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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

MATEY: multiscale adaptive transformer models for spatiotemporal physical systems

Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer architectures requires extremely long, computationally prohibitive token sequences. To address this issue, we propose two novel adaptive tokenization schemes that dynamically adjust patch sizes based on local features: one ensures convergent behavior to uniform patch refinement, while the other offers better computational efficiency. Moreover, we present a set of spatiotemporal attention schemes, where the temporal or axial spatial dimensions are decoupled, to evaluate their baseline computational and data efficiencies and to determine whether adaptive tokenization can improve this performance. We assess the performance of the proposed multiscale adaptive model, MATEY, in a sequence of experiments. Compared to a full spatiotemporal attention scheme or a scheme that decouples only the temporal dimension, we find that fully decoupled axial attention is less efficient and expressive, requiring more training time and model parameters to achieve the same accuracy. The experiments on the adaptive tokenization schemes show that, compared to a uniformly refined model, the proposed schemes achieve comparable or improved accuracy at a much lower cost in the tested two-dimensional settings. While the asymptotic analysis suggests the potential for favorable scaling, empirical validation at substantially longer sequence lengths remains to be performed in future work. Finally, we demonstrate in two fine-tuning tasks featuring different physics that models pretrained on PDEBench data outperform the ones trained from scratch, especially in the low data regime with frozen attention.

adaptive tokenization↗
DOE OSTI2020

Q-Learning-Based Impact Assessment of Propagating Extreme Weather on Distribution Grids

The increasing number of power outage events due to extreme weather conditions is hampering us socioeconomically. Preparing in advance for the extreme weather event is critical. It can help utility operators to reduce grid damages, restore grid service quickly, allocate energy resources and repair crews strategically, and hence dramatically increase grid resilience. In this paper, we propose a method to identify the sequence of worst impact zones in the power grid caused by extreme weather events based on Q-learning (a reinforcement learning algorithm). To quantity the weather severity and its effect on the grid, we model the impact of extreme weather on the grid as a function of intensity, vulnerability, and exposure. A modified IEEE 123-node distribution feeder is presented in a mesh grid and experimented for sequences of zones identification. Finally, simulation results present the identified sequences and their associated impacts on the grid caused by extreme weather events.

distribution system↗
DOE OSTI2021

Q-Learning Based Impact Assessment of Propagating Extreme Weather on Distribution Grids: Preprint

Increasing number of power outage events due to extreme weather condition is hampering us socioeconomically. Preparing in advance for the extreme weather event is critical and can help utility operators to reduce grid damages, restore grid service quickly, allocate energy resources and repair crews strategically, and hence dramatically increase grid resilience. In this paper, we propose a method to identify the sequence of worst impact zones in the power grid caused by extreme weather events based on Q-learning (a reinforcement learning algorithm). To quantify weather severity and it’s effect on the grid, we model the impact of extreme weather on the grid as a function of intensity, vulnerability and exposure. A modified IEEE 123-node distribution feeder is presented in a mesh grid and experimented for sequences of zones identification. Finally, simulation results present the identified sequences and their associated impacts on the grid caused by the extreme weather events.

distribution system↗
DOE OSTI2019

Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization

© 2019 Association for Computing Machinery. The goal of this work is to investigate the impact of missing values in clustering joint categorical social sequences. Identifying patterns in sociodemographic longitudinal data is important in a number of social science settings. However, performing analytical operations, such as clustering on life course trajectories, is challenging due to the categorical and multidimensional nature of the data, their mixed data types, and corruption by missing and inconsistent values. Data quality issues were investigated previously on single variable sequences. To understand their effects on multivariate sequence analysis, we employ a dataset of mixed data types and missing values, a dissimilarity measure designed for joint categorical sequence data, together with dimensionality reduction methodologies in a systematic design of sequence clustering experiments. Given the categorical nature of our data, we employ an “edit” distance using optimal matching. Because each data record has multiple variables of different types, we investigate the impact of mixing these variables in a single dissimilarity measure. Between variables with binary values and those with multiple nominal values, we find that the ability to overcome missing data problems is more difficult in the nominal domain than in the binary domain. Additionally, alignment of leading missing values can result in systematic biases in dissimilarity matrices and subsequently introduce both artificial clusters and unrealistic interpretations of associated data domains. We demonstrate the usage of t-distributed stochastic neighborhood embedding to visually guide mitigation of such biases by tuning the missing value substitution cost parameter or determining an optimal sequence span.

Lazar, Alina↗
DOE OSTI2023

Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning

We present a mathematical framework and computational methods for optimally designing a finite sequence of experiments. This sequential optimal experimental design (sOED) problem is formulated as a finite-horizon partially observable Markov decision process (POMDP) under a Bayesian setting and with information-theoretic utilities. The formulation is general and may accommodate continuous random variables, non-Gaussian posteriors, and nonlinear forward models. The sOED design policy incorporates elements of feedback and lookahead simultaneously, and we show it to generalize the commonly-used batch and greedy design strategies. We solve for the sOED policy using the policy gradient (PG) method from reinforcement learning, and provide a derivation for the PG expression in the sOED context. Adopting an actor-critic approach, the policy and value functions are parameterized using deep neural networks and improved via PG estimates produced from simulated episodes of designs and observations. The new PG-sOED algorithm is first validated on a linear-Gaussian benchmark, and then compared against other design baselines on a sensor movement problem for contaminant source inversion in a convection-diffusion field. As a result, we provide explanation for the policy behaviors using knowledge of the underlying physical process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Characterization of a novel polyextremotolerant fungus, Exophiala viscosa , with insights into its melanin regulation and ecological niche

Black yeasts are polyextremotolerant fungi that contain high amounts of melanin in their cell wall and maintain a primary yeast form. These fungi grow in xeric, nutrient depletes environments which implies that they require highly flexible metabolisms and have been suggested to contain the ability to form lichen-like mutualisms with nearby algae and bacteria. However, the exact ecological niche and interactions between these fungi and their surrounding community are not well understood. We have isolated 2 novel black yeasts from the genus Exophiala that were recovered from dryland biological soil crusts. Despite notable differences in colony and cellular morphology, both fungi appear to be members of the same species, which has been named Exophiala viscosa (i.e. E. viscosa JF 03-3 Goopy and E. viscosa JF 03-4F Slimy). A combination of whole genome sequencing, phenotypic experiments, and melanin regulation experiments have been performed on these isolates to fully characterize these fungi and help decipher their fundamental niche within the biological soil crust consortium. Our results reveal that E. viscosa is capable of utilizing a wide variety of carbon and nitrogen sources potentially derived from symbiotic microbes, can withstand many forms of abiotic stresses, and excretes melanin which can potentially provide ultraviolet resistance to the biological soil crust community. Besides the identification of a novel species within the genus Exophiala, our study also provides new insight into the regulation of melanin production in polyextremotolerant fungi.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Learning Heterogeneous Features Jointly: A Deep End-to-End Framework for Multi-Step Short-Term Wind Power Prediction

Leveraging multiple heterogeneous measurements to predict wind power has long been a challenging task in the electrical community. In this paper, a deep architecture incorporated with multitask learning and multimodal learning for wind power prediction, termed predictive stacked autoencoder (PSAE), is presented. PSAE is a unified framework integrating multiple stacked autoencoders (SAEs), one feature fusion layer, and one prediction terminal layer, which expands the architecture from two spatial dimensions, including the depth and width, compared to conventional prediction models. Initially, the SAEs at the bottom of PSAE extracted features from multiple kinds of measurements respectively. Following, the feature fusion layer encodes the high-order features extracted by different SAEs into a unified feature that is more informative and representative for wind power prediction. Finally, the prediction terminal layer functions as a regression machine which generates the predicted targets based on the fusion features. Trained in an end-to-end (E2E) manner, PSAE is capable of learning heterogeneous features jointly and achieving the prediction task of sequence-to-sequence (S2S). Experiments for multi-step short-term predictions are conducted on real-world data, and the results demonstrate the superiority of PSAE to prior methods.

17 WIND ENERGY↗
DOE OSTI2021

Fungal-Bacterial Cooccurrence Patterns Differ between Arbuscular Mycorrhizal Fungi and Nonmycorrhizal Fungi across Soil Niches

Soil bacteria and fungi are known to form niche-specific communities that differ between actively growing and decaying roots. Yet almost nothing is known about the cross-kingdom interactions that frame these communities and the environmental filtering that defines these potentially friendly or competing neighbors. We explored the temporal and spatial patterns of soil fungal (mycorrhizal and nonmycorrhizal) and bacterial cooccurrence near roots of wild oat grass, Avena fatua, growing in its naturalized soil in a greenhouse experiment. Amplicon sequences of the fungal internal transcribed spacer (ITS) and bacterial 16S rRNA genes from rhizosphere and bulk soils collected at multiple plant growth stages were used to construct covariation-based networks as a step toward identifying fungal-bacterial associations. Corresponding stable-isotope-enabled metagenome-assembled genomes (MAGs) of bacteria identified in cooccurrence networks were used to inform potential mechanisms underlying the observed links. Bacterial-fungal networks were significantly different in rhizosphere versus bulk soils and between arbuscular mycorrhizal fungi (AMF) and nonmycorrhizal fungi. Over 12 weeks of plant growth, nonmycorrhizal fungi formed increasingly complex networks with bacteria in rhizosphere soils, while AMF more frequently formed networks with bacteria in bulk soils. Analysis of network-associated bacterial MAGs suggests that some of the fungal-bacterial links that we identified are potential indicators of bacterial breakdown and consumption of fungal biomass, while others intimate shared ecological niches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ORNL_AISD-Ex: Quantum chemical prediction of UV/Vis absorption spectra for over 10 million organic molecules

We performed calculations of electronic excitation energies and associated oscillator strengths based on the time-dependent density-functional tight-binding (TD-DFTB) method [1]. The SMILES (Simplified molecular-input line-entry system) strings of the molecules from the AISD HOMO-LUMO database [2] were converted to a 3D atomistic structure and stored in a PDB file after preliminary geometry optimization using the Merck Molecular Force Field (MMFF94) in RDKit [3,4]. The primary information stored in the PDB file archive consists of Cartesian coordinates for each atom of the molecule in their 3D location in space, along with summary information about the structure, sequence, and experiment. We then performed molecular geometry optimization using the density-functional tight-binding (DFTB) method [5] in the electronic ground state, followed by single-point excited states calculations, as described below. We note that, since RDKit employs a random choice for the generation of molecular conformers, the molecular geometries obtained in this dataset could be different from the ones that were generated when the AISD HOMO-LUMO dataset was generated. The computed excitation energies and associated oscillator strengths can be converted to predict UV/Vis absorption spectra, where excitation energies correspond to absorption peak positions, and oscillator strengths are a good measure of the probability of absorption of visible or UV light in transitions between electronic ground and excited states. The conversion of SMILES strings to 3D Cartesian coordinates of fully DFTB-optimized molecules was successful for 10,502,904 out of 10,502,917 molecules. For these molecules, both geometry optimizations and excited states calculations were successful. The DFTB calculations did not complete for 13 molecules of the original AISD HOMO-LUMO dataset. We still provide information about the geometry of these molecules. The molecules are diverse for chemical compositions (which span 5 non-hydrogen elements: oxygen, carbon, nitrogen, fluorine, sulfur) and molecular size (the smallest molecule contains 5 non-hydrogen atoms, and the largest molecule contains 71 non-hydrogen atoms). The DFTB method [5] is an approximation to density functional theory (DFT), utilizing a minimal basis set in conjunction with a two-center approximation to the electronic Hamiltonian and overlap matrix elements. The DFTB total energy is the sum of an electronic and a repulsive energy contribution, and their calculation requires optimized electronic parameters and diatomic repulsive potential energy functions. All DFTB calculations were performed using the DFTB+ code [6] (version 21.2) and the wrapper for DFTB+ in the Atomic Simulation Environment (ASE) (version 3.22.1) [7], which performed an internal conversion of Cartesian coordinates from PDB to the .gen file format. For the geometry optimizations on the electronic ground state potential energy surface of the molecules, we have chosen the third-order DFTB (DFTB3) method [5c] and employed the matching 3ob set of electronic parameters and repulsive potentials [8]. The empirical γ-damping for hydrogen bond correction, and Grimme's D3 empirical dispersion correction with Becke-Johnson damping (D3(BJ)) [9] dispersion correction was included to improve the description of non-covalent interactions. For excited states single-point energy calculations, we employed the TD-DFTB method in conjunction with the DFTB2 method [5b] and the matching mio [5b,10] and halorg [11] parameter sets. We opted to request the simultaneous calculation of 50 excited states for singlet transition to investigate sufficient number of excited states, based on linear response theory using the Casida equation [Ref: T. A. Niehaus, S. Suhai, F. Della Sala, P Lugli, M. Elstner, G. Seifert, and Th. Frauenheim. Tight-binding approach to time-dependent density-functional response theory. Phys. Rev. B, 63:085108, 2001] and the ARPACK diagonalizer [R. B. Lehoucq, D. C. Sorensen, and C. Yang. Arpack users guide: Solution of large-scale eigenvalue problems by implicitly restarted arnoldi methods, 1997. 46, 51]. The dataset contains 1001 tar.gz files. Tar files are named as “ornl_aisd_ex_1.tar.gz†through “ornl_aisd_ex_1000.tar.gzâ€. Additionally, the 13 failed molecules are in “ornl_aisd_ex_unprocessed.tar.gzâ€. Except for the tar files listed below, each tar file contains 10,500 molecules. Tar files numbered 34, 121, 128, 352, 360, 429, 495, 509, 518, 627, 676, 668, and 862 contain 10,499 molecules each. The last tar file numbered 1000 contains 13,417 molecules. The total size of the uncompressed dataset is over 283 Gigabytes. The code for calculating the electronic excitation energies and statistical analysis of the dataset is provided at the following GitLab repository: https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python Calculating the UV spectrum of a molecule requires performing 3 main operations: 1. Converting the smiles string representation of a molecule into a geometric structure where each atom is assigned XYZ coordinates. The geometric structure is written to the file smiles.pdb. 2. Using smiles.pdb to compute the relaxed geometry of the molecule, which corresponds with the position of the atoms at the position of equilibrium at the ground state. This generates the files band.out, detailed.out, and geo_end.gen. 3. Using geo_end.gen to calculate the UV spectrum of the molecule which is written into the file EXC.DAT. Every molecule in the dataset has its own directory. The files contained in each molecule directory are as follows: 1. geo_end.gen 2. detailed.out 3. band.out 4. EXC.DAT 5. smiles.pdb REFERENCES [1] Niehaus, T. A.; Suhai, S.; Della Salla, F.; Lugli, P.; Elstner, M.; Seifert, G.; Frauenheim, Th. Tight-binding approach to time-dependent density-functional response theory. Phys. Rev. B, 2001, 63, 085108/1-9. [2] Blanchard, A.; Gounley, J.; Metha, K.; Yoo, P.; Irle, S. AISD HOMO-LUMO. DOI: 10.13139/ORNLNCCS/1869409 [3] RDKit: Cheminformatics and Machine Learning Software. 2013, [http://www.rdkit.org] [4] Tosco, P.; Stiefl, N. and Landrum, G. Bringing the MMFF force field to the RDKit: implementation and validation. J Cheminform. 2014, 6, 1–4. [5] a) Porezag, D.; Frauenheim, T.; Kohler, T.; Seifert, G.; Kaschner, Construction of tight-binding-like potentials on the basis of density-functional theory: Application to carbon, R. Phys. Rev. B 1995, 51, 12947-12957; b) Elstner, M.; Porezag, D.; Jungnickel, G.; Elsner, J.; Haugk, M.; Frauenheim, Th.; Suhai, S.; Seifert, G.; Phys. Rev. B 1998, 58, 7260-7268; c) Gaus, M.; Cui, Q.; Elstner, M. DFTB3: Extension of the Self-Consistent-Charge Density-Functional Tight-Binding Method (SCC-DFTB), J. Chem. Theory Comput. 2011, 7, 931-948; d) Cui, Q.; Elstner, M. Density functional tight binding: values of semi-empirical methods in an ab initio era, Phys. Chem. Chem. Phys. 2014, 16, 14368-14377. [6] Hourahine, B. et al. DFTB+, a software package for efficient approximate density functional theory based atomistic simulations, J. Chem. Phys. 2020, 152, 124101/1-19. [7] Larsen, A. H. et al. The atomic simulation environment—a Python library for working with atoms. J. Phys.: Cond. Matter 2017, 29, 273002. [8] Kubillus, M.; Kubar, T.; Gaus, M.; Rezac, J.; Elstner, M. Parameterization of the DFTB3 Method for Br, Ca, Cl, F, I, K, and Na in Organic and Biological Systems, J. Chem. Theory Comput. 2015, 11, 332-342. [9] Brandenburg, J. G.; Grimme, S. Accurate Modeling of Organic Molecular Crystals by Dispersion-Corrected Density Functional Tight Binding (DFTB), J. Phys. Chem. Lett. 2014, 5, 1785−1789. [10] a) Niehaus, T. A.; Elstner, M.; Frauenheim, Th.; Suhai, S. Application of an approximate density-functional method to sulfur containing compounds. J. Mol. Struct.: THEOCHEM 2001, 541, 185-94; b) Elstner, M.; Hobza, P.; Frauenheim, Th.; Suhai, S.; Kaxiras, E. Hydrogen bonding and stacking interactions of nucleic acid base pairs: A density-functional-theory based treatment. J. Chem. Phys. 2001, 114, 5149-55. [11] Kubar, T.; Bodrog, Z.; Gaus, M.; Köhler, C.; Aradi, B.; Frauenheim, Th.; Elstner, M. Parametrization of the SCC-DFTB Method for Halogens. J. Chem. Theory Comput. 2013, 9, 2939-49.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Role of man in flight experiment payloads, phase 1

The identification of required data for studies of Spacelab experiment functional allocation, the development of an approach to collecting these data from the payload community, and the specification of analytical methods necessary to quantitatively determine the role of man in specific Spacelab experiments are presented. A generalized Spacelab experiment operation sequence was developed, and the parameters necessary to describe each signle function in the sequence were identified. A set of functional descriptor worksheets were also drawn up. The methodological approach to defining the role of man was defined as a series of trade studies using a digial simulation technique. The tradeoff variables identified include scientific crew size, skill mix, and location. An existing digital simulation program suitable for the required analyses was identified and obtained.

Malone, T. B.↗
NASA NTRS1980

Trace constituents in the middle atmosphere by high resolution UV spectroscopy

An array of 5 autonomous spectrometers, the imaging spectrometric observatory covers a broad wavelength range (approximately 200 to 12,000 A), has a resolution selectable down to approximately 0.5 A, and a dynamic range of approximately 10 to the 7th power and is designed to select experiment measurement sequences by software control. Because current models of thermospheric ionic processes produce too much N2(+) ionization, the N2(+) reaction with O and the chemistry of metastable (N(+) ions and of O2(+) ions are objects of study on Spacelab 1.

Torr, D. G.↗
NASA NTRS1984

The Representation of Action Plans in Long Term Memory

A sequence of experiments conducted on a two hand chord typewriter, to compare the efficiency of different coding principles employed to associate letters with their chord productions is described. This keyboard represents an effort to identify effective alternatives to the existing typewriter. It consists of two seperate 5-key panels (one for each hand), and letters are entered by typing chords composed of one to five fingers. Each panel is capable of producing the full alphabet. One group of experiments was designed to separate between perceptual and motor factors in the acivation of single letter chords. The results underline the importance of perceptual factors in the activation of motor plans. The complexity of the patterns employed to represent letters was shown to account for 50 percent of variance in the typing speeds of single letters. The theoretical implications of these results are discussed in relation to a vision based theory of action plans.

Fussfeld, G. N.↗
NASA NTRS1988

The NASA/JPL multifrequency, multipolarisation airborne SAR system

Polarimetric synthetic aperture radars, operating at L-, C- and P-band, were designed to replace and upgrade a system destroyed in an aircraft accident. Ground and flight tests were conducted, and the radar was flown over a calibration site in a sequence of experiments designed to calibrate the system. The radar also took part in science campaigns.

Held, D. N.↗
NASA NTRS1995

Some observations on mesh refinement schemes applied to shock wave phenomena

This workshop's double-wedge test problem is taken from one of a sequence of experiments which were performed in order to classify the various canonical interactions between a planar shock wave and a double wedge. Therefore to build up a reasonably broad picture of the performance of our mesh refinement algorithm we have simulated three of these experiments and not just the workshop case. Here, using the results from these simulations together with their experimental counterparts, we make some general observations concerning the development of mesh refinement schemes for shock wave phenomena.

Quirk, James J.↗
NASA NTRS1999

Microgravity Experiments to Evaluate Electrostatic Forces in Controlling Cohesion and Adhesion of Granular Materials

The bulk behavior of dispersed, fluidized, or undispersed stationary granular systems cannot be fully understood in terms of adhesive/cohesive properties without understanding the role of electrostatic forces acting at the level of the grains themselves. When grains adhere to a surface, or come in contact with one another in a stationary bulk mass, it is difficult to measure the forces acting on the grains, and the forces themselves that induced the cohesion and adhesion are changed. Even if a single gain were to be scrutinized in the laboratory, it might be difficult, perhaps impossible, to define the distribution and character of surface charging and the three- dimensional relationship that charges (electrons, holes) have to one another. The hypothesis that we propose to test in microgravity (for dielectric materials) is that adhesion and cohesion of granular matter are mediated primarily by dipole forces that do not require the presence of a net charge; in fact, nominally electrically neutral materials should express adhesive and cohesive behavior when the neutrality results from a balance of positive and negative charge carriers. Moreover, the use of net charge alone as a measure of the electrical nature of grain-to-grain relationships within a granular mass may be misleading. We believe that the dipole forces arise from the presence of randomly-distributed positive and negative fixed charge carriers on grains that give rise to a resultant dipole moment. These dipole forces have long-range attraction. Random charges are created whenever there is triboelectrical activity of a granular mass, that is, whenever the grains experience contact/separation sequences or friction. Electrostatic forces are generally under-estimated for their role in causing agglomeration of dispersed grains in particulate clouds, or their role in affecting the internal frictional relationships in packed granular masses. We believe that electrostatic, in particular dipole-mediated processes, are pervasive and probably affect, at some level, everything from astrophysical-scale granular systems such as interstellar nebulae, protoplanetary dust and debris disks, planetary-scale systems such as debris palls from meteorite impact, volcanic eruptions, and aeolian dust storms, all the way to industrial-scale systems in mining, powder and grain processing, pharmaceuticals, and smoke-stack technologies. NASA must concern itself with the electrostatic behavior of dust and sand on Mars because of its potentially critical importance to human exploration. The motion and adhesion of martian surface materials will affect the design and performance of spacesuits, habitats, processing plants, solar panels, and any externally exposed equipment such as surface rovers or communication and weather stations. Additionally, the adhesion of dust and sand could greatly enhance contact with the potentially toxic components of the martian soil.

Marshall, J.↗
NASA NTRS1999

Microgravity Experiments to Evaluate Electrostatic Forces in Controlling Cohesion and Adhesion of Granular Materials

The bulk behavior of dispersed, fluidized, or undispersed stationary granular systems cannot be fully understood in terms of adhesive/cohesive properties without understanding the role of electrostatic forces acting at the level of the grains themselves. When grains adhere to a surface, or come in contact with one another in a stationary bulk mass, it is difficult to measure the forces acting on the grains, and the forces themselves that induced the cohesion and adhesion are changed. Even if a single grain were to be scrutinized in the laboratory, it might be difficult, perhaps impossible, to define the distribution and character of surface charging and the three-dimensional relationship that charges (electrons, holes) have to one another. The hypothesis that we propose to test in microgravity (for dielectric materials) is that adhesion and cohesion of granular matter are mediated primarily by dipole forces that do not require the presence of a net charge; in fact, nominally electrically neutral materials should express adhesive and cohesive behavior when the neutrality results from a balance of positive and negative charge carriers. Moreover, the use of net charge alone as a measure of the electrical nature of grain-to-grain relationships within a granular mass may be misleading. We believe that the dipole forces arise from the presence of randomly-distributed positive and negative fixed charge carriers on grains that give rise to a resultant dipole moment. These dipole forces have long-range attraction. Random charges are created whenever there is triboelectrical activity of a granular mass, that is, whenever the grains experience contact/separation sequences or friction.

Marshall, J.↗
NASA NTRS2001

Expression of the Acyl-Coenzyme A: Cholesterol Acyltransferase GFP Fusion Protein in Sf21 Insect Cells

The enzyme acyl-coenzyme A:cholesterol acyltransferase (ACAT) is an important contributor to the pathological expression of plaque leading to artherosclerosis n a major health problem. Adequate knowledge of the structure of this protein will enable pharmaceutical companies to design drugs specific to the enzyme. ACAT is a membrane protein located in the endoplasmic reticulum.t The protein has never been purified to homogeneity.T.Y. Chang's laboratory at Dartmouth College provided a 4-kb cDNA clone (K1) coding for a structural gene of the protein. We have modified the gene sequence and inserted the cDNA into the BioGreen His Baculovirus transfer vector. This was successfully expressed in Sf2l insect cells as a GFP-labeled ACAT protein. The advantage to this ACAT-GFP fusion protein (abbreviated GCAT) is that one can easily monitor its expression as a function of GFP excitation at 395 nm and emission at 509 nm. Moreover, the fusion protein GCAT can be detected on Western blots with the use of commercially available GFP antibodies. Antibodies against ACAT are not readily available. The presence of the 6xHis tag in the transfer vector facilitates purification of the recombinant protein since 6xHis fusion proteins bind with high affinity to Ni-NTA agarose. Obtaining highly pure protein in large quantities is essential for subsequent crystallization. The purified GCAT fusion protein can readily be cleaved into distinct GFP and ACAT proteins in the presence of thrombin. Thrombin digests the 6xHis tag linking the two protein sequences. Preliminary experiments have indicated that both GCAT and ACAT are expressed as functional proteins. The ultimate aim is to obtain large quantities of the ACAT protein in pure and functional form appropriate for protein crystal growth. Determining protein structure is the key to the design and development of effective drugs. X-ray analysis requires large homogeneous crystals that are difficult to obtain in the gravity environment of earth. Protein crystals grown in microgravity are often larger and have fewer defects than those grown on earth. The analysis of higher quality space-grown crystals will assist in structure-based drug design. We have successfully grown GCAT-infected Sf21 cells in both adhesion and suspension cultures. Expression levels of GCAT in cell lines such as Sf9 and High Five appear to be reduced. We intend to replicate GCAT expression in all three cell lines using the NASA rotating wall bioreactor which effectively duplicates a microgravity environment. The bioreactor itself could be launched to study the expression of the GFP and GCAT proteins in the actual microgravity environment achieved in orbit.

Mahtani, H. K.↗
NASA NTRS2001

Quantum Mechanical Modeling of Ballistic MOSFETs

The objective of this project was to develop theory, approximations, and computer code to model quasi 1D structures such as nanotubes, DNA, and MOSFETs: (1) Nanotubes: Influence of defects on ballistic transport, electro-mechanical properties, and metal-nanotube coupling; (2) DNA: Model electron transfer (biochemistry) and transport experiments, and sequence dependence of conductance; and (3) MOSFETs: 2D doping profiles, polysilicon depletion, source to drain and gate tunneling, understand ballistic limit.

Svizhenko, Alexei↗