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At least 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Nuclear Forensics Scanning

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46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Particle Size Analysis and Distribution

Scanning Electron Microscope (SEM) is a instrument used for examining samples at magnifications from 32 X to 500,000 X (for our instrument). The SEM is attached with an electron gun and is directed onto the sample. The sample then produces an image where it is read on the monitor. The electron gun is positioned at the top because it needs to shoot at the sample that is being tested. The electrons are then shot in a downward motion traveling through several lenses, such as the condenser lens for a more precise reading. Once the samples have been read by the instrument, it will show a picture that has a certain number of particles. Particle size distribution have many uses that are essential to life habits (health) or even conventional use for example construction. Here are just a few examples of how particle size analysis and distribution are being used even today. This Standard Reference Material (SRM) is primarily intended for use in the calibration of equipment used to measure particle size distributions (PSD) in the 9 μm to 30 μm range. A unit of SRM 1984 consists of a single bottle containing approximately 14 g of tungsten carbide/cobalt powder. Certified values: The PSD values at five mass percentiles were measured by scanning electron microscopy (SEM) and laser light scattering (LLS). The NIST certified PSD values by SEM are listed. These certified values were determined by the measurement of over 10 000 individual particles from three bottles. Comparing the certified NIST SRM 1984 tungsten carbide/cobalt powder with our results are almost identical. This is great because more accurate work can be done and proves that our laboratory is able to reproduce NIST results. In conclusion, the scanning electron microscope is used to capture the sampled element and display it to be counted. Without the use of particle distribution, it would be a difficult time to get necessities such as medicine, asthma pumps, constructing materials, or as simple as ink. Overall, our study is an experimental comparison of the techniques we have in house, which are LLS and SEM (combined with the NIH ImageJ software) using the NIST SRM-1984 as a common, well characterized sample so that we have an idea of how the results from the techniques compare.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

All-Atom Simulation of 3D Hot Spot Formation in Shocked TATB Explosive

TATB is an insensitive high explosive (IHE) critical to the stockpile that is challenging to model at the continuum scale. Advanced detonation models in the Cheetah high explosive chemistry code require validation though subscale simulations. High explosive initiation is determined by micron-scale physics of hot spots formed a shock-collapsed pores. Pore sizes between 100 nm and 1 μm are believed to be the most important for determining the shock sensitivity of TATB. This range of pore sizes is difficult to access at the atomic scale through allatom molecular dynamics (MD) simulations, even with Sierra-class computers. Quasi-2D simulations are widely used and allow much larger pore sizes (up to 400 nm) to be studied, but the applicability of 2D simulations to the actual 3D pore response is not understood. Resolving these uncertainties through “full physics” MD modeling is key for generalizing, parameterizing, and validating the kinds of continuum models used to inform design, safety, and performance. This work was a continuation of FY20 efforts pushing simulations to full 3D with the largest-ever all-atom simulations of an explosive. These were the first all-atom full-3D simulations of large hot spots thought to govern explosive detonation and required over a billion atoms. Simulations were performed using LAMMPS, an open SNL science code. MD explosive models present unique challenges, even for established codes such as LAMMPS. Their model forms are more complex than typical models for metals, while simulating high temperature-pressure conditions is demanding and increases computational cost. Scaling problems in GPU-enabled MD algorithms initially limited simulations to <100 million atoms but were resolved through collaboration with SNL. An overall 24x speedup was obtained relative to CPU machines. Specialized analysis of these simulations required a bottom-up refactoring and algorithm parallelization of in-house codes and application of computer vision algorithms to extract meaningful information.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Two-dimensional electronic Stark spectroscopy of a photosynthetic dimer

Stark spectroscopy, which measures changes in the linear absorption of a sample in the presence of an external DC electric field, is a powerful experimental tool for probing the existence of charge-transfer (CT) states in photosynthetic systems. CT states often have small transition dipole moments, making them insensitive to other spectroscopic methods, but are particularly sensitive to Stark spectroscopy due to their large permanent dipole moment. In a previous study, we demonstrated a new experimental method, two- dimensional electronic Stark spectroscopy (2DESS), which combines two-dimensional electronic spectroscopy (2DES) and Stark spec- troscopy. In order to understand how the presence of CT states manifest in 2DESS, here, we perform computational modeling and calculations of 2DESS as well as 2DES and Stark spectra, studying a photosynthetic dimer inspired by the photosystem II reaction center. Here, we identify specific cases where qualitatively different sets of system parameters produce similar Stark and 2DES spectra but significantly different 2DESS spectra, showing the potential for 2DESS to aid in identifying CT states and their coupling to excitonic states.

14 SOLAR ENERGY↗
DOE OSTI2022

Crowding-Induced Spatial Organization of Gene Expression in Cell-Sized Vesicles

Cell-free protein synthesis is an important tool for studying gene expression and harnessing it for applications. In cells, gene expression is regulated in part by the spatial organization of transcription and translation. Unfortunately, current cell-free approaches are unable to control the organization of molecular components needed for gene expression, which limits the ability to probe and utilize its effects. In this work, we show, using complementary computational and experimental approaches, that macromolecular crowding can be used to control the spatial organization and translational efficiency of gene expression in cell-sized vesicles. Computer simulations and imaging experiments reveal that, as crowding is increased, DNA plasmids become localized at the inner surface of vesicles. Ribosomes, in contrast, remain uniformly distributed, demonstrating that crowding can be used to differentially organize components of gene expression. We further carried out cell-free protein synthesis reactions in cell-sized vesicles and quantified mRNA and protein abundance. At sufficiently high levels of crowding, we observed localization of mRNA near vesicle surfaces, a decrease in translational efficiency and protein abundance, and anomalous scaling of protein abundance as a function of vesicle size. These results are consistent with high levels of crowding causing altered spatial organization and slower diffusion. Our work demonstrates a straightforward way to control the organization of gene expression in cell-sized vesicles and provides insight into the spatial regulation of gene expression in cells.

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

The Dark Energy Survey Supernova Program: an updated measurement of the Hubble constant using the inverse distance ladder

We measure the current expansion rate of the Universe, Hubble’s constant $H_0$, by calibrating the absolute magnitudes of supernovae to distances measured by baryon acoustic oscillations (BAO). This ‘inverse distance ladder’ technique provides an alternative to calibrating supernovae using nearby absolute distance measurements, replacing the calibration with a high-redshift anchor. We use the recent release of 1829 supernovae from the Dark Energy Survey spanning $0.01\lt z\lt 1.13$ anchored to the recent baryon acoustic oscillation measurements from Dark Energy Spectroscopic Instrument (DESI) spanning $0.30 \lt z_{\mathrm{eff}}\lt 2.33$. To trace cosmology to $z=0$, we use the third-, fourth-, and fifth-order cosmographic models, which, by design, are agnostic about the energy content and expansion history of the universe. With the inclusion of the higher redshift DESI-BAO data, the third-order model is a poor fit to both data sets, with the fourth-order model being preferred by the Akaike Information Criterion. Using the fourth-order cosmographic model, we find $H_0=67.19^{+0.66}_{-0.64}\mathrm{~km} \mathrm{~s}^{-1} \mathrm{~Mpc}^{-1}$, in agreement with the value found by Planck without the need to assume Flat-$\Lambda$CDM. However, the best-fitting expansion history differs from that of Planck, providing continued motivation to investigate these tensions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Uncovering I/O demands on HPC platforms: Peeking under the hood of Santos Dumont

High-Performance Computing (HPC) platforms are required to solve the most diverse large-scale scientific problems in various research areas, such as biology, chemistry, physics, and health sciences. Researchers use a multitude of scientific softwares, which have different requirements. These include input and output operations, which directly impact performance due to the existing difference in processing and data access speeds. Thus, supercomputers must efficiently handle mixed workload when storing data from the applications. Understanding the set of applications and their performance running in a supercomputer is paramount to understanding the storage system's usage, pinpointing possible bottlenecks, and guiding optimization techniques. This research proposes a methodology and visualization tool to evaluate a supercomputer's data storage infrastructure's performance, taking into account the diverse workload and demands of the system over a long period of operation. As a study case, we focus on the Santos Dumont supercomputer, identifying inefficient usage, problematic performance factors, and providing guidelines on how to tackle those issues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Route to Design Novel Functional Peptides by Applying a Denoising Diffusional Model to mRNA Display Libraries

In vitro directed evolution techniques, such as mRNA display, enable peptide ligand discovery and optimization. However, physical libraries that rely on a genetic code can only search a small fraction of sequence space due to inherent biases in the genetic code and experimental limitations. To address this challenge, denoising diffusion implicit models (DDIMs) are applied to generate novel peptide ligands against B‐cell lymphoma extra‐large (Bcl‐x L ), a key cancer target. Starting with high‐throughput sequencing data from previous selections, a DDIM is trained to produce novel sequences with high affinity binding. Experimental validation confirms that most generated sequences are functionally equivalent to the original library members for Bcl‐x L binding and demonstrated comparable binding kinetics and affinity relative to the wildtype and nearest original neighbors. Importantly, this approach generated rare sequences not easily accessible via mutation and directed evolution. These results indicate that DDIMs can complement and expand directed evolution data, efficiently exploring underrepresented regions of sequence space. This approach provides a broadly applicable framework for accelerating ligand discovery and optimizing molecular properties across diverse targets.

Qi, Pearl [Mork Family Department of Chemical Engi↗
DOE OSTI2020

Electric field stimulates production of highly conductive microbial OmcZ nanowires

Multifunctional living materials are attractive due to their powerful ability to self-repair and replicate. However, most natural materials lack electronic functionality. Here we show that an electric field, applied to electricity-producing Geobacter sulfurreducens biofilms, stimulates production of cytochrome OmcZ nanowires with 1,000-fold higher conductivity (30 S cm -1 ) and threefold higher stiffness (1.5 GPa) than the cytochrome OmcS nanowires that are important in natural environments. Using chemical imaging-based multimodal nanospectroscopy, we correlate protein structure with function and observe pH-induced conformational switching to β-sheets in individual nanowires, which increases their stiffness and conductivity by 100-fold due to enhanced π-stacking of heme groups; this was further confirmed by computational modeling and bulk spectroscopic studies. These nanowires can transduce mechanical and chemical stimuli into electrical signals to perform sensing, synthesis and energy production. Overall, these findings of biologically produced, highly conductive protein nanowires may help to guide the development of seamless, bidirectional interfaces between biological and electronic systems.

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

Variations of characteristic time scales in rotating stratified turbulence using a large parametric numerical study

In this work, we study rotating stratified turbulence (RST) making use of numerical data stemming from a large parametric study varying the Reynolds, Froude and Rossby numbers, Re, Fr and Ro in a broad range of values. The computations are performed using periodic boundary conditions on grids of 10243 points, with no modeling of the small scales, no forcing and with large-scale random initial conditions for the velocity field only, and there are altogether 65 runs analyzed in this paper. The buoyancy Reynolds number defined as R B = ReFr 2 varies from negligible values to ≈ 105, approaching atmospheric or oceanic regimes. This preliminary analysis deals with the variation of characteristic time scales of RST with dimensionless parameters, focusing on the role played by the partition of energy between the kinetic and potential modes, as a key ingredient for modeling the dynamics of such flows. We find that neither rotation nor the ratio of the Brunt-Väisälä frequency to the inertial frequency seem to play a major role in the absence of forcing in the global dynamics of the small-scale kinetic and potential modes. Specifically, in these computations, mostly in regimes of wave turbulence, characteristic times based on the ratio of energy to dissipation of the velocity and temperature fluctuations, T V and T P , vary substantially with parameters. Their ratio γ=T V /T P follows roughly a bell-shaped curve in terms of Richardson number Ri. It reaches a plateau --on which time scales become comparable, γ≈0.6 -- when the turbulence has significantly strengthened, leading to numerous destabilization events together with a tendency towards an isotropization of the flow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Elucidating and predicting the dynamic evolution of water and land systems due to natural and energy-related forcings

Focal Area(s): 3. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI; & 1. Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Science Challenge: Interactions between water, land, and energy systems are complex and occur on a variety of scales, ranging from local to basinal to regional. Accurately predicting the behavior of ground water and surface water systems for 5-10 years and beyond requires an understanding of the current system and the ability to model both the natural system at scale and human-induced forcings related to energy and other activities. Artificial intelligence and machine learning (AI/ML) combined with modern compilation and integration efforts for U.S. groundwater and surface water systems present potential solutions to bolstering detailed physics-based models of these systems. Big data tied with ML and physics-based modeling can drive breakthroughs in understanding the earth system, but research is often impeded by data access (e.g., privacy issues), quality, formats, gaps, multi-source, multi-scale, integration, and spatiotemporal challenges. Effective integration of real data and simulated (synthetic) data that fill gaps is critical. Overcoming these complex data and model integration challenges will enable a transformational approach to acquiring enhanced understanding of environmental systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Identifying Opportunities at the Interface of Chemistry and Quantum Information Science (Final Technical Report)

This project convened a National Academies committee to identify opportunities and research priorities at the interface of chemistry and quantum information science (QIS). The work culminated in a consensus study report that (1) articulates three fundamental research areas to advance QIS (design and synthesis of molecular qubits; measurement and control of molecular quantum systems; and experimental and computational scaling of qubit design and function), and (2) underscores the importance of cross-disciplinary collaboration, access to facilities and instrumentation, FAIR-aligned data infrastructure, and workforce development initiatives to sustain U.S. leadership in QIS. The report and all other material associated with this project can be downloaded on the project webpage: https://www.nationalacademies.org/projects/DELS-BCST-21-01 .

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

Circuit complexity and functionality: A statistical thermodynamics perspective

Circuit complexity, defined as the minimum circuit size required for implementing a particular Boolean computation, is a foundational concept in computer science. Determining circuit complexity is believed to be a hard computational problem. Recently, in the context of black holes, circuit complexity has been promoted to a physical property, wherein the growth of complexity is reflected in the time evolution of the Einstein-Rosen bridge (“wormhole”) connecting the two sides of an anti-de Sitter “eternal” black hole. Here, we are motivated by an independent set of considerations and explore links between complexity and thermodynamics for functionally equivalent circuits, making the physics-inspired approach relevant to real computational problems, for which functionality is the key element of interest. In particular, our thermodynamic framework provides an alternative perspective on the obfuscation of programs of arbitrary length—an important problem in cryptography—as thermalization through recursive mixing of neighboring sections of a circuit, which can be viewed as the mixing of two containers with “gases of gates.” This recursive process equilibrates the average complexity and leads to the saturation of the circuit entropy, while preserving functionality of the overall circuit. The thermodynamic arguments hinge on ergodicity in the space of circuits which we conjecture is limited to disconnected ergodic sectors due to fragmentation. The notion of fragmentation has important implications for the problem of circuit obfuscation as it implies that there are circuits of same size and functionality that cannot be connected via a polynomial number of local moves. Furthermore, we argue that fragmentation is unavoidable unless the complexity classes NP and coNP coincide, a statement that implies the collapse of the polynomial hierarchy of computational complexity theory to its first level.

Science & Technology - Other Topics↗
DOE OSTI2012

Computational Investigation of Glycosylation Effects on a Family 1 Carbohydrate-Binding Module

Carbohydrate-binding modules (CBMs) are ubiquitous components of glycoside hydrolases, which degrade polysaccharides in nature. CBMs target specific polysaccharides, and CBM binding affinity to cellulose is known to be proportional to cellulase activity, such that increasing binding affinity is an important component of performance improvement. To ascertain the impact of protein and glycan engineering on CBM binding, we use molecular simulation to quantify cellulose binding of a natively glycosylated Family 1 CBM. To validate our approach, we first examine aromatic-carbohydrate interactions on binding, and our predictions are consistent with previous experiments, showing that a tyrosine to tryptophan mutation yields a 2-fold improvement in binding affinity. We then demonstrate that enhanced binding of 3-6-fold over a nonglycosylated CBM is achieved by the addition of a single, native mannose or a mannose dimer, respectively, which has not been considered previously. Furthermore, we show that the addition of a single, artificial glycan on the anterior of the CBM, with the native, posterior glycans also present, can have a dramatic impact on binding affinity in our model, increasing it up to 140-fold relative to the nonglycosylated CBM. These results suggest new directions in protein engineering, in that modifying glycosylation patterns via heterologous expression, manipulation of culture conditions, or introduction of artificial glycosylation sites, can alter CBM binding affinity to carbohydrates and may thus be a general strategy to enhance cellulase performance. Our results also suggest that CBM binding studies should consider the effects of glycosylation on binding and function.

09 BIOMASS FUELS↗