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At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Quantitative examination of carbide and sulphide precipitates in chemically complex steels processed by direct strip casting

A high strength low alloy steel composition has been melted and processed by two different routes: simulated direct strip casting and slow cooled ingot casting. The microstructures were examined with scanning and transmission electron microscopy, atom probe tomography and small angle neutron scattering (SANS). The formation of cementite (Fe{sub 3}C), manganese sulphides (MnS) and niobium carbo-nitrides (Nb(C,N)) was investigated in both casting conditions. The sulphides were found to be significantly refined by the higher cooling rate, and developed an average diameter of only 100 nm for the fast cooled sample, and a diameter too large to be measured with SANS in the slow cooled condition (> 1.1 μm). Slow cooling resulted in the development of classical Nb(C,N) precipitation, with an average diameter of 7.2 nm. However, after rapid cooling both the SANS and atom probe tomography data indicated that the Nb was retained in the matrix as a random solid solution. There was also some evidence that O, N and S are also retained in solid solution in levels not found during conventional processing. - Highlights: • The influence of cooling rate on microstructure is investigated in a HSLA steel. • SANS, TEM and APT are used to characterise the sulphides and Nb(C,N) precipitates. • The slow cooling rate result in the formation of Nb(C,N) precipitates. • The fast cooling rate results in a microstructure supersaturated in Nb, C and N. • The sulphides are 100 nm in the fast cooled sample and > 1 μm in the slow cooled one.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Dual Rate Adaptive Control for an Industrial Heat Supply Process Using Signal Compensation Approach

The industrial heat supply process (HSP) is a highly nonlinear cascaded process which uses a steam valve opening as its control input, the steam flow-rate as its inner loop output and the supply water temperature as its outer loop output. The relationship between the heat exchange rate and the model parameters, such as steam density, entropy, and fouling correction factor and heat exchange efficiency are unknown and nonlinear. Moreover, these model parameters vary in line with steam pressure, ambient temperature and the residuals caused by the quality variations of the circulation water. When the steam pressure and the ambient temperature are of high values and are subjected to frequent external random disturbances, the supply water temperature and the steam flow-rate would interact with each other and fluctuate a lot. This is also true when the process exhibits unknown characteristic variations of the process dynamics caused by the unexpected changes of the heat exchange residuals. As a result, it is difficult to control the supply water temperature and the rates of changes of steam flow-rate well inside their targeted ranges. In this paper, a novel compensation signal based dual rate adaptive controller is developed by representing the unknown variations of dynamics as unmodeled dynamics. In the proposed controller design, such a compensation signal is constructed and added onto the control signal obtained from the linear deterministic model based feedback control design. Such a compensation signal aims at eliminating the unmodeled dynamics and the rate of changes of the currently sample unmodeled dynamics. A successful industrial application is carried out, where it has been shown that both the supply water temperature and the rate of the changes of the steam flow-rate can be controlled well inside their targeted ranges when the process is subjected to unknown variations of its dynamics.

Adaptive signal approacg↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
DOE OSTI2012

Automatic treatment of the variance estimation bias in TRIPOLI-4 criticality calculations

The central limit (CLT) theorem States conditions under which the mean of a sufficiently large number of independent random variables, each with finite mean and variance, will be approximately normally distributed. The use of Monte Carlo transport codes, such as Tripoli4, relies on those conditions. While these are verified in protection applications (the cycles provide independent measurements of fluxes and related quantities), the hypothesis of independent estimates/cycles is broken in criticality mode. Indeed the power iteration technique used in this mode couples a generation to its progeny. Often, after what is called 'source convergence' this coupling almost disappears (the solution is closed to equilibrium) but for loosely coupled systems, such as for PWR or large nuclear cores, the equilibrium is never found, or at least may take time to reach, and the variance estimation such as allowed by the CLT is under-evaluated. In this paper we first propose, by the mean of two different methods, to evaluate the typical correlation length, as measured in cycles number, and then use this information to diagnose correlation problems and to provide an improved variance estimation. Those two methods are based on Fourier spectral decomposition and on the lag k autocorrelation calculation. A theoretical modeling of the autocorrelation function, based on Gauss-Markov stochastic processes, will also be presented. Tests will be performed with Tripoli4 on a PWR pin cell. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Disordered vortex matter out of equilibrium: a Langevin molecular dynamics study

We discuss the use of Langevin molecular dynamics in the investigation of the non-equilibrium properties of disordered vortex matter. Here, our special focus is set on values of system parameters that are realistic for disordered high-T c superconductors such as YBCO. Using a discretised elastic line model, we study different aspects of vortices far from thermal equilibrium. On the one hand, we investigate steady-state properties of driven magnetic flux lines in a disordered environment, namely the current–voltage characteristics, the gyration radius and the pinning time statistics. On the other hand, we study the complex relaxation processes and glassy-like dynamics that emerge in type-II superconductors due to the intricate competition between the long-range vortex–vortex repulsion and flux pinning due to randomly placed point defects. To this end, we consider different types of sudden perturbations: temperature, magnetic field and external current quenches.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRSDate not supplied

Upscaling of 500 °C Durable SiC JFET-R Integrated Circuits

At HiTEC2018, NASA Glenn Research Center reported the first demonstration of yearlong 500°C operation of ceramic-packaged “Generation 10” ~200-transistor integrated circuits (ICs) based on two-level interconnect silicon carbide (4H-SiC) junction field effect transistors and resistors (JFET-R). This HiTEC 2021submissionupdateson-going efforts at NASA Glenn spanning two subsequent prototype IC generations “11 and 12” to increase both complexity and durability of these ICs. Increased chip complexities of around 1000 transistors/chip for Gen. 11and near 3000 transistors/chip for Gen. 12 aremade possibleby reductions in minimum layout feature sizes (including resistorwidth shrinkage from 6 μm to 2μm) coupled with enlarged die size (from 3x3 mm to 5x5mm). Gen. 11 ICs electrically tested to date include an 8-bit delta-sigma analog to digital converter (ADC) as well as upscaled random access memory (RAM)and nearly 1 kbit read only memory (ROM). However, Gen. 11 prototype ICs exhibited significantly lower yield and durability than Gen. 10 ICs. Development of revised processing is being investigated towards mitigating these issues in subsequentGen. 12 fabrication runcurrentlyin progress.

Silicon Carbide↗
NASA NTRSDate not supplied

Influence of Thermophysical Property Variability on Thermal Predictions of Ti-6Al-4V

The additive manufacturing (AM) industry has experienced rapid growth in recent decades as industrial interest in the process has grown. Because of this, there are many groups interested in simulations of the AM process. However, the influence of thermophysical property variability on the melt pool geometry during processing of various AM alloys is unclear. The goal of my work at NASA Langley Research Center (LaRC) was to characterize this influence on Ti-6Al-4V during AM processing alongside developing a tool to enable equivalent studies on other AM materials. To facilitate this process, a database tool was developed to contain and organize thermophysical data previously reported by primary sources. The specific thermophysical properties of interest for this study were density, specific heat capacity, and conductivity. Thermal diffusivity was calculated using the other three properties. This data was then fit with a Gaussian distribution and then randomly sampled from using Monte Carlo random value sampling. Using the Rosenthal equation, this sample data was used to simulate the temperature field during additive manufacturing and extract the melt pool geometry. Specifically, the melt pool’s length, width, and depth. This process was repeated an arbitrary number of times, with the default being 1000. Once this information was obtained, histograms were made showing the distributions of the sizes of the melt pool’s length, width, and depth. Representative statistical metrics of the distributions were calculated (mean, standard deviation, and coefficient of variance). This work found that at the simulated processing values, the melt pool’s width and depth had a 5.7% variation while the length had a 3.2% variation. Additionally, a tool was constructed to graph a thermal color map representation of the melt pool for specific, arbitrary values of density, specific heat, and conductivity. This tool allowed for more efficient plotting of individual queries of the thermophysical properties. The results that were observed in the research were that values reported in literature vary and this variance can have a significant impact on the simulation. Predictions of the melt pool’s dimensions show that length has a standard deviation of approximately 6 μm, width has a standard deviation of approximately 5 μm, and depth can vary by approximately 3 μm. Considering the mean sizes of length, width, and depth are 185 μm, 88 μm, and 44 μm respectively, such a deviation is significant. This shows that depth, for example, could be more than 10% larger or smaller than expected. This demonstrates that variability in the reported thermophysical properties are not negligible and should be expected to have an influence on the results of the laser powder bed fusion additive manufacturing process. When simulating this process in the future, measures should be taken to account for this discrepancy and the uncertainty involved

Justin Martin↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2015

NUMERICAL SIMULATIONS OF HELICITY CONDENSATION IN THE SOLAR CORONA

The helicity condensation model has been proposed by Antiochos to explain the observed smoothness of coronal loops and the observed buildup of magnetic shear at filament channels. The basic hypothesis of the model is that magnetic reconnection in the corona causes the magnetic stress injected by photospheric motions to collect only at those special locations where prominences are observed to form. In this work we present the first detailed quantitative MHD simulations of the reconnection evolution proposed by the helicity condensation model. We use the well-known ansatz of modeling the closed corona as an initially uniform field between two horizontal photospheric plates. The system is driven by applying photospheric rotational flows that inject magnetic helicity into the corona. The flows are confined to a finite region on the photosphere so as to mimic the finite flux system of a bipolar active region, for example. The calculations demonstrate that, contrary to common belief, opposite helicity twists do not lead to significant reconnection in such a coronal system, whereas twists with the same sense of helicity do produce substantial reconnection. Furthermore, we find that for a given amount of helicity injected into the corona, the evolution of the magnetic shear is insensitive to whether the pattern of driving photospheric motions is fixed or quasi-random. In all cases, the shear propagates via reconnection to the boundary of the flow region while the total magnetic helicity is conserved, as predicted by the model. We discuss the implications of our results for solar observations and for future, more realistic simulations of the helicity condensation process.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1973

Concepts for a theoretical and experimental study of lifting rotor random loads and vibrations (further experiments with progressing/regressing rotor flapping modes), Phase 7-C

The experiments with progressing/regressing forced rotor flapping modes have been extended in several directions and the data processing method has been considerably refined. The 16 inch hingeless 2-bladed rotor model was equipped with a new set of high precision blades which removed previously encountered tracking difficulties at high advance ratio, so that tests up to .8 rotor advance ratio could be conducted. In addition to data with 1.20 blade natural flapping frequency data at 1.10 flapping frequency were obtained. Outside the wind tunnel, tests with a ground plate located at different distances below the rotor were conducted while recording the dynamic downflow at a station .2R below the rotor plane with a hot wire anemometer.

Hohenemser, K. H.↗
DOE OSTI2021

The response field and the saddle points of quantum mechanical path integrals

Highlights: • Moyal quantum mechanics and Marinov’s path integral. • Classical and semiclassical limits of Marinov’s path integral. • Oscillating functional integrals. • Instantons of the Marinov’s path integral. In quantum statistical mechanics, Moyal’s equation governs the time evolution of Wigner functions and of more general Weyl symbols that represent the density matrix of arbitrary mixed states. A formal solution to Moyal’s equation is given by Marinov’s path integral. In this paper we demonstrate that this path integral can be regarded as the natural link between several conceptual, geometric, and dynamical issues in quantum mechanics. A unifying perspective is achieved by highlighting the pivotal role which the response field, one of the integration variables in Marinov’s integral, plays for pure states even. The discussion focuses on how the integral’s semiclassical approximation relates to its strictly classical limit; unlike for Feynman type path integrals, the latter is well defined in the Marinov case. The topics covered include a random force representation of Marinov’s integral based upon the concept of “Airy averaging”, a related discussion of positivity-violating Wigner functions describing tunneling processes, and the role of the response field in maintaining quantum coherence and enabling interference phenomena. The double slit experiment for electrons and the Bohm–Aharonov effect are analyzed as illustrative examples. Furthermore, a surprising relationship between the instantons of the Marinov path integral over an analytically continued (“Wick rotated”) response field, and the complex instantons of Feynman-type integrals is found. The latter play a prominent role in recent work towards a Picard–Lefschetz theory applicable to oscillatory path integrals and the resurgence program.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms

We present a mathematical framework for constructing and analyzing parallel algorithms for lattice kinetic Monte Carlo (KMC) simulations. The resulting algorithms have the capacity to simulate a wide range of spatio-temporal scales in spatially distributed, non-equilibrium physiochemical processes with complex chemistry and transport micro-mechanisms. Rather than focusing on constructing exactly the stochastic trajectories, our approach relies on approximating the evolution of observables, such as density, coverage, correlations and so on. More specifically, we develop a spatial domain decomposition of the Markov operator (generator) that describes the evolution of all observables according to the kinetic Monte Carlo algorithm. This domain decomposition corresponds to a decomposition of the Markov generator into a hierarchy of operators and can be tailored to specific hierarchical parallel architectures such as multi-core processors or clusters of Graphical Processing Units (GPUs). Based on this operator decomposition, we formulate parallel Fractional step kinetic Monte Carlo algorithms by employing the Trotter Theorem and its randomized variants; these schemes, (a) are partially asynchronous on each fractional step time-window, and (b) are characterized by their communication schedule between processors. The proposed mathematical framework allows us to rigorously justify the numerical and statistical consistency of the proposed algorithms, showing the convergence of our approximating schemes to the original serial KMC. The approach also provides a systematic evaluation of different processor communicating schedules. We carry out a detailed benchmarking of the parallel KMC schemes using available exact solutions, for example, in Ising-type systems and we demonstrate the capabilities of the method to simulate complex spatially distributed reactions at very large scales on GPUs. Finally, we discuss work load balancing between processors and propose a re-balancing scheme based on probabilistic mass transport methods.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Toward Long-Term Aquatic Science Products from Heritage Landsat Missions

This paper aims at generating a long-term consistent record of Landsat-derived remote sensing reflectance (Rrs) products, which are central for producing downstream aquatic science products (e.g., concentrations of total suspended solids). The products are derived from Landsat-5 and Landsat-7 observations leading to Landsat-8 era to enable retrospective analyses of inland and nearshore coastal waters. In doing so, the data processing was built into the SeaWiFS Data Analysis System (SeaDAS) followed by vicariously calibrating Landsat-7 and -5 data using reference in situ measurements and near-concurrent ocean color products, respectively. The derived Rrs products are then validated using (a) matchups using the Aerosol Robotic Network (AERONET) data measured by in situ radiometers, i.e., AERONET-OC, and (b) ocean color products at select sites in North America. Following the vicarious calibration adjustments, it is found that the overall biases in Rrs products are significantly reduced. The root-mean-square errors (RMSE), however, indicate noticeable uncertainties due to random and systematic noise. Long-term (since 1984) seasonal Rrs composites over 12 coastal and inland systems are further evaluated to explore the utility of Landsat archive processed via SeaDAS. With all the qualitative and quantitative assessments, it is concluded that with careful algorithm developments, it is possible to discern natural variability in historic water quality conditions using heritage Landsat missions. This requires the changes in Rrs exceed maximum expected uncertainties, i.e., 0.0015 [1/sr], estimated from mean RMSEs associated with the matchups and intercomparison analyses. It is also anticipated that Landsat-5 products will be less susceptible to uncertainties in turbid waters with Rrs(660) > 0.004 [1/sr], which is equivalent of ~1.2% reflectance. Overall, end-users may utilize heritage Rrs products with "fitness-for-purpose" concept in mind, i.e., products could be valuable for one application but may not be viable for another. Further research should be dedicated to enhancing atmospheric correction to account for non-negligible near-infrared reflectance in CDOM-rich and extremely turbid waters.

Pahlevan, Nima↗
DOE OSTI2020

Direct processing of PbZr 0.53 Ti 0.47 O 3 films on glass and polymeric substrates

This work reports on direct crystallization of PbZr 0.53 Ti 0.47 O 3 (PZT) thin films on glass and polymeric substrates, using pulsed thermal processing (PTP). Specifically, xenon flash lamps deliver pulses of high intensity, short duration, broadband light to the surface of a chemical solution deposited thin film, resulting in the crystallization of the film. Structural analysis by X-ray diffraction (XRD) and transmission electron microscopy show the existence of perovskite structure in nano-sized grains (≤5 nm). Local functional analysis by band excitation piezoelectric spectroscopy and electrostatic force microscopy confirm the presence of a ferroelectric phase and retention of voltage-written polarization for multiple days. Based on structural and functional analyses, strategies are discussed for optimization of pulse voltage and duration for the realization of crystalline ferroelectric thin films. For ~200 nm-thick PZT films on glass substrates, 500 μs-long pulses were required for crystallization, starting with 100 pulses at 350 V, 10 or 25 pulses at 400 V and in general lower number of pulses at higher voltages (resulting in higher radiant energy). Overall power densities of >6.4 kW/cm 2 were needed for appearance of peaks corresponding to the perovskite phase in the XRD. Films on glass processed at 350–400 V had a higher degree of 111-oriented perovskite grains. Higher applied radiant energy (through increased pulse voltage or count) resulted in more random and/or partially 001-oriented films. For ~1 μm-thick PZT films on polymeric substrates, 10 to 25 250 μs-long pulses at voltages ranging between 200 to 250 V, corresponding to power densities of ~2.8 kW/cm 2 , were optimal for maximized perovskite phase crystallization, while avoiding substrate damage.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Thermodynamic and kinetic characterization of hydroxyethylamine β-secretase-1 inhibitors

Highlights: •Kinetic and thermodynamic characterization of 10 hydroxyethylamine BACE-1 inhibitors. •Equilibrium binding of inhibitors was enthalpy driven for BACE-1. •Negative entropy of binding was observed towards BACE-1, but not Cathepsin-D. •Structural analysis demonstrates ligand binding induces a major conformational change. •Structural analysis and SPR analysis corroborate induced fit and negative entropy of binding. -- Abstract: Alzheimer’s disease (AD) is a devastating neurodegenerative disease affecting millions of people. β-Secretase-1 (BACE-1), an enzyme involved in the processing of the amyloid precursor protein (APP) to form Aβ, is a well validated target for AD. Herein, the authors characterize 10 randomly selected hydroxyethylamine (HEA) BACE-1 inhibitors in terms of their association and dissociation rate constants and thermodynamics of binding using surface plasmon resonance (SPR). Rate constants of association (k{sub a}) measured at 25 °C ranged from a low of 2.42 × 10{sup 4} M{sup −1} s{sup −1} to the highest value of 8.3 × 10{sup 5} M{sup −1} s{sup −1}. Rate constants of dissociation (k{sub d}) ranged from 1.09 × 10{sup −4} s{sup −1} (corresponding to a residence time of close to three hours), to the fastest of 0.028 s{sup −1}. Three compounds were selected for further thermodynamic analysis where it was shown that equilibrium binding was enthalpy driven while unfavorable entropy of binding was observed. Structural analysis revealed that upon ligand binding, the BACE-1flap folds down over the bound ligand causing an induced fit. The maximal difference between alpha carbon positions in the open and closed conformations of the flap was over 5 Å. Thus the negative entropy of binding determined using SPR analysis was consistent with an induced fit observed by structural analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Resolution of 100 photons and quantum generation of unbiased random numbers

Macroscopic quantum phenomena, such as observed in superfluids and superconductors, have led to promising technological advancements and some of the most important tests of fundamental physics. At present, quantum detection of light is mostly relegated to the microscale, where avalanche photodiodes are very sensitive to distinguishing single-photon events from vacuum but cannot differentiate between larger photon-number events. Beyond this, the ability to perform measurements to resolve photon numbers is highly desirable for a variety of quantum information applications including computation, sensing, and cryptography. True photon-number resolving detectors do exist, but they are currently limited to the ability to resolve on the order of 10 photons, which is too small for certain proposals. In this work, we extend photon measurement into the mesoscopic regime by implementing a detection scheme based on multiplexing highly quantum-efficient transition-edge sensors to accurately resolve photon numbers between zero and 100. Further, we then demonstrate the use of our system by implementing a quantum random number generator with no inherent bias. This method is based on sampling a coherent state in the photon-number basis and is robust against environmental noise, phase and amplitude fluctuations in the laser, loss and detector inefficiency as well as eavesdropping. Beyond true random number generation, our detection scheme serves as a means to implement quantum measurement and engineering techniques valuable for photonic quantum information processing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Electric dipole excitations near the neutron separation energies in 96 Mo

Electric dipole strength near the neutron separation energy significantly impacts nuclear structure properties and astrophysical scenarios. These excitations are complex in nature and may involve the so-called pygmy dipole resonance (PDR). Transition densities play a crucial role in understanding the nature of nuclear excited states, including collective excitations, as well as in constructing transition potentials in DWBA or coupled-channels equations. In this work, we focus on electric dipole excitations in spherical molybdenum isotopes, particularly 96 Mo, employing fully consistent Hartree-Fock-Bogoliubov (HFB) and Quasiparticle Random Phase Approximation (QRPA) methods. We analyze the dipole strength near the neutron separation energy, which represents the threshold for neutron capture processes, and examine the isospin characteristics of PDR states through transition density calculations. Examination of proton and neutron transition densities reveals distinctive features of each dipole state, indicating their isoscalar and isovector nature. We observe that the primary component in the enhanced low-energy region exhibits isovector character. The PDR displays a mixture of isoscalar and isovector nature, distinguishing it from the isovector giant dipole resonance (IVGDR). These findings lay the groundwork for future investigations into the role of transition densities in reaction models and for their application to inelastic scattering calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗