems, beginning with the medical scanners (Arcadia CT group) for the detection of layered communities in sandstone rocks from Antarctica. Even this crude instrument was able to point to the areas of the rock that were dominated by microbial populations - this provides the critical first information that says, "Go back and look at these sites with other methods." We showed that without sample preparation or destruction it was possible to gain knowledge as to the presence of density differences suggestive of life.

Tsapin, A.↗
NASA NTRS1990

An atlas of mixed-morphology pairs

Current theories of galaxy formation imply that environment is the most important factor in deciding whether a collapsing region becomes a spiral galaxy or an elliptical galaxy. If this is the case, then isolated pairs comprising a galaxy of each morphology provide an especially interesting test, since, strictly speaking, they should not exist. The first phase of studying E-S pairs is to define a sample which is genuinely isolated and genuinely E-S. The catalog compiled by Karachentsev is the most useful Northern Hemisphere starting point, containing 603 pairs which obeyed certain well-defined angular isolation criteria. All of these pairs now have velocities, enabling further removal of at least some of the non-physical binaries. The conventional cutoff at 500 km s(exp -1) was used for this purpose. However, the morphological types were estimated from Palomar Sky Survey prints, and are consequently not very accurate. It was therefore decided to obtain good charge coupled device (CCD) images in the B-band of all pairs from the Karachentsev sample which were classified as either E-S (73 pairs) or E-SO (91 pairs), for a total of 164 pairs. This includes the 24 pairs with known redshift differences greater than 500 km s(exp -1) (9 E-S and 15 E-SO), which were also imaged in this survey, not only for completeness but also in case the criteria were later changed, or the redshifts were found to be in error. A total of 198 CCD frames were obtained between February 1986 and the present, with most of the observations being completed by the summer of 1987. A number of missing stragglers and replacements for poor quality images were taken more recently. Most observations used the number 1-0.9m telescope at Kitt Peak, with a few frames from the 2.1m. Some pairs required more than one image due to their separation. The morphological study did not require photometric conditions, but calibrations were made for almost all images anyway, and the weather was probably photometric at least half of the time, at least to an accuracy of around 0.1 magnitudes. The results of this first phase will be published in the form of an Atlas, from which any interested readers will be able to select their own sub-sample using their preferred morphological criteria.

Sharp, N. A.↗
DOE OSTI2021

Science Validation for Dark Energy Research with Optical Imaging Surveys

The Universe has been expanding at an accelerating rate over the past several billion years, as though an unknown form of dark energy permeates all of space. The ultimate scientific goal of the proposed research is to distinguish between different physical mechanisms that could account for this observed accelerated expansion, for example, the zero-point energy of the vacuum, a dynamical form of energy that varies in time and/or space, or a modification to our theory of gravity. Wide- field optical imaging surveys of the night sky can test these competing models by measuring both the cosmic expansion history and the growth of large-scale structure. To this end, the Dark Energy Survey (DES) has cataloged several hundred million galaxies and thousands of supernovae. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will enlarge the census to billions of galaxies and hundreds of thousands of supernovae. A critical question for these dark energy experiments is whether systematic uncertainties can continue to be controlled at a level to keep pace with the statistical precision offered by such enormous datasets. The immediate research objectives of this project were (1) to prepare and validate input datasets that are the foundation of cosmological analyses with DES, and (2) to prepare for value-added characterization of Rubin Observatory commissioning data to inform early operations and accelerate the realization of dark energy science from LSST data products. For DES, we assembled and curated cosmology-ready data releases that include value-added components such as enhanced photometric and astrometric calibrations, alternative source extraction algorithms, maps of the survey coverage and survey conditions, object classifications, object quality selections, galaxy shapes, and photometric redshifts. We used the galaxy clustering technique to validate the photometric redshift distributions of various galaxy samples to be used as lenses in combined studies of galaxy clustering and weak gravitational lensing. The galaxy clustering redshift analysis was enhanced by use of a larger sample of reference galaxies from the eBOSS spectroscopic survey that extends to higher redshifts. For LSST, we prepared for science validation studies of commissioning data aimed at dark energy science capability that extend beyond the normative system-level tests to be done by the Rubin Observatory Construction Project. We identified a set of proposed survey strategies and candidate target fields that could be observed during the commissioning period to enhance science validation activities related to studies of dark energy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2004

Epoxy Adhesives for Stator Magnet Assembly in Stirling Radioisotope Generators (SRG)

As NASA seeks to fulfill its goals of exploration and understanding through missions planned to visit the moons of Saturn and beyond, a number of challenges arise from the idea of deep space flight. One of the first problems associated with deep space travel is electrical power production for systems on the spacecraft. Conventional methods such as solar power are not practical because efficiency decreases substantially as the craft moves away from the Sun. The criterion for power generation during deep space missions are very specific, the main points requiring high reliability, low mass, minimal vibration and a long lifespan. A Stirling generator, although fairly old in concept, is considered to be a potential solution for electrical power generation for deep space flight. A Stirling generator works on the same electromagnetic principles of a standard generator, using the linear motion of the alternator through the stationary stator which produces electric induction. The motion of the alternator, however, is produced by the heating and cooling dynamics of pressurized gases. Essentially heating one end and cooling another of a contained gas will cause a periodic expansion and compression of the gas from one side to the other, which a displacer translates into linear mechanical motion. NASA needs to confirm that the materials used in the generator will be able to withstand the rigors of space and the life expectancy of the mission. I am working on the verification of the epoxy adhesives used to bond magnets to the steel lamination stack to complete the stator; in terms of in-service performance and durability under various space environments. Understanding the proper curing conditions, high temperature properties, and degassing problems as well as production difficulties are crucial to the long term success of the generator. system and steel substrate used in the stator. To optimize the curing conditions of the epoxies, modulated differential scanning calorimetry analysis was done as a function of cure time and temperatures. Adhesion bond strength was tested at various temperatures with lap shear samples using Hiperco 50 substrate to ensure that the proper adhesive is being used. To try and solve the problem of bondline thickness, micro glass beads of 0.0017" in diameter were investigated to see if any other physical properties of the epoxy were affected. Efforts will be made to develop a standard, optimized, fabrication process/procedure of sub-scale magnet-stator assemblies for various adhesive performance evaluation studies under simulated generator conditions. Also, accelerated aging testing will be done in a pressurized canister with stator assembly samples for three years to verify if any degassing or thermal degradation of the epoxy occurs. The necessity of verifying the correct epoxy adhesive system for the stator magnet in the SRG is crucial because failure of the stator assembly would jeopardize the electrical system, and thereby the entire mission itself. My work involves specimen fabrications, testing, and data analyses of the epoxy adhesive system for the stator magnet in the SRG is crucial because failure of the stator assembly would jeopardize the electrical system, and thereby the entire mission itself.

Cater, George M.↗
NASA NTRS2015

Rao-Blackwellization for Adaptive Gaussian Sum Nonlinear Model Propagation

When dealing with imperfect data and general models of dynamic systems, the best estimate is always sought in the presence of uncertainty or unknown parameters. In many cases, as the first attempt, the Extended Kalman filter (EKF) provides sufficient solutions to handling issues arising from nonlinear and non-Gaussian estimation problems. But these issues may lead unacceptable performance and even divergence. In order to accurately capture the nonlinearities of most real-world dynamic systems, advanced filtering methods have been created to reduce filter divergence while enhancing performance. Approaches, such as Gaussian sum filtering, grid based Bayesian methods and particle filters are well-known examples of advanced methods used to represent and recursively reproduce an approximation to the state probability density function (pdf). Some of these filtering methods were conceptually developed years before their widespread uses were realized. Advanced nonlinear filtering methods currently benefit from the computing advancements in computational speeds, memory, and parallel processing. Grid based methods, multiple-model approaches and Gaussian sum filtering are numerical solutions that take advantage of different state coordinates or multiple-model methods that reduced the amount of approximations used. Choosing an efficient grid is very difficult for multi-dimensional state spaces, and oftentimes expensive computations must be done at each point. For the original Gaussian sum filter, a weighted sum of Gaussian density functions approximates the pdf but suffers at the update step for the individual component weight selections. In order to improve upon the original Gaussian sum filter, Ref. [2] introduces a weight update approach at the filter propagation stage instead of the measurement update stage. This weight update is performed by minimizing the integral square difference between the true forecast pdf and its Gaussian sum approximation. By adaptively updating each component weight during the nonlinear propagation stage an approximation of the true pdf can be successfully reconstructed. Particle filtering (PF) methods have gained popularity recently for solving nonlinear estimation problems due to their straightforward approach and the processing capabilities mentioned above. The basic concept behind PF is to represent any pdf as a set of random samples. As the number of samples increases, they will theoretically converge to the exact, equivalent representation of the desired pdf. When the estimated qth moment is needed, the samples are used for its construction allowing further analysis of the pdf characteristics. However, filter performance deteriorates as the dimension of the state vector increases. To overcome this problem Ref. [5] applies a marginalization technique for PF methods, decreasing complexity of the system to one linear and another nonlinear state estimation problem. The marginalization theory was originally developed by Rao and Blackwell independently. According to Ref. [6] it improves any given estimator under every convex loss function. The improvement comes from calculating a conditional expected value, often involving integrating out a supportive statistic. In other words, Rao-Blackwellization allows for smaller but separate computations to be carried out while reaching the main objective of the estimator. In the case of improving an estimator's variance, any supporting statistic can be removed and its variance determined. Next, any other information that dependents on the supporting statistic is found along with its respective variance. A new approach is developed here by utilizing the strengths of the adaptive Gaussian sum propagation in Ref. [2] and a marginalization approach used for PF methods found in Ref. [7]. In the following sections a modified filtering approach is presented based on a special state-space model within nonlinear systems to reduce the dimensionality of the optimization problem in Ref. [2]. First, the adaptive Gaussian sum propagation is explained and then the new marginalized adaptive Gaussian sum propagation is derived. Finally, an example simulation is presented.

state estimation↗
NASA NTRS2020

Building a standardized Observing System Simulation Experiment (OSSE) framework for Mars

We advocate that the Decadal Survey recommends the NASA Science Mission Directorate to develop a rigorous Observing System Simulation Experiment (OSSE) framework for Mars, to optimize future atmospheric observations. Atmospheric conditions on Mars are a potential hazard source for landing missions. Errors in the estimates of atmospheric density profiles, inadequate knowledge of wind vertical structure and dust concentration as a function of height are likely causes of uncertainty at the landing site on the order of kilometers. An operational real-time weather forecasting capability for Mars would reduce such uncertainties, carrying enormous benefits to future robotic missions, and would be an invaluable prerequisite for human missions.A real-time forecasting capability relies upon three fundamental components: a critical mass of observing systems, a data assimilation system (DAS), and a global forecast model. The DAS allows the model to ingest the data effectively, optimizing the observational information content,and transforming them into a gridded representation of the atmosphere at a given time, called an ‘analysis’. The analysis is the best estimate of the atmospheric state for that time, and also represents a set of ‘initial conditions’ from which a global model can be initialized, to predict a future state of the atmosphere. The connection between analysis and forecast represents the foundation of modern weather forecasting. However, from the point of view of a forecast system,not all observations are equally impactful, partially because of the problem of “observational error correlation”, one important research topic in data assimilation development. For the Earth, partly due to the spontaneous and deregulated development of observations and forecast capabilities worldwide for more than half a century,the use of observations in contemporary operational forecast systems is suboptimal, with many potentially useful data being underutilized. On the contrary, Mars atmospheric scientists are in the unique situation of designing the next-generation observing systems by learning from the experience gathered on the Earth, so as to assure that the future instruments are specifically optimized to give the maximum benefit to a future weather forecast capability.An immensely powerful tool that has been firmly established by atmospheric scientists on the Earth is represented by a properly designed OSSE framework. A realistic OSSE framework cannot only quantify the benefit of future data types, be them surface based or space borne, but can also help design and optimize an entire observational network. Furthermore, OSSEs can provide deep insights into an atmosphere’s behavior, by addressing conceptual problems of its intrinsic predictability and delineating the regions or features of the atmosphere which are more sensitive to additional data and would benefit from a denser sampling. The difficulties posed by OSSEs are fundamentally different for Earth and Mars. For Earth, the enormous data volume imposes a tremendous constraint on any innovation in the observing systems: it is very hard for a single sensor to impact the skill. For Mars, the problem is the opposite: almost any additional instrument will exert some impact. However, OSSEs can help to evaluate the cost/benefit for every sensor and suggest optimal data configuration and density.The purpose of this white paper is to provide an introduction to a rigorously designed OSSE framework, explain the underlying problems and challenges, and engage the Mars community to collaborate with Earth Atmospheric scientists in order to develop a joint-OSSE framework for Mars with the largest consensual basis possible. An OSSE infrastructure would increase the understanding of the Martian atmosphere, would help NASA to optimize instrument specifications and orbit choice, providing the maximium benefit for a given expenditure of resources, and could even help establishing a roadmap for a future real-time weather forecasting capability.

Oreste Reale↗
NASA NTRS2023

Automated Fluidics Device for Extraction and Quantification of miRNA Biomarkers From Blood

Radiation Assessment DuRing Exposure And long-Duration Spaceflight (RADREADS) demonstrates space-compatible point-of-care technology for quantitative biological monitoring of blood miRNA biomarkers in response to long-term low dose radiation exposure. This individualized monitoring approach will inform targeted treatment strategies to maximize medical resource utilization by accounting for individual susceptibility to radiation-related illnesses. As human spaceflight progresses beyond Earth’s magnetic shielding, radiation exposure poses a significant risk to astronaut health and safety. Extended operation in this environment comes with an increased risk of radiation exposure, leading to higher risks of radiation sickness, cancer, central nervous system effects, and degenerative diseases. While conventional physical dosimetry techniques capture radiation dose, individualistic susceptibility to radiation damage is varied. Multiple characteristics, including age, body weight, sex, genetics, and immune status, have been found to influence radiosensitivity (Liu et al. 2011, and Bouffler 2016). This differential response necessitates individualized monitoring and targeted treatment strategies to maximize medical resource utilization; however, a practical diagnostic platform for quantifying long-term, low dose radiation-induced tissue damage does not currently exist. MicroRNAs (miRNAs) are a class of small, non-coding RNAs that regulate gene expression by mediating the degradation of messenger RNA. The levels of particular miRNAs are influenced by biological processes such as inflammation and serve as biomarkers for a variety of conditions including cancer (Singh et al. 2017). MicroRNAs are found in various bodily fluids and are amenable to collection via liquid biopsies, providing a minimally invasive and easily quantifiable readout for a variety of radiosensitive reporters. A preliminary signature of 15 spaceflight sensitive miRNA has been identified in rodent and human studies, including miR-21-5p, miR-24-3p, miR-92a-3p, miR-17-5p, miR-16a-3p, miR-34a-3p, and miR-223-3p. These targets generally increased expression with radiation dose and linear energy transfer, though variation between individuals is not yet described. Current gaps in the field include a lack of understanding of longitudinal biological responses to long-term, low dose radiation exposure and the absence of space-compatible point-of-care technology for quantitative biological monitoring. In this body of work, we aim to develop an automated bleed-to-read system to process whole blood for the detection of miRNA biomarkers in order to monitor individualistic responses to radiation exposure. This will be achieved via separating serum (or plasma) from whole blood, followed by extraction, amplification, and quantification of the miRNA using a RT-qPCR reaction. Previously, the WetLab-2 hardware enabled execution of a RT-qPCR reaction aboard ISS; however, it is a manual system that requires crew manipulation and bulky components (Parra et al. 2017). To address these issues, automated fluid handling hardware was developed for each stage of sample preparation. Extraction of total RNA is achieved by sequentially pumping reagents through an off-the-shelf nucleic acid binding column (miRNeasy Serum/Plasma Advanced Kit, Qiagen). This approach eliminates several manual pipetting and centrifuging steps and limits the use of toxic chemicals commonly found in other sample processing techniques. The resulting elution will then be automatically dispensed for RT-qPCR analysis using a compact rotary qPCR (Mic qPCR Cycler, Bio Molecular Systems) that will improve spaceflight compatibility by removing bubbles from the detection region, another challenge highlighted by WetLab-2 (Parra et al. 2017). Efforts are also being made to simplify the RT-qPCR reaction to a 1-step air-dryable mix to improve long-term reagent stability at room temperature and reduce system complexity. By automating the RT-qPCR processes via microfluidic manipulation, RADREADS will reduce crewmember hands-on time and enable the personalized detection of radiation-induced tissue damage during long duration missions. Minimally invasive, longitudinal monitoring of individual’s response to radiation exposure will inform how the physiological system responds to long-term low dose space radiation and enables development of targeted countermeasures by the medical team. Ultimately, this portable technology will require minimal technical expertise and can also be used to monitor miRNA biomarkers associated with other diseases.

Tristen Head↗
NASA NTRS2024

3D Printed Materials Characterization for Rapid Prototyping and Plant Growth

Through KSC IRTD funding in 2022, this project brought a list of 18, 3D printed filaments into formal characterization testing to provide a reference for their behaviors under relevant applications. The project format set up a series of tests to expose 3D printed specimens. A total of 1,989 individual 3D printed test specimens were sent across KSC to be scrutinized by three laboratories to fulfill a multidisciplinary assessment of each material TRL. Testing started with 18 materials. Initially, seed germination assays in the PPA, sample materials were enclosed in petri dishes with lettuce seeds on damp germination paper. No significant impacts on lettuce seed germination were observed in this testing. Next, sample coupons were printed and sent for materials testing to the KSC Analysis/Mechanical and Environmental Testing Laboratory, where they were subjected to 14- and 30-day soak periods in solutions used to provide nutrients to plants or to sanitize hardware before and after use. Following a long soak typical of a 30-day plant growout in Hoagland’s solution, 14 materials gained more than 10% of their own mass. This indicated an increased potential for leaching or providing conditions that are not food safe. Materials that exceeded 15% absorption by mass were eliminated from further testing. Based off this result, the team continued with a core list of nine filaments to fulfill Tensile, Flexural, Biofilm formation, and plant growth testing. Those materials were PLA (Raise3D), ABS (Raise3D), PETG (PolyethyleneTerephthalate Glycol) (Raise3D), ASA (Acrylonitrile Styrene Acrylate) (Raise3D), PC (Polycarbonate) (Raise3D), TPU (Thermoplastic polyurethane)-95 (Raise3D), PLA Copper (Gizmodorks), PP (Polypropylene) (Braskem), and HIPS (High Impact Polystyrene) (Gizmodorks). Testing also quantified the spectral impact of using different color 3D printed surfaces in a growth chamber. The material used for spectral testing was PLA. Printing employed a standard surface texture representative of all materials. It was shown through Tensile Testing (ASTM D638-22) that the breaking force of a 3D printed part greatly varied depending on layer orientation. This is common through all materials, and demonstrates that the strength of a 3D printed component can be maximized by layering the material normal to the primary force on the part. Four-point flexural testing (ASTM D790) provided quantities of interest, Flexural modulus, Flexural strength, Flexural stress, and strain at break within a 5% strain limit from each of nine materials. Biofilm formation testing was conducted in the Molecular and Microbiological Laboratory. Testing completed on specimens from each material showed equal formation on the surface. Additional plant growth testing was conducted in the PPA beyond the initial germination testing. The final assessment documents that three materials (PLA, ABS, and PC) have reached TRL 6 through extensive testing, and ultimate end-to-end applied use in experimental or testing conditions (flight and ground). TRL 5 materials (ASA, TPU-95, PLA Copper, PP, PETG, and HIPS) have all been successfully applied in Research and Development for crop growth applications and are ready to be applied in formal testing. TRL 4 materials Nylon910, PLA Carbon Fiber, PPA CF, PPA Glass Fiber (GF), NinjaFlex, and P-filament 721 are materials that were able to be printed and tested, but have yet to show data meeting applied requirements. TRL 3 NylonX, Flex TPE-185, and Nylon were unable to be reliably printed to fulfill testing. These results provide researchers with reference for materials to use during plant growth experimentation, and also set a standard for future characterization work applying 3D printing and materials to testing, research, and experimentation.

Gioia Massa↗
NASA NTRS2001

Comparison of ER-2 Aircraft and POAM-III, MLS, and SAGE-II Satellite Measurements During SOLVE Using Traditional Correlative Analysis and Trajectory Hunting Technique

We compared the version 5 Microwave Limb Sounder (MLS) aboard the Upper Atmosphere Research Satellite (UARS), version 3 Polar Ozone and Aerosol Measurement-III (POAM-111) aboard the French satellite SPOT-IV, version 6.0 Stratospheric Aerosol and Gas Experiment 11 (SAGE-II) aboard the Earth Radiation Budget Satellite, and NASA ER-2 aircraft measurements made in the northern hemisphere in January-February 2000 during the SAGE III Ozone Loss and Validation Experiment (SOLVE). This study addresses one of the key scientific objectives of the SOLVE campaign, namely, to validate multi-platform satellite measurements made in the polar stratosphere during winter. This intercomparison was performed using a traditional correlative analysis (TCA) and a trajectory hunting technique (THT). Launching backward and forward trajectories from the points of measurement, the THT identifies air parcels sampled at least twice within a prescribed match criterion during the course of 5 days. We found that the ozone measurements made by these four instruments agree most of the time within 110% in the stratosphere up to 1400 K (approximately 35 km). The water vapor measurements from POAM-III and the ER-2 Harvard Lyman-alpha hygrometer and JPL laser hygrometer agree to within 10.5 ppmv (or about +/-10%) in the lower stratosphere above 380 K. The MLS and ER-2 ClO measurements agree within their error bars for the TCA. The MLS and ER-2 nitric acid measurements near 17-20 km altitude agree within their uncertainties most of the time with a hint of a positive offset by MLS according to the TCA. We also applied the AER box model constrained by the ER-2 measurements for analysis of the ClO and HN03 measurements using the THT. We found that: (1) the model values of ClO are smaller by about 0.3-0.4 (0.2) ppbv below (above) 400 K than those by MLS and (2) the HN03 comparison shows a positive offset of MLS values by approximately 1 and 1-2 ppbv below 400 K and near 450 K, respectively. It is hard to quantify the HN03 offset in the 400-440 K range because of the high sensitivity of nitric acid to the PSC schemes. Our study shows that, with some limitations (like HN03 comparison under PSC conditions), the THT is a more powerful tool for validation studies than the TCA, making conclusions of the comparison statistically more robust.

Danilin, M. Y.↗
DOE OSTI2022

The Effect of Geometry, Spin, and Orbital Optimization in Achieving Accurate, Correlated Results for Iron–Sulfur Cubanes

Iron–sulfur clusters comprise an important functional motif in the catalytic centers of biological systems, capable of enabling important chemical transformations at ambient conditions. This remarkable capability derives from a notoriously complex electronic structure that is characterized by a high density of states that is sensitive to geometric changes. The spectral sensitivity to subtle geometric changes has received little attention from correlated, large active space calculations, owing partly to the exceptional computational complexity for treating these large and correlated systems accurately. Here, to provide insight into this aspect, we report the first Complete Active Space Self Consistent Field (CASSCF) calculations for different geometries of the [Fe(II/III) 4 S 4 (SMe) 4 ] -2 clusters using two complementary, correlated solvers: spin-pure Adaptive Sampling Configuration Interaction (ASCI) and Density Matrix Renormalization Group (DMRG). We find that the previously established picture of a double-exchange driven magnetic structure, with minute energy gaps (<1 mHa) between consecutive spin states, has a weak dependence on the underlying geometry. However, the spin gap between the singlet and the spin state 2S + 1 = 19, corresponding to a maximal number of Fe-d electrons being unpaired and of parallel spin, is strongly geometry dependent, changing by a factor of 3 upon slight deformations that are still within biologically relevant parameters. The CASSCF orbital optimization procedure, using active spaces as large as 86 electrons in 52 orbitals, was found to reduce this gap compared to typical mean-field orbital approaches. Our results show the need for performing large active space calculations to unveil the challenging electronic structure of these complex catalytic centers and should serve as accurate starting points for fully correlated treatments upon inclusion of dynamical correlation outside the active space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗