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

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Investigating the impacts of solid phase extraction on dissolved organic matter optical signatures and the pairing with high‐resolution mass spectrometry data across a freshwater stream network

Abstract Advancing our understanding of dissolved organic matter (DOM) chemistry in aquatic systems necessitates the integration of data streams from multiple analytical platforms. Some measurements require pretreatment with solid phase extraction (SPE), while others are performed directly on whole water samples. Evidence has suggested that SPE will be biased against select DOM fractions, leading to concerns over the ability to establish data linkages across platforms with variable needs for SPE pretreatment, such as those from optical measurements and those that provide high‐resolution molecular information. Here, we directly addressed this concern by assessing the impact of SPE on DOM optical properties through excitation–emission matrices with parallel factor analysis (PARAFAC) for 47 samples across a stream network within a single watershed reflective of variable DOM sources. PARAFAC data was further paired with molecular information obtained by Fourier transform ion cyclotron resonance mass spectrometry (FTICR‐MS). A comparison of PARAFAC models first revealed no systematic qualitative differences in major components between whole water DOM and DOM isolated by SPE (SPE‐DOM); however, quantitative biases against select components were observed. Further linkages with FTICR‐MS data revealed that the molecular fingerprint associated with each PARAFAC component was consistent between the whole water DOM and SPE‐DOM. Our results suggest that bulk scale linkages across these analytical platforms could be inferred irrespective of the observed quantitative biases resulting from SPE for samples within this example watershed. This work represents a key step toward the systematic evaluation of linkages between optical and high‐resolution mass spectrometry datasets in freshwater lotic environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Exploring Local Vibrational Structure in Protein-Bound Chlorophyll a: Isotope-Enrichment Experiments and Electrostatic Analysis

Local chlorophyll (Chl) vibrations play an essential role in biological photosynthesis by facilitating rapid energy transfer between pigments. In research studies, they also provide a useful spectroscopic probe of the local protein environment that surrounds each pigment. However, measuring the complete vibrational absorption spectrum of a protein-bound Chl molecule is much more difficult than, for example, Chl in neat solvent due to overlap with protein vibrations that typically drown out Chl vibrational features. Resonance Raman and fluorescence spectroscopies provide a way around this problem for Franck–Condon active vibrations, but these often rely on cryogenic measurement conditions and fail to capture vibrational signatures from, e.g., ester-group vibrations that lack coupling to a convenient electronic transition. In the present contribution, we use 13 C-enrichment of the protein backbone to shift protein background signals to lower frequency, providing a largely clean spectral window in which to study local Chl a C═O stretch modes. A room-temperature absorption spectrum for Chl a in the water-soluble chlorophyll protein (WSCP) of Lepidium virginicum is thus extracted as a difference between protein-plus-pigment and protein-only vibrational spectra. Excellent agreement in the molecular fingerprint region with the vibrational spectrum of Chl a in organic solvents confirms that the resulting spectrum represents the response of the protein-bound Chl a molecule. Furthermore, the ester group resonance is observed to shift in response to the S53P mutation that eliminates a 17 3 ester group hydrogen bond from the protein environment. Finally, we analyze these experimental results using MD-based electrostatic analysis, finding that electric-field mapping at the C atom of the ester group provides a satisfactory explanation for the observed frequency shifts between organic solvent and protein environment. MD analysis further suggests that a red-shifted ester peak observed experimentally for the S53P mutant results from solvation of the 17 3 Chl ester group due increase water penetration into the Chl-binding pocket relative to wild-type WSCP. Here, we anticipate that these results will prove useful both for benchmarking future simulation work and as a reference for interpreting Chl vibrational spectra as a probe of pigment–protein interactions.

14 SOLAR ENERGY↗
DOE OSTI2021

Ultra-short pulse generation from mid-IR to THz range using plasma wakes and relativistic ionization fronts

In this work, we discuss numerical and experimental results on frequency downshifting and upshifting of a 10 μ m infrared (IR) laser to cover the entire wavelength (frequency) range from λ = 1 to 150 μ m ( ν = 300–2 THz) using two different plasma techniques. The first plasma technique utilizes frequency downshifting of the drive laser pulse in a nonlinear plasma wake. Based on this technique, we have proposed and demonstrated that in a tailored plasma structure, multi-millijoule energy, single-cycle, long-wavelength IR (3–20 μ m) pulses can be generated by using an 810 nm Ti:sapphire drive laser. Here, we extend this idea to the THz frequency regime. We show that sub-joule, terawatts, single-cycle terahertz (2–12 THz or 150–25 μ m) pulses can be generated by replacing the drive laser with a picosecond 10 μ m CO 2 laser and a different shaped plasma structure. The second plasma technique employs frequency upshifting by colliding a CO 2 laser with a rather sharp relativistic ionization front created by ionization of a gas in less than half cycle (17 fs) of the CO 2 laser. Even though the electrons in the ionization front carry no energy, the frequency of the CO 2 laser can be upshifted due to the relativistic Doppler effect as the CO 2 laser pulse enters the front. The wavelength can be tuned from 1 to 10 μ m by simply changing the electron density of the front. While the upshifted light with 5 < λ ( μ m ) < 10 propagates in the forward direction, that with 1 < λ ( μ m ) < 5 is back-reflected. These two plasma techniques seem extremely promising for covering the entire molecular fingerprint region.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Intense infrared lasers for strong-field science

The advent of chirped-pulse amplification in the 1980s and femtosecond Ti:sapphire lasers in the 1990s enabled transformative advances in intense laser–matter interaction physics. Whereas most of experiments have been conducted in the limited near-infrared range of 0.8–1 μm, theories predict that many physical phenomena such as high harmonic generation in gases favor long laser wavelengths in terms of extending the high-energy cutoff. Significant progress has been made in developing few-cycle, carrier-envelope phase-stabilized, high-peak-power lasers in the 1.6–2 μm range that has laid the foundation for attosecond X ray sources in the water window. Even longer wavelength lasers are becoming available that are suitable to study light filamentation, high harmonic generation, and laser–plasma interaction in the relativistic regime. Long-wavelength lasers are suitable for sub-bandgap strong-field excitation of a wide range of solid materials, including semiconductors. In the strong-field limit, bulk crystals also produce high-order harmonics. In this review, we first introduce several important wavelength scaling laws in strong-field physics, then describe recent breakthroughs in short- (1.4–3 μm), mid- (3–8 μm), and long-wave (8–15 μm) infrared laser technology, and finally provide examples of strong-field applications of these novel lasers. Furthermore, some of the broadband ultrafast infrared lasers will have profound effects on medicine, environmental protection, and national defense, because their wavelengths cover the water absorption band, the molecular fingerprint region, as well as the atmospheric infrared transparent window.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

RingIR AG-4000 Testing

The AG-4000 detector can identify gas phase species using molecular fingerprinting and has potential application for SARS-CoV-2 detection in near real time. As part of the development process Sandia will utilize the biological aerosol test bed deployed at the Aerosol Complex to evaluate the penetration of MS2 bacteriophage aerosol through the Ring IR system. The objective of this project is to provide experimentally derived measurements of the RingIR AG-4000 penetration efficiency, including external exhaust filter for mitigation of exhaust aerosol and operation using MS2 bacteriophage as a biological surrogate to the SARS-CoV-2 virus.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Quantum-enhanced Imaging and Spectroscopy and their Relevance to International Safeguards

As a follow-up to our previous report on quantum sensing for safeguards, here we delve deeper into quantum-enhanced imaging & spectroscopy and address their relevance to international safeguards. Much of the approaches rely on entangled photons, a quantum phenomenon not possible with classical physics, although just correlated photons will work for some applications, such as ghost imaging. We provide a comprehensive survey of quantum approaches, including multiple entangled photon ghost imaging and spectroscopy techniques. Entangled photons for noise reduction are also described, as well as Non-Line-Of-Sight imaging, compressive techniques, and squeezed light. Of particular interest is the generation of entangled photons with large wavelength separation, such as infrared/visible entangled photon pairs. Such entangled pairs would allow interaction with objects in the IR, such as in the molecular “fingerprint” wavelength region, while the recording device captures the visible photons, thus leveraging the high efficiency and lower cost of visible detectors. Unfortunately, entangled x-ray photons are not practical, which would have been useful for safeguards to interrogate shielded materials. Entangled gamma rays are even further beyond reason. We provide our assessment for application of quantum-enhanced imaging & spectroscopy for international safeguards, including suggested improvements to existing IAEA instruments and destructive assay measurements that are done at IAEA lab facilities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1996

Small Business Innovations

Under a Small Business Innovation Research (SBIR) contract to Kennedy Space Center, EIC Laboratories invented a Raman Spectrograph with fiber optic sampling for space applications such as sensing hazardous fuel vapors and making on-board rapid analyses of chemicals and minerals. Raman spectroscopy is a laser-based measurement technique that provides through a unique vibrational spectrum a molecular 'fingerprint,' and can function in aqueous environments. EIC combined optical fiber technology with Raman methods to develop sensors that can be operated at a distance from the spectrographic analysis instruments and the laser excitation source. EIC refined and commercialized the technology to create the Fiber Optic Raman Spectrograph and the RamanProbe. Commercial applications range from process control to monitoring hazardous materials.

Source record↗
NASA NTRS2009

Mobile Instruments Measure Atmospheric Pollutants

As a part of NASA's active research of the Earth s atmosphere, which has included missions such as the Atmospheric Laboratory of Applications and Science (ATLAS, launched in 1992) and the Total Ozone Mapping Spectrometer (TOMS, launched on the Earth Probe satellite in 1996), the Agency also performs ground-based air pollution research. The ability to measure trace amounts of airborne pollutants precisely and quickly is important for determining natural patterns and human effects on global warming and air pollution, but until recent advances in field-grade spectroscopic instrumentation, this rapid, accurate data collection was limited and extremely difficult. In order to understand causes of climate change and airborne pollution, NASA has supported the development of compact, low power, rapid response instruments operating in the mid-infrared "molecular fingerprint" portion of the electromagnetic spectrum. These instruments, which measure atmospheric trace gases and airborne particles, can be deployed in mobile laboratories - customized ground vehicles, typically - to map distributions of pollutants in real time. The instruments must be rugged enough to operate rapidly and accurately, despite frequent jostling that can misalign, damage, or disconnect sensitive components. By measuring quickly while moving through an environment, a mobile laboratory can correlate data and geographic points, revealing patterns in the environment s pollutants. Rapid pollutant measurements also enable direct determination of pollutant sources and sinks (mechanisms that remove greenhouse gases and pollutants), providing information critical to understanding and managing atmospheric greenhouse gas and air pollutant concentrations.

Source record↗
DOE OSTI2020

Molecular polarizabilities as fingerprints of perturbations to water by ions and confinement

Perturbations to water, both by ions and confining media, have been the focus of numerous experimental and theoretical studies. Yet, several open questions remain, including the extent to which such perturbations modify the structural and dielectric properties of the liquid. Here, we present a first-principles molecular dynamics study of alkali cations in water (Li + , Na + , and K + ) as well as of water and LiCl and KCl solutions under confinement within carbon nanotubes (CNTs) of small diameter (1.1-1.5 nm). Our simulations support the view that the water structure is only modified locally in the presence of cations. We found that molecular polarizabilities are fingerprints of hydrogen bonding modifications, which occur at most up to the second solvation shell for all cations in bulk water. Under confinement, we found that the overall value of the molecular polarizability of water molecules near the surface is determined by the balance of two effects, which are quantitatively different in CNTs of different radii: the presence of broken hydrogen bonds at the surface leads to a decrease in the polarizabilities of water molecules, while the interaction with the CNT enhances polarizabilities. Interestingly, the reduction in dipole moments of interfacial water molecules under confinement is instead driven only by changes in the water structure and not by interfacial interactions. As expected, confinement effects on water molecular polarizabilities and dipole moments are more pronounced in the case of the 1.1 nm CNT.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Tunable phase-change metasurfaces coupled with mid-infrared molecular vibrations

Chiral optical metasurfaces have emerged as a promising platform in coupling with molecular vibrational fingerprints through the enhanced light-matter interaction under different circularly polarized light illumination. Here, this work reports the mode coupling between the mid-infrared phonon vibrations of polymethyl methacrylate (PMMA) molecules and the thermally tunable chiral metasurfaces based on the phase-change material Ge₂Sb₂Te₅ (GST-225). Phase-change chiral metasurfaces with high circular dichroism (CD) in absorption and tunable plasmonic resonance in the frequency range of 48–56 THz are demonstrated, which covers the phonon vibrational frequency of PMMA molecules at 52 THz. The mode splitting features are observed in the absorption and CD spectra when the metasurface resonance is tuned across the phonon vibrational frequency of PMMA molecules during the phase transition of GST-225. The underlying mechanism of molecule-metasurface coupling is further revealed by studying the electric field and power loss density distributions of the phonon–plasmon coupled modes under both left-handed and right-handed circularly polarized (LCP and RCP) light. The demonstrated results show the potential of dynamically tunable chiral metasurfaces for the applications in label-free molecular sensing, biomedical diagnostics, thermal imaging, and mid-infrared photonics.

Tang, Haotian [Missouri Univ. of Science and Techn↗
DOE OSTI2021

dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design

Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change ( Δ r G ′ o ) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor’s ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings ( https://github.com/maranasgroup/dGPredictor ).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Exploring fingerprints of ultrafast structural dynamics in molecular solutions with an X-ray laser

We apply ultrashort x-ray laser pulses to track optically excited structural dynamics of [Ir 2 (dimen) 4 ] 2+ molecules in solution. In our exploratory study we determine angular correlations in the scattered x-rays, which comprise a complex fingerprint of the ultrafast dynamics. Model-assisted analysis of the experimental correlation data allows us to elucidate various aspects of the photoinduced changes in the excited molecular ensembles. We unambiguously identify that in our experiment the photoinduced transition dipole moments in [Ir 2 (dimen) 4 ] 2+ molecules are oriented perpendicular to the Ir-Ir bond. The analysis also shows that the ground state conformer of [Ir 2 (dimen) 4 ] 2+ with a larger Ir–Ir distance is mostly responsible for the formation of the excited state. We also reveal that the ensemble of solute molecules can be characterized with a substantial structural heterogeneity due to solvent influence. In conclusion, the proposed x-ray correlation approach offers an alternative path for studies of ultrafast structural dynamics of molecular ensembles in the liquid and gas phases.

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

What Sets the Star Formation Rate of Molecular Clouds? The Density Distribution as a Fingerprint of Compression and Expansion Rates

We use a suite of 3D simulations of star-forming molecular clouds, with and without stellar feedback, magnetic fields, and driven turbulence, to study the compression and expansion rates of the gas as functions of density. We show that, around the mean density, supersonic turbulence promotes rough equilibrium between the amounts of compressing and expanding gas, consistent with continuous gas cycling between high- and low-density states. We find that the inclusion of protostellar jets produces rapidly expanding and compressing low-density gas. We find that the gas mass flux peaks at the transition between the lognormal and power-law forms of the density probability distribution function (PDF). This is consistent with the transition density tracking the post-shock density, which promotes an enhancement of mass at this density (i.e., shock compression and filament formation). At high densities, the gas dynamics are dominated by self-gravity: the compression rate in all of our runs matches the rate of the run with only gravity, suggesting that processes other than self-gravity have little effect at these densities. The net gas mass flux becomes constant at a density below the sink formation threshold, where it equals the star formation rate. The density at which the net gas mass flux equals the star formation rate is one order of magnitude lower than our sink threshold density, corresponds to the formation of the second power-law tail in the density PDF, and sets the overall star formation rates of these simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs

Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning. Previous molecular machine learning models have used strings, fingerprints, global features and simple molecular graphs that are inherently information-sparse representations. However, as the complexity of prediction tasks increases, the molecular representation needs to encode higher fidelity information. This work introduces a new approach to infusing quantum-chemical-rich information into molecular graphs via stereoelectronic effects, enhancing expressivity and interpretability. Learning to predict the stereoelectronics-infused representation with a tailored double graph neural network workflow enables its application to any downstream molecular machine learning task without expensive quantum-chemical calculations. We show that the explicit addition of stereoelectronic information substantially improves the performance of message-passing two-dimensional machine learning models for molecular property prediction. We show that the learned representations trained on small molecules can accurately extrapolate to much larger molecular structures, yielding chemical insight into orbital interactions for previously intractable systems, such as entire proteins, opening new avenues of molecular design. Finally, we have developed a web application (simg.cheme.cmu.edu) where users can rapidly explore stereoelectronic information for their own molecular systems.

Boiko, Daniil A↗
NASA NTRS2009

A Comprehensive Characterization of Microorganisms and Allergens in Spacecraft Environment

The determination of risk from infectious disease during long-duration missions is composed of several factors including the concentration and the characteristics of the infectious agent. Thus, a thorough knowledge of the microorganisms aboard spacecraft is essential in mitigating infectious disease risk to the crew. While stringent steps are taken to minimize the transfer of potential pathogens to spacecraft, several medically significant organisms have been isolated from both the Mir and International Space Station (ISS). Historically, the method for isolation and identification of microorganisms from spacecraft environmental samples depended upon their growth on culture media. Unfortunately, only a fraction of the organisms may grow on a culture medium, potentially omitting those microorganisms whose nutritional and physical requirements for growth are not met. Thus, several pathogens may not have been detected, such as Legionella pneumophila, the etiological agent of Legionnaire s disease. We hypothesize that environmental analysis using non-culture-based technologies will reveal microorganisms, allergens, and microbial toxins not previously reported in spacecraft, allowing for a more complete health assessment. The development of techniques for this flight experiment, operationally named SWAB, has already provided advances in NASA laboratory processes and beneficial information toward human health risk assessment. The translation of 16S ribosomal DNA sequencing for the identification of bacteria from the SWAB experiment to nominal operations has increased bacterial speciation of environmental isolates from previous flights three fold compared to previous conventional methodology. The incorporation of molecular-based DNA fingerprinting using repetitive sequence-based polymerase chain reaction (rep-PCR) into the capabilities of the laboratory has provided a methodology to track microorganisms between crewmembers and their environment. Both 16S ribosomal DNA identification and bacterial fingerprinting have improved NASA s capability to better understand spacecraft environments and determine the source of contamination events. Preflight sampling has been completed for air, surface, and water samples. In-flight sample collection has been completed for a total of 8 air and surface sample collection sessions. In-flight hardware has performed well and the surface sampling device received positive feedback from the crew for its ease of use. While processing and analysis continue for these samples, early results have begun to provide information on the spacecraft environment. Using a method called Denaturing Gradient Gel Electrophoresis (DGGE), several air and samples were evaluated to determine the types of organisms that were present. Using only molecular techniques, DGGE does not depend on any microbial growth on culture media, allowing a more comprehensive assessment of the spacecraft interior. Preliminary results have identified several microorganisms that would not have been isolated using current technology, though none of these organisms would be considered medically significant. Interestingly, the isolation of Gram negative organisms is greater using DGGE than conventional media based isolation. The cause of this finding is unclear, though it may be the result of the technique s ability to isolate both viable and non-viable bacteria. The next phase of the SWAB sample analysis is the use of quantitative polymerase chain reaction (QPCR) to look for specific medically significant organisms. While not as broad as DGGE, QPCR is much more sensitive and may reveal findings that were not seen during the initial evaluation. Together, this information will lead toward an accurate microbial risk assessment to help set flight requirements to protect the safety, health, and performance of the crew.

Castro, V.A.↗
DOE OSTI2025

Prediction of hydration energies of adsorbates at Pt(111) and liquid water interfaces using machine learning

Aqueous phase heterogeneous catalysis is important to various industrial processes, including biomass conversion, Fischer–Tropsch synthesis, and electrocatalysis. Accurate calculation of solvation thermodynamic properties is essential for modeling the performance of catalysts for these processes. Explicit solvation methods employing multiscale modeling, e.g., involving density functional theory and molecular dynamics have emerged for this purpose. Although accurate, these methods are computationally intensive. This study introduces machine learning (ML) models to predict solvation thermodynamics for adsorbates on a Pt(111) surface, aiming to enhance computational efficiency without compromising accuracy. In particular, ML models are developed using a combination of molecular descriptors and fingerprints and trained on previously published water–adsorbate interaction energies, energies of solvation, and free energies of solvation of adsorbates bound to Pt(111). These models achieve root mean square error values of 0.09 eV for interaction energies, 0.04 eV for energies of solvation, and 0.06 eV for free energies of solvation, demonstrating accuracy within the standard error of multiscale modeling. Feature importance analysis reveals that hydrogen bonding, van der Waals interactions, and solvent density, together with the properties of the adsorbate, are critical factors influencing solvation thermodynamics. Furthermore, these findings suggest that ML models can provide rapid and reliable predictions of solvation properties. This approach not only reduces computational costs but also offers insights into the solvation characteristics of adsorbates at Pt(111)–water interfaces.

Adsorption↗
NASA NTRS2007

A Comprehensive Characterization of Microorganisms and Allergens in Spacecraft Environment

The determination of risk from infectious disease during long-duration missions is composed of several factors including (1) the host#s susceptibility, (2) the host#s exposure to the infectious disease agent, and (3) the concentration of the infectious agent, and (4) the characteristics of the infectious agent. While stringent steps are taken to minimize the transfer of potential pathogens to spacecraft, several medically significant organisms have been isolated from both the Mir and International Space Station (ISS). Historically, the method for isolation and identification of microorganisms from spacecraft environmental samples depended upon their growth on culture media. Unfortunately, only a fraction of the organisms may grow on a culture medium, potentially omitting those microorganisms whose nutritional and physical requirements for growth are not met. Thus, several pathogens may not have been detected, such as Legionella pneumophila, the etiological agent of Legionnaire#s disease. We hypothesize that environmental analysis using non-culture-based technologies will reveal microorganisms, allergens, and microbial toxins not previously reported in spacecraft, allowing for a more complete health assessment. The development of techniques for this flight experiment, operationally named SWAB, has already provided advances in NASA laboratory processes and beneficial information toward human health risk assessment. The first accomplishment of the SWAB experiment was the incorporation of 16S ribosomal DNA sequencing for the identification of bacteria. The use of this molecular technique has increased bacterial speciation of environmental isolates from previous flights three fold compared to conventional methodology. This increased efficiency in bacterial speciation provides a better understanding of the microbial ecology and the potential risk to the crew. Additional SWAB studies focused on the use of molecular-based DNA fingerprinting using repetitive sequencebased polymerase chain reaction (rep-PCR). This technology has allowed contamination tracking of microorganisms between crewmembers and their environment. This study not only demonstrated that ISS has a greater diversity of organisms than originally expected, but also provided insight into possible routes of infection to the crew. Additional ground-based studies used rep-PCR and protein based assays to determine the potential of methicillin resistant Staphylococcus aureus (MRSA) aboard ISS. MRSA has become increasingly common on Earth and pose a treatment problem for infections during flight. While no MRSA have been isolated from ISS to date, the mecA gene product that is responsible for methicillin resistance was isolated in other Staphylococcus species aboard ISS suggesting a potential of MRSA through gene transfer. Using improved sample collection technologies, flight sampling for SWAB was initiated in August 2006 and should continue through spring of 2007. The focus of these flight samples is the collection of DNA for evaluation by Denaturing Gradient Gel Electrophoresis (DGGE). Unlike other techniques, DGGE does not depend on any microbial growth on culture media allowing a more comprehensive assessment of the spacecraft interior. This study should provide insight into the true microbial ecology that is experienced by the crew during flight. This information will lead toward an accurate microbial risk assessment to help set flight requirements to protect the safety, health, and performance of the crew.

Ott, C. M.↗