Search NASA⌕ Search

SEARCH · Search NASA

Results for “dynamic sampling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Techniques for avoiding discrimination errors in the dynamic sampling of condensable vapors

In the mass spectrometric sampling of dynamic systems, measurements of the relative concentrations of condensable and noncondensable vapors can be significantly distorted if some subtle, but important, instrumental factors are overlooked. Even with in situ measurements, the condensables are readily lost to the container walls, and the noncondensables can persist within the vacuum chamber and yield a disproportionately high output signal. Where single pulses of vapor are sampled this source of error is avoided by gating either the mass spectrometer ""on'' or the data acquisition instrumentation ""on'' only during the very brief time-window when the initial vapor cloud emanating directly from the vapor source passes through the ionizer. Instrumentation for these techniques is detailed and its effectiveness is demonstrated by comparing gated and nongated spectra obtained from the pulsed-laser vaporization of several materials.

Lincoln, K. A.↗
DOE OSTI2020

Dynamic Importance Sampling

Dynamic Importance (DynIm) Sampling is a new approach for importance sampling in high-dimensional space. DynIm has two key characteristics. (1) Importance sampling: The notion of importance of candidate samples is used to guide the sampling, where importance is defined based on the (dis)similarity from previously selected samples (via Euclidean distance metric). (2) Dynamic sampling: The sampling can be performed dynamically, as new candidates are generated and new samples must be selected. DynIm was developed as part of a machine learning based coupling of scales in large multiscale simulations and can be used for a variety of applications.

Bhatia, Harsh↗
NASA NTRS1975

Dynamic sampling of laser-induced vapor plumes by mass spectrometry

Mass spectrometric techniques and instrumentation for the dynamic sampling of laser-induced vapor clouds during their submillisecond expansion periods are discussed. Illustrations are drawn primarily from the results of pulsed-laser heating of some oxides and graphites; these include comparisons of incident laser beam pulses with the resultant pulses of positive ions and selected neutral species. Observed characteristics of vapor pulses from graphite are shown to correlate with calculated surface temperature response. Dynamic velocity measurements of individual vapor species are described and, from these velocities, vaporization temperatures are derived and compared using four gas dynamic models for vapor expansion into vacuum. Additional measurements indicate that the laser beam may interact directly with the neutral vapors to produce ions.

Lincoln, K. A.↗
DOE OSTI2017

Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling

Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage. There is a pressing need to develop novel imaging and spectroscopic imaging methods that will minimize such sample damage as well as reduce the data acquisition time. The latter is useful for high-throughput analysis of materials structure and chemistry. Here, in this work, we present a novel machine learning based method for dynamic sparse sampling of EDS data using a scanning electron microscope. Our method, based on the supervised learning approach for dynamic sampling algorithm and neural networks based classification of EDS data, allows a dramatic reduction in the total sampling of up to 90%, while maintaining the fidelity of the reconstructed elemental maps and spectroscopic data. In conclusion, we believe this approach will enable imaging and elemental mapping of materials that would otherwise be inaccessible to these analysis techniques.

36 MATERIALS SCIENCE↗
NASA NTRS2013

Dynamic Sampling of Cabin VOCs during the Mission Operations Test of the Deep Space Habitat

The atmospheric composition inside spacecraft is dynamic due to changes in crew metabolism and payload operations. A portable FTIR gas analyzer was used to monitor the atmospheric composition of four modules (Core lab, Veggie Plant Atrium, Hygiene module, and Xhab loft) within the Deep Space Habitat '(DSH) during the Mission Operations Test (MOT) conducted at the Johnson Space Center. The FTIR was either physically relocated to a new location or the plumbing was changed so that a different location was monitored. An application composed of 20 gases was used and the FTIR was zeroed using N2 gas every time it was relocated. The procedures developed for operating the FTIR were successful as all data was collected and the FTIR worked during the entire MOT mission. Not all the 20 gases in the application sampled were detected and it was possible to measure dynamic VOC concentrations in each DSH location.

Monje, Oscar↗
NASA NTRS2013

Dynamic Sampling of Trace Contaminants During the Mission Operations Test of the Deep Space Habitat

The atmospheric composition inside spacecraft during long duration space missions is dynamic due to changes in the living and working environment of crew members, crew metabolism and payload operations. A portable FTIR gas analyzer was used to monitor the atmospheric composition within the Deep Space Habitat (DSH) during the Mission Operations Test (MOT) conducted at the Johnson Space Center (JSC). The FTIR monitored up to 20 gases in near- real time. The procedures developed for operating the FTIR were successful and data was collected with the FTIR at 5 minute intervals. Not all the 20 gases sampled were detected in all the modules and it was possible to measure dynamic changes in trace contaminant concentrations that were related to crew activities involving exercise and meal preparation.

Monje, Oscar↗
DOE OSTI2023

Spatially-resolved dynamic sampling of different phasic magnetic resonances of nanoparticle ensembles in a magnetotactic bacterium Magnetospirillum magnetotacticum

Abstract Nanoscaled magnetic particle ensembles are promising building blocks for realizing magnon based binary logic. Element-specific real-space monitoring of magnetic resonance modes with sampling rates in the GHz regime is imperative for the experimental verification of future complex magnonic devices. Here we present the observation of different phasic magnetic resonance modes using the element-specific technique of time-resolved scanning transmission x-ray microscopy within a chain of dipolarly coupled Fe 3 O 4 nanoparticles (40–50 nm particle size) inside a single cell of a magnetotactic bacterium Magnetospirillum magnetotacticum . The particles are probed with 25 nm resolution at the Fe L 3 x-ray absorption edge in response to a microwave excitation of 4.07 GHz. A plethora of resonance modes is observed within multiple particle segments oscillating in- and out-of-phase, well resembled by micromagnetic simulations.

time-resolved scanning transmission x-ray microsco↗
DOE OSTI2017

An Expanded Chemo-dynamical Sample of Red Giants in the Bar of the Large Magellanic Cloud

We report new spectroscopic observations obtained with the Michigan/Magellan Fiber System of 308 red giants (RGs) located in two fields near the photometric center of the bar of the Large Magellanic Cloud. This sample consists of 131 stars observed in previous studies (in one field) and 177 newly observed stars (in the second field) selected specifically to more reliably establish the metallicity and age distributions of the bar. For each star, we measure its heliocentric line-of-sight velocity, surface gravity, and metallicity from its high-resolution spectrum (effective temperatures come from photometric colors). The spectroscopic Hertzsprung–Russell diagrams—modulo small offsets in surface gravities—reveal good agreement with model isochrones. The mean metallicity of the 177-RG sample is [Fe/H] = −0.76 ± 0.02 with a metallicity dispersion σ = 0.28 ± 0.03. The corresponding metallicity distribution—corrected for selection effects—is well fitted by two Gaussian components: one metal-rich with a mean −0.66 ± 0.02 and a standard deviation 0.17 ± 0.01, and the other metal-poor with −1.20 ± 0.24 and 0.41 ± 0.06. The metal-rich and metal-poor populations contain approximately 85% and 15% of stars, respectively. We also confirm that the velocity dispersion in the bar center decreases significantly from 31.2 ± 4.3 to 18.7 ± 1.9 km s{sup −1} with increasing metallicity over the range −2.09 to −0.38. Individual stellar masses are estimated using the spectroscopic surface gravities and the known luminosities. We find that lower mass, hence older, RGs have larger metallicity dispersion and lower mean metallicity than the higher-mass, younger RGs. The estimated masses, however, extend to implausibly low values (∼0.1 M{sub ⊙}), making it impossible to obtain an absolute age–metallicity or age distribution of the bar.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A device for volatile organic compound (VOC) analysis from skin using heated dynamic headspace sampling

Abstract Human skin is an important source of volatile organic compounds (VOCs) offering noninvasive methods to gain clinical metabolite information. This work was focused on the development of a skin sampling device based on a dynamic headspace sampling method with the addition of temperature to increase VOC metabolite recovery. The device preconcentrates skin VOC emissions onto a sorbent substrate, which can either be preserved for offline analysis or attached to a real time sensor downstream. In this work, skin VOC samples were analyzed offline using thermal desorption-gas chromatography-mass spectrometry. A list of 10 common skin VOCs was pre-selected to optimize parameters of sampling time, sampling temperature, and sorbent selection. Overall, this study highlights an effective skin VOC sampling technology with a heating dimension (40 °C, rather than 30 °C or no heating) with a sampling time of 15 min (rather than 5 or 30 mins) and onto Tenax TA sorbent (rather than PDMS), which collectively increases the recovery of compounds with lower vapor pressure and decreases the observed variability in skin VOC measurements. Finally, a list of 79 skin VOC compounds were detected and identified within a cohort of 20 young, healthy volunteers.

Biochemistry & Molecular Biology↗
DOE OSTI2018

Quantitative comparison of adaptive sampling methods for protein dynamics

Adaptive sampling methods, often used in combination with Markov state models, are becoming increasingly popular for speeding up rare events in simulation such as molecular dynamics (MD) without biasing the system dynamics. Several adaptive sampling strategies have been proposed, but it is not clear which methods perform better for different physical systems. Here, we present a systematic evaluation of selected adaptive sampling strategies on a wide selection of fast folding proteins. The adaptive sampling strategies were emulated using models constructed on already existing MD trajectories. We provide theoretical limits for the sampling speed-up and compare the performance of different strategies with and without using some a priori knowledge of the system. The results show that for different goals, different adaptive sampling strategies are optimal. In order to sample slow dynamical processes such as protein folding without a priori knowledge of the system, a strategy based on the identification of a set of metastable regions is consistently the most efficient, while a strategy based on the identification of microstates performs better if the goal is to explore newer regions of the conformational space. Interestingly, the maximum speed-up achievable for the adaptive sampling of slow processes increases for proteins with longer folding times, encouraging the application of these methods for the characterization of slower processes, beyond the fast-folding proteins considered here.

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

Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations

Multiscale simulations are a well-accepted way to bridge the length and time scales required for scientific studies with the solution accuracy achievable through available computational resources. Traditional approaches either solve a coarse model with selective refinement or coerce a detailed model into faster sampling, both of which have limitations. Here, we present a paradigm of adaptive, multiscale simulations that couple different scales using a dynamic-importance sampling approach. Our method uses machine learning to dynamically and exhaustively sample the phase space explored by a macro model using microscale simulations and enables an automatic feedback from the micro to the macro scale, leading to a self-healing multiscale simulation. As a result, our approach delivers macro length and time scales, but with the effective precision of the micro scale. Our approach is arbitrarily scalable as well as transferable to many different types of simulations. Overall, our method made possible a multiscale scientific campaign of unprecedented scale to understand the interactions of RAS proteins with a plasma membrane in the context of cancer research running over several days on Sierra, which is currently the second-most-powerful supercomputer in the world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations

We study the phase equilibrium between liquid water and ice Ih modeled by the TIP4P/Ice interatomic potential using enhanced sampling molecular dynamics simulations. Our approach is based on the calculation of ice Ih-liquid free energy differences from simulations that visit reversibly both phases. The reversible interconversion is achieved by introducing a static bias potential as a function of an order parameter. The order parameter was tailored to crystallize the hexagonal diamond structure of oxygen in ice Ih. Furthermore, we analyze the effect of the system size on the ice Ih-liquid free energy differences, and we obtain a melting temperature of 270 K in the thermodynamic limit. This result is in agreement with estimates from thermodynamic integration (272 K) and coexistence simulations (270 K). Since the order parameter does not include information about the coordinates of the protons, the spontaneously formed solid configurations contain proton disorder as expected for ice Ih.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Targeted quantification of functional enzyme dynamics in environmental samples for microbially mediated biogeochemical processes: Targeted quantification of functional enzyme dynamics

Microbially mediated biogeochemical processes are catalyzed by enzymes that control the transformation of carbon, nitrogen, and other elements in environment. The dynamic linkage between enzymes and biogeochemical species transformation has, however, rarely been investigated because of the lack of analytical approaches to efficiently and reliably quantify enzymes and their dynamics in soils and sediments. Herein, we developed a signature peptide-based technique for sensitively quantifying dissimilatory and assimilatory enzymes using nitrate-reducing enzymes in a hyporheic zone sediment as an example. Moreover, the measured changes in enzyme concentration were found to correlate with the nitrate reduction rate in a way different from that inferred from biogeochemical models based on biomass or functional genes as surrogates for functional enzymes. This phenomenon has important implications for understanding and modeling the dynamics of microbial community functions and biogeochemical processes in environments. Our results also demonstrate the importance of enzyme quantification for the identification and interrogation of those biogeochemical processes with low metabolite concentrations as a result of faster enzyme-catalyzed consumption of metabolites than their production. The dynamic enzyme behaviors provide a basis for the development of enzyme-based models to describe the relationship between the microbial community and biogeochemical processes.

Li, Minjing↗
DOE OSTI2022

Quashing a suspected selection bias in galaxy samples having dynamically measured supermassive black holes

Local early-type galaxies (ETGs) with directly measured black hole masses, M bh , have been reported to represent a biased sample relative to the ETG population at large. Such galaxies with Spitzer Space Telescope imaging have been purported to possess velocity dispersions, σ, at least ∼0.1 dex larger for a given galaxy stellar mass, M * , gal , than is typically observed among thousands of ETGs imaged by the Sloan Digital Sky Survey (SDSS). This apparent offset led Shankar et al. to reduce the normalisation of the observed M bh ∝ σ 5 relation by at least ∼0.5 dex to give their ‘intrinsic relations’, including σ-based modifications to the observed M bh –M * , gal relation. These modifications were based on the untested assumption that the stellar masses had been derived consistently between the two samples. Here, we provide the necessary check using galaxies common to the Spitzer Survey of Stellar Structure in Galaxies (S 4 G) and the SDSS. We find that the stellar masses of galaxies with and without directly measured black hole masses had appeared offset from each other due to the use of inconsistent stellar mass-to-light ratios, Υ * , for the optical and infrared data. We briefly discuss the ‘intrinsic relations’ and why some of these will at times appear to have had partial success when applied to data based on similarly inconsistent values ofΥ * . Finally, we reiterate the importance of the υ (lower-case Υ) term, which we previously introduced into the M bh –M * relations to help avoid Υ * -related mismatches.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Quantum chemical replica-exchange umbrella sampling molecular dynamics simulations reveal the formation mechanism of iron phthalocyanine from iron and phthalonitrile

Phthalocyanine (Pc) and its metal complexes (MPcs) have been used industrially since their discovery in the early 20th century. The phthalonitrile (PN) method is a well-known synthesis method in which Pc or MPc can be afforded by heating a mixture of PN and metal powders over 280 °C with only moderate yield. However, the formation mechanism of the phthalocyanines and the intermediate stages of this seemingly simple reaction have yet to be fully understood. To study this mechanism computationally, we carried out quantum chemical molecular dynamics (MD) simulations based on the density-functional tight-binding (DFTB) method, applying the replica-exchange umbrella sampling (REUS) method, starting from four PN molecules and one iron atom. The DFTB-REUS-MD simulations successfully yielded FePc, and a metastable structure very similar to FePc but with a reactive nitrene unit was also identified that might explain the incomplete conversion of the reactants into FePc. Furthermore, analysis of the MD trajectories reveals a three-step FePc formation mechanism for the PN method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗