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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Comparing bacterial community composition of healthy and dark spot-affected Siderastrea siderea in Florida and the Caribbean

Coral disease is one of the major causes of reef degradation. Dark Spot Syndrome (DSS) was described in the early 1990's as brown or purple amorphous areas of tissue on a coral and has since become one of the most prevalent diseases reported on Caribbean reefs. It has been identified in a number of coral species, but there is debate as to whether it is in fact the same disease in different corals. Further, it is questioned whether these macroscopic signs are in fact diagnostic of an infectious disease at all. The most commonly affected species in the Caribbean is the massive starlet coral Siderastrea siderea . We sampled this species in two locations, Dry Tortugas National Park and Virgin Islands National Park. Tissue biopsies were collected from both healthy colonies and those with dark spot lesions. Microbial-community DNA was extracted from coral samples (mucus, tissue, and skeleton), amplified using bacterial-specific primers, and applied to PhyloChip G3 microarrays to examine the bacterial diversity associated with this coral. Samples were also screened for the presence of a fungal ribotype that has recently been implicated as a causative agent of DSS in another coral species, but the amplifications were unsuccessful. S. siderea samples did not cluster consistently based on health state (i.e., normal versus dark spot). Various bacteria, including Cyanobacteria and Vibrios , were observed to have increased relative abundance in the discolored tissue, but the patterns were not consistent across all DSS samples. Overall, our findings do not support the hypothesis that DSS in S. siderea is linked to a bacterial pathogen or pathogens. This dataset provides the most comprehensive overview to date of the bacterial community associated with the scleractinian coral S. siderea .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Data Structures for Extreme Scale Computing

As computing problems of national importance grow, the government meets the increased demand by funding the development of ever larger systems. The overarching goal of the work supported in part by this grant is to increase efficiency of programming and performing computations on these large computing systems. In past work, we have demonstrated that some of these computations once thought to require expensive hardware designs and/or complex, special-purpose programming may be executed efficiently on low-cost commodity cluster computing systems using a general-purpose “latency-tolerant” programming framework. One important developed application of the ideas underlying this framework is graph database technology supporting social network pattern matching used by US intelligence agencies to more quickly identify potential terrorist threats. This database application has been spun out by the Pacific Northwest National Laboratory, a Department of Energy Laboratory, into a commercial start-up, Trovares Inc. We explore an alternative application of the same underlying ideas to a well-studied challenge arising in engineering: solving unstructured sparse linear equations. Solving these equations is key to predicting the behavior of large electronic circuits before they are fabricated. Predicting that behavior ahead of fabrication means that designs can optimized and errors corrected ahead of the expense of manufacture.

42 ENGINEERING↗
NASA NTRS1984

Preliminary Study of (2-D) Digital Filtering for Dealiasing NOSS Wind Direction Data

A pattern classification approach to wind streamline direction ambiguity removal is investigated. The basic approach involves establishing an appropriate measurement of a characterizing vector of the streamline orientation angle parameter surface. Clustering and training techniques are employed to distinguish major meteorological features from which directional information may be determined.

Stoughton, J. W.↗
NASA NTRS1994

Halococcus salifodinae sp. nov., an Archaeal Isolate from an Austrian Salt Mine

A novel extremely halophilic archaeon (archaebacterium) was isolated from rock salt obtained from an Austrian salt mine. The deposition of the salt is thought to have occurred during the Permian period (225 x 106 to 280 x 10(exp 6) years ago). This organism grew over a pH range of 6.8 to 9.5. Electron microscopy revealed cocci in tetrads or larger clusters. The partial 16S rRNA sequences, polar lipid composition, and menaquinone content suggested that this organism was related to members of the genus Halococcus, while the whole-cell protein patterns, the presence of several unknown lipids, and the presence of pink pigmentation indicated that it was different from previously described coccoid halophiles. We propose that this isolate should be recognized as a new species and should be named Halococcus salifodinae. The type strain is Bl(sub p) (= ATCC 51437 = DSM 8989). A chemotaxonomically similar microorganism was isolated from a British salt mine.

Denner, Ewald B. M.↗
DOE OSTI2018

Welding Defect Classification from Simulated Ultrasonic Signals

Nondestructive testing is widely used to detect and to size up discontinuities embedded in a material. Among the several ultrasonic techniques, time of flight diffraction (TOFD) combines high speed inspection, high sizing reliability and low rate of incorrect results. However, the classification of defects through ultrasound signals acquired by the TOFD technique depends heavily on the knowledge and experience of the operator and thus, this classification is still frequently questioned. Besides, this task requires long processing time due to the large amount of data to be analyzed. Nevertheless, computational tools for pattern recognition can be employed to analyze a high amount of data with large efficiency. In the present work, simulation of ultrasound propagation in two-dimensional media containing, each one, different kinds of modeled discontinuities which mimic defects in welded joints were performed. Clustering (k-means) and classification (principal component analysis and k-nearest neighbors) algorithms were employed to associate each simulated ultrasound signal with its corresponding modeled defects. The results for each method were analyzed, discussed and compared. The results are very promising.

42 ENGINEERING↗
DOE OSTI2017

Witnessing the growth of the nearest galaxy cluster: thermodynamics of the Virgo Cluster outskirts

Here, we present results from Suzaku Key Project observations of the Virgo Cluster, the nearest galaxy cluster to us, mapping its X-ray properties along four long ‘arms’ extending beyond the virial radius. The entropy profiles along all four azimuths increase with radius, then level out beyond ~0.5r 200 , while the average pressure at large radii exceeds Planck Sunyaev–Zel'dovich measurements. These results can be explained by enhanced gas density fluctuations (clumping) in the cluster's outskirts. Using a standard Navarro, Frenk and White model, we estimate a virial mass, radius and concentration parameter of M 200 = 1.05 ± 0.02 × 10 14 M⊙, r 200 = 974.1 ± 5.7 kpc and c = 8.8 ± 0.2, respectively. The inferred cumulative baryon fraction exceeds the cosmic mean at r ~r 200 along the major axis, suggesting enhanced gas clumping possibly sourced by a candidate large-scale structure filament along the north–south direction. The Suzaku data reveal a large-scale sloshing pattern, with two new cold fronts detected at radii of 233 and 280 kpc along the western and southern arms, respectively. Two high-temperature regions are also identified 1 Mpc towards the south and 605 kpc towards the west of M87, likely representing shocks associated with the ongoing cluster growth. Although systematic uncertainties in measuring the metallicity for low-temperature plasma remain, the data at large radii appear consistent with a uniform metal distribution on scales of ~90 × 180 kpc and larger, providing additional support for the early chemical enrichment scenario driven by galactic winds at redshifts of 2–3.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

PROFILES OF DARK MATTER VELOCITY ANISOTROPY IN SIMULATED CLUSTERS

We report statistical results for dark matter (DM) velocity anisotropy, {beta}, from a sample of some 6000 cluster-size halos (at redshift zero) identified in a {Lambda}CDM hydrodynamical adaptive mesh refinement simulation performed with the ENZO code. These include profiles of {beta} in clusters with different masses, relaxation states, and at several redshifts, modeled both as spherical and triaxial DM configurations. Specifically, although we find a large scatter in the DM velocity anisotropy profiles of different halos (across elliptical shells extending to at least {approx}1.5r{sub vir}), universal patterns are found when these are averaged over halo mass, redshift, and relaxation stage. These are characterized by a very small velocity anisotropy at the halo center, increasing outward to {approx}0.27 and leveling off at {approx}0.2r{sub vir}. Indirect measurements of the DM velocity anisotropy fall on the upper end of the theoretically expected range. Though measured indirectly, the estimations are derived by using two different surrogate measurements-X-ray and galaxy dynamics. Current estimates of the DM velocity anisotropy are based on a very small cluster sample. Increasing this sample will allow theoretical predictions to be tested, including the speculation that the decay of DM particles results in a large velocity boost. We also find, in accord with previous works, that halos are triaxial and likely to be more prolate when unrelaxed, whereas relaxed halos are more likely to be oblate. Our analysis does not indicate that there is significant correlation (found in some previous studies) between the radial density slope, {gamma}, and {beta} at large radii, 0.3 r{sub vir} < r < r{sub vir}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Three-Dimensional Patterning of Nanoparticles by Molecular Stamping

Directing the formation of nanoscale architectures from nanoparticles is one of the key challenges in designing nanomaterials with prescribed functions. Atomic systems, given their ability to form molecules and crystals via directional chemical bonds, provide an inspiration for establishing approaches where nanoparticles with designed anisotropic binding modalities can be assembled into nanoscale architectures. However, fabricating such nanoparticles has been challenging due to their small dimensions and limited ways for site-specific control of their surface. To this end, we present a Molecular Stamping (MOST) approach to pattern DNA-coated nanoparticles with molecules at the predefined positions on a nanoparticle surface. This patterning is realized by use of a rigid and coordinative DNA frame as a molecular stamping apparatus (MOST App). The MOST App transfers multiple types of molecular “inks”, DNA sequences, onto nanoparticle surface and fixes these molecular inks into place to form a designed pattern. After a nanoparticle release from MOST App, it possesses single-molecule patches that can provide anisotropic bonds with distinctive affinities. We further use these stamped nanoparticles to assemble prescribed clusters, whose structure is determined by the locations of patches. Using electron microscopy and tomographic methods, we investigate the efficiency of cluster formation and the resulting spatial arrangements of nanoparticles. The presented approach provides a single-molecule and spatiallydetermined control over nanoparticle functionalization for creating nanoparticles with designed placement of different molecules and for realizing a rational fabrication of nanomaterial architectures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Cluster perturbation theory. IV. Convergence of cluster perturbation series for energies and molecular properties

The theoretical foundation has been developed for establishing whether cluster perturbation (CP) series for the energy, molecular properties, and excitation energies are convergent or divergent and for using a two-state model to describe the convergence rate and convergence patterns of the higher-order terms in the CP series. To establish whether the perturbation series are convergent or divergent, a fictitious system is introduced, for which the perturbation is multiplied by a complex scaling parameter z . The requirement for convergent perturbation series becomes that the energy or molecular property, including an excitation energy, for the fictitious system is an analytic, algebraic function of z that has no singularities when the norm | z | is smaller than one. Examples of CP series for the energy and molecular properties, including excitation energies, are also presented, and the two-state model is used for the interpretation of the convergence rate and the convergence patterns of the higher-order terms in these series. Finally, the calculations show that the perturbation series effectively become a two-state model at higher orders.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

DETECTION OF SMALL-SCALE GRANULAR STRUCTURES IN THE QUIET SUN WITH THE NEW SOLAR TELESCOPE

Results of a statistical analysis of solar granulation are presented. A data set of 36 images of a quiet-Sun area on the solar disk center was used. The data were obtained with the 1.6 m clear aperture New Solar Telescope at Big Bear Solar Observatory and with a broadband filter centered at the TiO (705.7 nm) spectral line. The very high spatial resolution of the data (diffraction limit of 77 km and pixel scale of 0.''0375) augmented by the very high image contrast (15.5% {+-} 0.6%) allowed us to detect for the first time a distinct subpopulation of mini-granular structures. These structures are dominant on spatial scales below 600 km. Their size is distributed as a power law with an index of -1.8 (which is close to the Kolmogorov's -5/3 law) and no predominant scale. The regular granules display a Gaussian (normal) size distribution with a mean diameter of 1050 km. Mini-granular structures contribute significantly to the total granular area. They are predominantly confined to the wide dark lanes between regular granules and often form chains and clusters, but different from magnetic bright points. A multi-fractality test reveals that the structures smaller than 600 km represent a multi-fractal, whereas on larger scales the granulation pattern shows no multi-fractality and can be considered as a Gaussian random field. The origin, properties, and role of the population of mini-granular structures in the solar magnetoconvection are yet to be explored.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1984

Pattern recognition for Space Applications Center director's discretionary fund

Results and conclusions are presented on the application of recent developments in pattern recognition to spacecraft star mapping systems. Sensor data for two representative starfields are processed by an adaptive shape-seeking version of the Fc-V algorithm with good results. Cluster validity measures are evaluated, but not found especially useful to this application. Recommendations are given two system configurations worthy of additional study,

Singley, M. E.↗
NASA NTRS1992

A fuzzy clustering algorithm to detect planar and quadric shapes

In this paper, we introduce a new fuzzy clustering algorithm to detect an unknown number of planar and quadric shapes in noisy data. The proposed algorithm is computationally and implementationally simple, and it overcomes many of the drawbacks of the existing algorithms that have been proposed for similar tasks. Since the clustering is performed in the original image space, and since no features need to be computed, this approach is particularly suited for sparse data. The algorithm may also be used in pattern recognition applications.

Krishnapuram, Raghu↗
NASA NTRS2022

Aerosols Optical and Radiative Properties in Indonesia Based on AERONET Version 3

Heterogeneity in seasonal aerosol types due to different sources, local characteristics, and meteorology lead to different optical and radiative properties from one place to another. This study analyzes the long-term averages of aerosol optical and radiative properties obtained by the Aerosol Robotic Network (AERONET) version 3 at four sites in Indonesia representing fire prone areas (Palangka Raya, Pontianak, Jambi: 2012–2020), and urban-industrial (Bandung: 2009–2018). We examined the seasonal variation of aerosol optical depth (AOD), Angstrom Exponent (AE), Precipitable Water Vapor (PWV), and Single Scattering Albedo (SSA) combined with meteorological parameters at these Indonesian sites. Multiple clustering techniques from different relationships among various aerosol optical and radiative parameters was employed to further study the aerosol type in respect to different seasonality. The results exhibited a distinct seasonal pattern at all sites except Bandung, whereas AOD increased owing to forest and peat fire outbreaks during the dry season. The trend in AOD annual depicts an increase in Bandung as emission from urban/industrial increases episodically, while it depicts a decrease at the three biomass burning sites. As AOD increased, Palangka Raya, Pontianak, and Jambi exhibited wider ranges of observed AE (1.2–1.8) and high FMF (>0.8) in the dry burning period (June to October). On the other hand, Bandung has more aerosol distribution in the range of FMF values 0.6–1 and AE 1–1.6, compared with other sites, suggesting contribution of complex aerosol emission from different sources and likely due to the formation of secondary aerosol production from trace gases. Palangka Raya, Pontianak, and Jambi exhibited non-absorbing characteristics, as indicated by high SSA (>0.95), high FMF, and high EAE values (1.2–1.7) with no discrepancy in seasonal patterns, implying that biomass burning aerosols in Indonesia tend to cool the atmosphere with strong scattering rather than absorbing. At Bandung, aerosols mixed with slightly absorbing and non-absorbing aerosols owing to urban and industrial emissions. This study lays a foundation for further research on the contribution of aerosol radiative forcing to climate within the complex aerosol and cloud environments in Indonesia.

Aerosol optical depth↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
DOE OSTI2012

THE BIZARRE CHEMICAL INVENTORY OF NGC 2419, AN EXTREME OUTER HALO GLOBULAR CLUSTER

We present new Keck/HIRES observations of six red giants in the globular cluster (GC) NGC 2419. Although the cluster is among the most distant and most luminous in the Milky Way, it was considered chemically ordinary until very recently. Our previous work showed that the near-infrared Ca II triplet line strength varied more than expected for a chemically homogeneous cluster, and that at least one star had unusual abundances of Mg and K. Here, we confirm that NGC 2419 harbors a population of stars, comprising about one-third of its mass, that is depleted in Mg by a factor of eight and enhanced in K by a factor of six with respect to the Mg-normal population. Although the majority, Mg-normal population appears to have a chemical abundance pattern indistinguishable from ordinary, inner-halo GCs, the Mg-poor population exhibits dispersions of several elements. The abundances of K and Sc are strongly anti-correlated with Mg, and some other elements (Si and Ca among others) are weakly anti-correlated with Mg. These abundance patterns suggest that the different populations of NGC 2419 sample the ejecta of diverse supernovae in addition to asymptotic giant branch ejecta. However, the abundances of Fe-peak elements except Sc show no star-to-star variation. We find no nucleosynthetic source that satisfactorily explains all of the abundance variations in this cluster. Because NGC 2419 appears like no other GC, we reiterate our previous suggestion that it is not a GC at all, but rather the core of an accreted dwarf galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A SPECTROSCOPIC ANALYSIS OF THE GALACTIC GLOBULAR CLUSTER NGC 6273 (M19)

A combined effort utilizing spectroscopy and photometry has revealed the existence of a new globular cluster class. These “anomalous” clusters, which we refer to as “iron-complex” clusters, are differentiated from normal clusters by exhibiting large (≳0.10 dex) intrinsic metallicity dispersions, complex sub-giant branches, and correlated [Fe/H] and s-process enhancements. In order to further investigate this phenomenon, we have measured radial velocities and chemical abundances for red giant branch stars in the massive, but scarcely studied, globular cluster NGC 6273. The velocities and abundances were determined using high resolution (R ∼ 27,000) spectra obtained with the Michigan/Magellan Fiber System (M2FS) and MSpec spectrograph on the Magellan–Clay 6.5 m telescope at Las Campanas Observatory. We find that NGC 6273 has an average heliocentric radial velocity of +144.49 km s{sup −1} (σ = 9.64 km s{sup −1}) and an extended metallicity distribution ([Fe/H] = −1.80 to −1.30) composed of at least two distinct stellar populations. Although the two dominant populations have similar [Na/Fe], [Al/Fe], and [α/Fe] abundance patterns, the more metal-rich stars exhibit significant [La/Fe] enhancements. The [La/Eu] data indicate that the increase in [La/Fe] is due to almost pure s-process enrichment. A third more metal-rich population with low [X/Fe] ratios may also be present. Therefore, NGC 6273 joins clusters such as ω Centauri, M2, M22, and NGC 5286 as a new class of iron-complex clusters exhibiting complicated star formation histories.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

NMR spin relaxation in proteins: The patterns of motion that dissipate power to the bath

We developed in recent years the two-body coupled-rotator slowly relaxing local structure (SRLS) approach for the analysis of NMR relaxation in proteins. The two bodies/rotators are the protein (diffusion tensor D{sub 1}) and the spin-bearing probe, e.g., the {sup 15}N−{sup 1}H bond (diffusion tensor, D{sub 2}), coupled by a local potential (u). A Smoluchowski equation is solved to yield the generic time correlation functions (TCFs), which are sums of weighted exponentials (eigenmodes). By Fourier transformation one obtains the generic spectral density functions (SDFs) which underlie the experimental relaxation parameters. The typical paradigm is to characterize structural dynamics in terms of the best-fit values of D{sub 1}, D{sub 2}, and u. Additional approaches we pursued employ the SRLS TCFs, SDFs, or eigenmodes as descriptors. In this study we develop yet another perspective. We consider the SDF as function of the angular velocity associated with the fluctuating fields underlying NMR relaxation. A parameter called j-fraction, which represents the relative contribution of eigenmode, i, to a given value of the SDF function at a specific frequency, ω, is defined. j-fraction profiles of the dominant eigenmodes are derived for 0 ≤ ω ≤ 10{sup 12} rad/s. They reveal which patterns of motion actuate power dissipation at given ω-values, what are their rates, and what is their relative contribution. Simulations are carried out to determine the effect of timescale separation, D{sub 1}/D{sub 2}, axial potential strength, and local diffusion axiality. For D{sub 1}/D{sub 2} ≤ 0.01 and strong local potential of 15 k{sub B}T, power is dissipated by global diffusion, renormalized (by the strong potential) local diffusion, and probe diffusion on the surface of a cone (to be called cone diffusion). For D{sub 1}/D{sub 2} = 0.1, power is dissipated by mixed eigenmodes largely of a global-diffusion-type or cone-diffusion-type, and a nearly bare renormalized-local-diffusion eigenmode. For D{sub 1}/D{sub 2} > 0.1, most eigenmodes are of a mixed type. The analysis is affected substantially by reducing the potential strength from 15 to 5 k{sub B}T, and/or allowing for axial D{sub 2} with D{sub 2,∥}/D{sub 2,⊥} = 10. The scheme developed is applied to {sup 15}N−{sup 1}H relaxation from the β-sheet residue K19 and the α-helix residue A34 of the third immunoglobulin-binding domain of streptococcal protein G. Previous studies revealed rhombic local potentials with different rhombicity around C{sub i−1}{sup α}−C{sub i}{sup α}, and different timescale separation (0.047 for K19 and 0.102 for A34). Here, we find that K19 and A34 dissipate power to the bath through global diffusion, mixed cone-diffusion-related and mixed renormalized-local-diffusion-related motions. At small ω-values, A34 is more effective than K19 in dissipating power. In general, it executes faster cone-diffusion-type, and slower renormalized-local-diffusion-type and local-probe-fluctuation-type motions. K19 experiences faster N−H fluctuations than A34. Eigenmode clustering, experienced by K19 to a larger extent, is observed in the fast-probe-fluctuation regime. New information on the effect of the structural context on N−H bond dynamics has been obtained. The patterns of motion that dissipate NMR-relaxation-related power illuminate protein dynamics from a new perspective. They constitute yet another qualifier of N−H bond dynamics. This study sets the stage for developing ways for enhancing the contribution of desired pathways for power dissipation at selected angular velocities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗