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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Dinuclear Gold(I) Complexes Bearing Alkyl-Bridged Bis(N-heterocyclic carbene) Ligands as Catalysts for Carboxylative Cyclization of Propargylamine: Synthesis, Structure, and Kinetic and Mechanistic Comparison to the Mononuclear Complex [Au(IPr)Cl]

Eight new dinuclear gold(I) complexes, [Au 2 (L)X 2 ] (1–8), were synthesized using a straightforward synthetic procedure under very mild conditions. The complexes have been characterized by NMR spectroscopy, elemental analysis, and single-crystal X-ray structure analysis. Their catalytic activity was investigated in the carboxylative cyclization of propargylamine (PPA). A superior performance in comparison to [Au(IPr)Cl] (9) was obtained for complexes 1 and 2 having an eight-methylene bridge connecting two NHCs with an arene bearing an isopropyl substituent for X = Cl, Br. This prompted more detailed kinetic and mechanistic studies by FTIR comparing dinuclear complex 2 of X = Cl to complex 9. Fortuitously the FTIR studies allowed monitoring of the formation of the products carbamic acid (CA) and carbamate salt (CS), as well as a key cyclized intermediate first discovered by Ikariya. These data allow additional insight into the mechanism as well as the central role which may be played by Au(I) carbamate formation as a higher energy resting state present in the catalytic cycle. In conclusion, the crystal structures of four of the new complexes and a detailed computational study relevant to the role of carbamic acid (CA) and carbamates in the catalytic cycle are also reported.

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

A review on recent machine learning applications for imaging mass spectrometry studies

Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. Here, in this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Genome annotation provides insight into carbon monoxide and hydrogen metabolism in Rubrivivax gelatinosus

In this article, we report here the sequencing and analysis of the genome of the purple non-sulfur photosynthetic bacterium Rubrivivax gelatinosus CBS. This microbe is a model for studies of its carboxydotrophic life style under anaerobic condition, based on its ability to utilize carbon monoxide (CO) as the sole carbon substrate and water as the electron acceptor, yielding CO 2 and H 2 as the end products. The CO-oxidation reaction is known to be catalyzed by two enzyme complexes, the CO dehydrogenase and hydrogenase. As expected, analysis of the genome of Rx. gelatinosus CBS reveals the presence of genes encoding both enzyme complexes. The CO-oxidation reaction is CO-inducible, which is consistent with the presence of two putative CO-sensing transcription factors in its genome. Genome analysis also reveals the presence of two additional hydrogenases, an uptake hydrogenase that liberates the electrons in H 2 in support of cell growth, and a regulatory hydrogenase that senses H 2 and relays the signal to a two-component system that ultimately controls synthesis of the uptake hydrogenase. The genome also contains two sets of hydrogenase maturation genes which are known to assemble the catalytic metallocluster of the hydrogenase NiFe active site. Finally and collectively, the genome sequence and analysis information reveals the blueprint of an intricate network of signal transduction pathways and its underlying regulation that enables Rx. gelatinosus CBS to thrive on CO or H 2 in support of cell growth.

08 HYDROGEN↗
DOE OSTI2025

Multiple oxidation states of uranium stabilized by an O , N , O -ligand

Elements of the 5f row maintain a wide variety of oxidation states that have been exploited in synthesis, catalysis, and separations. Herein, we describe the complexation of an O,N,O chelator, ExPh, with uranium in the (IV) and (VI) (uranyl) oxidation states. These two uranium complexes, U(IV)ExPh and UO 2 ExPh, respectively, were characterized in the solid state via single-crystal X-ray diffraction (SC-XRD) analysis. Electrochemical studies using cyclic voltammetry were employed to investigate the multiple redox events associated with the U(IV) complex. Spectro-electrochemical analysis of U(IV)ExPh provided spectroscopic evidence of a stable U(V) species. Chemical oxidation of U(IV)ExPh allowed isolation of the U(V) complex, U(V)ExPh. All three stable uranium complexes produced in this study were characterized via IR, UV-vis and NMR spectroscopies, and micro-spectrophotometry. On the other hand, efforts to reduce U(IV)ExPh to the corresponding U(III) species or produce this putative complex directly from ExPh failed to yield an isolable product. The stabilization of three formal oxidation states of uranium, coupled with previous lanthanide-row results, paves the way for studies of ExPh and its analogues in minor actinide chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Structure of the human histone chaperone FACT Spt16 N-terminal domain

The Spt16–SSRP1 heterodimer is a histone chaperone that plays an important role in regulating chromatin assembly. Here, a crystal structure of the N-terminal domain of human Spt16 is presented and it is shown that this domain may contribute to histone binding. The histone chaperone FACT plays an important role in facilitating nucleosome assembly and disassembly during transcription. FACT is a heterodimeric complex consisting of Spt16 and SSRP1. The N-terminal domain of Spt16 resembles an inactive aminopeptidase. How this domain contributes to the histone chaperone activity of FACT remains elusive. Here, the crystal structure of the N-terminal domain (NTD) of human Spt16 is reported at a resolution of 1.84 Å. The structure adopts an aminopeptidase-like fold similar to those of the Saccharomyces cerevisiae and Schizosaccharomyces pombe Spt16 NTDs. Isothermal titration calorimetry analyses show that human Spt16 NTD binds histones H3/H4 with low-micromolar affinity, suggesting that Spt16 NTD may contribute to histone binding in the FACT complex. Surface-residue conservation and electrostatic analysis reveal a conserved acidic patch that may be involved in histone binding.

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

Symbolic pregression: Discovering physical laws from distorted video

In this work, we present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled synthetic video (or, more generally, for discovering and modeling predictable features in time-series data). We first train an autoencoder that maps each video frame into a low-dimensional latent space where the laws of motion are as simple as possible, by minimizing a combination of nonlinearity, acceleration, and prediction error. Differential equations describing the motion are then discovered using Pareto-optimal symbolic regression. We find that our pre-regression (“pregression”) step is able to rediscover Cartesian coordinates of unlabeled moving objects even when the video is distorted by a generalized lens. Using intuition from multidimensional knot theory, we find that the pregression step is facilitated by first adding extra latent space dimensions to avoid topological problems during training and then removing these extra dimensions via principal component analysis. An inertial frame is autodiscovered by minimizing the combined equation complexity for multiple experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Complex quantum transport in a modulation doped strained Ge quantum well heterostructure with a high mobility 2D hole gas

The complex quantum transport of a strained Ge quantum well (QW) modulation doped heterostructure with two types of mobile carriers has been observed. The two dimensional hole gas (2DHG) in the Ge QW exhibits an exceptionally high mobility of 780 000 cm{sup 2}/Vs at temperatures below 10 K. Through analysis of Shubnikov de-Haas oscillations in the magnetoresistance of this 2DHG below 2 K, the hole effective mass is found to be 0.065 m{sub 0}. Anomalous conductance peaks are observed at higher fields which deviate from standard Shubnikov de-Haas and quantum Hall effect behaviour due to conduction via multiple carrier types. Despite this complex behaviour, analysis using a transport model with two conductive channels explains this behaviour and allows key physical parameters such as the carrier effective mass, transport, and quantum lifetimes and conductivity of the electrically active layers to be extracted. This finding is important for electronic device applications, since inclusion of highly doped interlayers which are electrically active, for enhancement of, for example, room temperature carrier mobility, does not prevent analysis of quantum transport in a QW.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

A review of advantages of high-efficiency X-ray spectrum imaging for analysis of nanostructured ferritic alloys

Nanostructured ferritic alloys (NFAs) exhibit complex microstructures consisting of 100-500 nm ferrite grains, grain boundary solute enrichment, and multiple populations of precipitates and nanoclusters (NCs). Understanding these materials' excellent creep and radiation-tolerance properties requires a combination of multiple atomic-scale experimental techniques. Recent advances in scanning transmission electron microscopy (STEM) hardware and data analysis methods have the potential to revolutionize nanometer to micrometer scale materials analysis. The application of these methods is applied to NFAs as a test case and is compared to both conventional STEM methods as well as complementary methods such as scanning electron microscopy and atom probe tomography. In this paper, we review past results and present new results illustrating the effectiveness of latest-generation STEM instrumentation and data analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Binary Complex Neural Network Acceleration on FPGA

Being able to learn from complex data with phase information is imperative for many signal processing applications. Today’s real-valued deep neural networks (DNNs) have shown efficiency in latent information analysis but fall short when applied to the complex domain. Deep complex networks (DCN) , in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision making requirements of many deployable systems dealing with short observations or short signal bursts. Recent, Binarized Complex Neural Network (BCNN), which integrates DCNs with binarized neural networks (BNN), shows great potential in classifying complex data in real-time. In this paper, we propose a structural pruning based accelerator of BCNN, which is able to provide more than 5000 frames/s inference throughput on edge devices. The high performance comes from both the algorithm and hardware sides. On the algorithm side, we conduct structural pruning to the original BCNN models and obtain 20 × pruning rates with negligible accuracy loss; on the hardware side, we propose a novel 2D convolution operation accelerator for the binary complex neural network. Experimental results show that the proposed design works with over 90% utilization and is able to achieve the inference throughput of 5882 frames/s and 4938 frames/s for complex NIN-Net and ResNet-18 using CIFAR-10 dataset and Alveo U280 Board.

Peng, Hongwu↗
DOE OSTI2017

Structure of the active form of human origin recognition complex and its ATPase motor module

Binding of the Origin Recognition Complex (ORC) to origins of replication marks the first step in the initiation of replication of the genome in all eukaryotic cells. Here, we report the structure of the active form of human ORC determined by X-ray crystallography and cryo-electron microscopy. The complex is composed of an ORC1/4/5 motor module lobe in an organization reminiscent of the DNA polymerase clamp loader complexes. A second lobe contains the ORC2/3 subunits. The complex is organized as a double-layered shallow corkscrew, with the AAA+ and AAA+-like domains forming one layer, and the winged-helix domains (WHDs) forming a top layer. CDC6 fits easily between ORC1 and ORC2, completing the ring and the DNA-binding channel, forming an additional ATP hydrolysis site. Analysis of the ATPase activity of the complex provides a basis for understanding ORC activity as well as molecular defects observed in Meier-Gorlin Syndrome mutations.

Tocilj, Ante↗
DOE OSTI2016

Preparatory Verification of a New Monte Carlo Code cosRMC based on VERA Core Physics Benchmark

To promote nuclear power plant system design and simulation software self-reliance development, DOE of China approved a R and D key project to develop an integrated engine for pressurized water reactor core and system design and analysis, referred as COSINE hereafter. The scope of this code package includes reactor physics, thermal hydraulics, safety analysis, severe accidents, probability safety assessment and etc. Many universities, research institutes and nuclear related corporations in China are involved in the code development, among which the State Nuclear Power Software Development Center (SNPSDC) plays the leading role. With the requirement of accurate three-dimensional modeling of the advanced, special or new complex core in reactor neutronics and shielding analysis, Monte Carlo method is becoming a more powerful tool for reactor analysis. Thus, a new Monte Carlo transport code named cosRMC (abbreviation for COSINE Reactor Monte Carlo) has been developed jointly by SNPSDC and Department of Engineering Physics, Tsinghua University, the main developer of RMC code. The cosRMC now has many functions as criticality calculation, fixed-source calculation, burnup calculation, and etc. Some techniques for geometry treatment, new burnup algorithm, source convergence acceleration, massive tally and parallel calculation have been implemented in cosRMC to improve the efficiency. In order to ensure its reliability, several types of V and V work have been and are being carried out for cosRMC. In this paper, some preparatory verification results based on VERA Core Physics Benchmark are presented. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

PATHA: Performance Analysis Tool for HPC Applications

Large science projects rely on complex workflows to analyze terabytes or petabytes of data. These jobs are often running over thousands of CPU cores and simultaneously performing data accesses, data movements, and computation. It is difficult to identify bottlenecks or to debug the performance issues in these large workflows. In order to address these challenges, we have developed Performance Analysis Tool for HPC Applications (PATHA) using the state-of-art open source big data processing tools. Our framework can ingest system logs to extract key performance measures, and apply the most sophisticated statistical tools and data mining methods on the performance data. Furthermore, it utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of PATHA, we conduct a case study on the workflows from an astronomy project known as the Palomar Transient Factory (PTF). This study processed 1.6 TB of system logs collected on the NERSC supercomputer Edison. Using PATHA, we were able to identify performance bottlenecks, which reside in three tasks of PTF workflow with the dependency on the density of celestial objects.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2014

Analysis of substrate specificity of Schizosaccharomyces pombe Mag1 alkylpurine DNA glycosylase

DNA glycosylases specialized for the repair of alkylation damage must identify, with fine specificity, a diverse array of subtle modifications within DNA. The current mechanism involves damage sensing through interrogation of the DNA duplex, followed by more specific recognition of the target base inside the active site pocket. To better understand the physical basis for alkylpurine detection, we determined the crystal structure of Schizosaccharomyces pombe Mag1 (spMag1) in complex with DNA and performed a mutational analysis of spMag1 and the close homologue from Saccharomyces cerevisiae (scMag). Despite strong homology, spMag1 and scMag differ in substrate specificity and cellular alkylation sensitivity, although the enzymological basis for their functional differences is unknown. We show that Mag preference for 1,N{sup 6}-ethenoadenine ({var_epsilon}A) is influenced by a minor groove-interrogating residue more than the composition of the nucleobase-binding pocket. Exchanging this residue between Mag proteins swapped their {var_epsilon}A activities, providing evidence that residues outside the extrahelical base-binding pocket have a role in identification of a particular modification in addition to sensing damage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

WE-G-BRA-06: Application of Systems and Control Theory-Based Hazard Analysis to Radiotherapy

Purpose: The process of delivering radiation occurs in a complex socio-technical system heavily reliant on human operators. Furthermore, both humans and software are notoriously challenging to account for in traditional hazard analysis models. High reliability industries such as aviation have approached this problem through using hazard analysis techniques grounded in systems and control theory. The purpose of this work is to apply the Systems Theoretic Accident Model Processes (STAMP) hazard model to radiotherapy. In particular, the System-Theoretic Process Analysis (STPA) approach is used to perform a hazard analysis of a proposed on-line adaptive cranial radiosurgery procedure that omits the CT Simulation step and uses only CBCT for planning, localization, and treatment. Methods: The STPA procedure first requires the definition of high-level accidents and hazards leading to those accidents. From there, hierarchical control structures were created followed by the identification and description of control actions for each control structure. Utilizing these control structures, unsafe states of each control action were created. Scenarios contributing to unsafe control action states were then identified and translated into system requirements to constrain process behavior within safe boundaries. Results: Ten control structures were created for this new CBCT-only process which covered the areas of hospital and department management, treatment design and delivery, and vendor service. Twenty three control actions were identified that contributed to over 80 unsafe states of those control actions resulting in over 220 failure scenarios. Conclusion: The interaction of people, hardware, and software are highlighted through the STPA approach. STPA provides a hierarchical model for understanding the role of management decisions in impacting system safety so that a process design requirement can be traced back to the hazard and accident that it is intended to mitigate. Varian Medical Systems, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2011

New Developments In Particle Image Velocimetry (PIV) For The Study Of Complex Plasmas

Particle Image Velocimetry (PIV) is a fluid measurement technique in which the average displacement of small groups of particles is made by comparing a pair of images that are separated in time by an interval {Delta}t. For over a decade, a several variations of the PIV technique, e.g., two-dimensional, stereoscopic, and tomographic PIV, have been used to characterize particle transport, instabilities, and the thermal properties of complex plasmas. This paper describes the basic principles involved in the PIV analysis technique and discusses potential future applications of PIV to the study of complex plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Divergent predictions of carbon storage between two global land models: Attribution of the causes through traceability analysis

Representations of the terrestrial carbon cycle in land models are becoming increasingly complex. It is crucial to develop approaches for critical assessment of the complex model properties in order to understand key factors contributing to models' performance. In this study, we applied a traceability analysis which decomposes carbon cycle models into traceable components, for two global land models (CABLE and CLM-CASA') to diagnose the causes of their differences in simulating ecosystem carbon storage capacity. Driven with similar forcing data, CLM-CASA' predicted – 31 % larger carbon storage capacity than CABLE. Since ecosystem carbon storage capacity is a product of net primary productivity (NPP) and ecosystem residence time ( τ E ), the predicted difference in the storage capacity between the two models results from differences in either NPP or τ E or both. Our analysis showed that CLM-CASA'simulated 37 % higher NPP than CABLE. On the other hand, τ E , which was a function of the baseline carbon residence time ( τ ' E ) and environmental effect on carbon residence time, was on average 11 years longer in CABLE than CLM-CASA'. This difference in τ E was mainly caused by longer τ ' E of woody biomass (23 vs. 14 years in CLM-CASA'), and higher proportion of NPP allocated to woody biomass (23 vs. 16 %). Differences in environmental effects on carbon residence times had smaller influences on differences in ecosystem carbon storage capacities compared to differences in NPP and τ ' E . Altogether, the traceability analysis showed that the major causes of different carbon storage estimations were found to be parameters setting related to carbon input and baseline carbon residence times between two models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Efficient Reliability Analysis using Generalized Multifidelity Modeling and Explainable Active Learning

To assess the reliability of critical technologies like nuclear plants and infrastructure systems and improve the robustness of design, engineers have to quantify the uncertainties surrounding the system behavior accurately. However, the complexity of the problem can make standard reliability analysis algorithms prohibitively expensive, primarily due to the high computational cost of estimating the system response at each iteration. This cost can be greatly reduced by using multi-fidelity modeling and machine learning to build a surrogate model to replace the expensive response function. We propose a general and robust method for building surrogates from multiple Low Fidelity (LF) models coupled with machine learning to retain accuracy. Our framework first constructs “Corrected Low Fidelity models” (CLFs) by coupling a High Fidelity (HF) model inferred Gaussian Process correction term with each of the LF models. It then uses the correction terms to assign model probabilities to each of these CLFs in an explainable way before using them to assemble the final surrogate. No assumptions are made about the type of the LF models or their correlation with the HF model. The proposed surrogate modeling framework is used within the subset simulation algorithm (a variance-reduced MCMC-based reliability analysis algorithm) for enhanced efficiency. Additionally, an active learning step is added to the algorithm to adaptively decide when the surrogate is not sufficiently accurate, at which point the HF model is called and used to refine the surrogate. Through a frame buckling example, our method is shown to be highly efficient at reducing the expensive HF model calls while accurately estimating the failure probability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A comprehensive analysis of transient pressure and rate data from CO 2 storage projects in a depleted pinnacle reef oil field complex, Michigan, USA

Pressure and rate data are commonly recorded as part of a basic monitoring program in CCS projects. This paper discusses the application of multiple analytical techniques to interpret pressure and rate transient data from CO 2 injection and storage operations. The techniques of interest, i.e., injection-falloff analysis, injectivity/productivity index analysis and pressure pulse arrival time analysis, are commonly used in the oil and gas industry to assess reservoir properties, but not well known in the CCS literature (especially the last two). Injection-falloff analysis involves log-log pressure derivative plotting for the falloff data and history-matching of the entire injection-falloff sequence to determine permeability. In the injectivity/productivity index analysis, rate-normalized pressure buildup is plotted against material balance time or ratio of cumulative injection to injection rate to determine the injectivity index (ratio of injection rate to stabilized pressure buildup) which can be related to the permeability-thickness product. The arrival time analysis identifies the arrival of a pressure disturbance (~0.1 psi change from ambient) to determine the hydraulic diffusivity from which permeability can be estimated. The applicability of these techniques is demonstrated via illustrative examples from multiple wells in different pinnacle carbonate reefs undergoing CO 2 -EOR in Northern Michigan. The paper ends with a discussion of the relative merits of each interpretive technique, as well as recommendations that could be useful for other field projects.

42 ENGINEERING↗