Search NASA⌕ Search

SEARCH · Search NASA

Results for “Processing maps”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

rustpix

rustpix is a high-performance, open-source Rust library with first-class Python bindings (via PyO3) for processing pixel-detector data in neutron imaging. It targets time-stamping detectors such as Timepix3 (TPX3) at ORNL's Spallation Neutron Source (VENUS beamline), where each detected neutron deposits charge across a cluster of pixels within a very high-rate event stream (96M+ hits/sec). rustpix parses TPX3 event data in parallel using memory-mapped I/O, offers four interchangeable clustering algorithms (ABS adjacency-based search, DBSCAN, graph/union-find connected components, and a parallel grid method), and extracts weighted, super-resolved centroids to produce neutron-event lists. A streaming architecture lets it process files larger than available memory. rustpix is distributed as a pip-installable Python package (with NumPy integration), Rust crates, a command-line tool, and an interactive GUI; it writes HDF5, Apache Arrow, and CSV; and it is designed to extend to TPX4 and other detector types. Released as open-source under the MIT License.

Zhang, Chen [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2014

Detection of thermal SZ-CMB lensing cross-correlation in Planck nominal mission data

The nominal mission maps from the Planck satellite contain a wealth of information about secondary anisotropies in the cosmic microwave background (CMB), including those induced by the thermal Sunyaev-Zel'dovich (tSZ) effect and gravitational lensing. As both the tSZ and CMB lensing signals trace the large-scale matter density field, the anisotropies sourced by these processes are expected to be correlated. We report the first detection of this cross-correlation signal, which we measure at 6.2σ significance using the Planck data. We take advantage of Planck's multifrequency coverage to construct a tSZ map using internal linear combination techniques, which we subsequently cross-correlate with the publicly-released Planck CMB lensing potential map. The cross-correlation is subject to contamination from the cosmic infrared background (CIB), which is known to correlate strongly with CMB lensing. We correct for this contamination via cross-correlating our tSZ map with the Planck 857 GHz map and confirm the robustness of our measurement using several null tests. We interpret the signal using halo model calculations, which indicate that the tSZ-CMB lensing cross-correlation is a unique probe of the physics of intracluster gas in high-redshift, low-mass groups and clusters. Our results are consistent with extrapolations of existing gas physics models to this previously unexplored regime and show clear evidence for contributions from both the one- and two-halo terms, but no statistically significant evidence for contributions from diffuse, unbound gas outside of collapsed halos. We also show that the amplitude of the signal depends rather sensitively on the amplitude of fluctuations (σ{sub 8}) and the matter density (Ω{sub m}), scaling as σ{sub 8}{sup 6.1}Ω{sub m}{sup 1.5} at ℓ = 1000. We constrain the degenerate combination σ{sub 8}(Ω{sub m}/0.282){sup 0.26} = 0.824±0.029, a result that is in less tension with primordial CMB constraints than some recent tSZ analyses. We also combine our measurement with the Planck measurement of the tSZ auto-power spectrum to demonstrate a technique that can in principle constrain both cosmology and the physics of intracluster gas simultaneously. Our detection is a direct confirmation that hot, ionized gas traces the dark matter distribution over a wide range of scales in the universe ( ∼ 0.1–50 Mpc/h)

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System

To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang–Suen and Guo–Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications.

Lourenço, Miguel (ORCID:0000000157673394)↗
DOE OSTI2020

Insights into cosmological structure formation with machine learning.

Our modern understanding of cosmological structure formation posits that small matter density fluctuations present in the early Universe, as traced by the cosmic microwave background, grow via gravitational instability to form extended haloes of dark matter. A theoretical understanding of the structure, evolution and formation of dark matter haloes is an essential step towards unravelling the intricate connection between halo and galaxy formation, needed to test our cosmological model against data from upcoming galaxy surveys. Physical understanding of the process of dark matter halo formation is made difficult by the highly non-linear nature of the haloes' evolution. I describe a new approach to gain physical insight into cosmological structure formation based on machine learning. This approach combines the ability of machine learning algorithms to learn non-linear relationships, with techniques that enable us to physically interpret the learnt mapping. I describe applicatio ns of th e method, with the aim of investigating which aspects of the early universe density field impact the later formation of dark matter haloes. First I present a case where the process of halo formation is turned into a binary classification problem; the algorithm predicts whether or not dark matter `particles' in the initial conditions of a simulation will collapse into haloes of a given mass range. Second, I present its generalization to regression, where the algorithm infers the final mass of the halo to which each particle will later belong. I show that the initial tidal shear does not play a significant role compared to the initial density field in establishing final halo masses. Finally, I demonstrate that extending the framework to deep learning algorithms such as convolutional neural networks allows us to explore connections between the early universe and late time haloes beyond those studied by existing analytic approximations of halo collapse.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Structure-toxicity relationship of cefoperazone and its impurities to developing zebrafish by transcriptome and Raman analysis

Cefoperazone (CFP) is a potent antibacterial agent that is widely used for the treatment of bacterial infections. Previously, we found that both the C-7 and C-3 substituents of CFP are toxic functional groups, and two groups could affect gene expression in zebrafish embryos, thereby resulting in variable abnormal phenotypes. (6R, 7S)-cefoperazone (7S-CFP) is the 7-epimer of CFP and 1-methyl-1H-tetrazole-5-thiol (MTT) is the C-3 substituent of CFP. Both molecules are impurities isolated from CFP that can induce adverse effects. Transcriptome analysis was performed in the present study to identify differentially expressed genes (DEGs), coupled with Raman mapping of individual organ regions to detect changes in the biochemical composition of zebrafish embryos, which reflect the differences in distribution of the compounds. CFP, 7S–CFP, and MTT exposure altered the expression of 254, 368, and 1153 genes, respectively. Gene ontology analysis revealed that various processes related to development, growth, and morphology of tissues were significantly enriched with DEGs. We integrated seven co-DEGs with protein-protein interaction networks and identified various developmental processes that were regulated by the three compounds, including vasodilation, eye, brain, melanogenesis, and heart looping. Our findings suggested that Calca and Ptger4a may be potentially utilized as novel biomarkers for CFP, which causes bleeding. Raman analysis indicated that CFP, 7S-CFP, and MTT exhibited abnormal maps in tissues, which coincided with changes in their expression and morphological features. This study may provide bioinformatics and spectral information that may be used in further investigations on the relationship between structure and toxicity of drugs and impurities. - Highlights: • We studied the relationship between the structure and toxicity of cefoperazone. • Transcriptome strategy was performed to identify differentially expressed genes. • We integrated seven co-DEGs with protein-protein interaction networks. • Our results founded the novel biomarkers for cefoperazone, which causes bleeding. • Raman analysis indicated that three compounds exhibited abnormal maps in tissues.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Simulation driven adaptive sampling for neutron-diffraction based strain mapping of additively manufactured parts

Neutron diffraction based strain mapping is a useful technique for measuring residual strains in additively manufactured (AM) metal parts. The measurement is traditionally done by scanning the sample in a point-wise raster pattern to extract the strain at each position. Since the overall scan can span several hours, adaptive sampling approaches using Bayesian optimization based on Gaussian process (BO-GP) regression have been introduced—demonstrating that even with a fraction of the typically made measurements the dominant strain patterns in the sample can be reconstructed. However, the parameters of the BO-GP algorithm have to be carefully chosen for best performance, and the movement time between arbitrary points can offset the time savings from a reduced number of measurement locations. In this paper, we propose algorithms to refine the BO-GP based methods by using simulations of strain patterns in AM parts based on the materials and the process used to print them. We demonstrate that the simulated strain patterns can be used to help choose better parameters for the BO-GP based framework—leading to low reconstruction error for the final strain pattern. Furthermore, we show that the strain mapping experiment can be initialized with a sampling pattern learnt from the simulation data and ordered to reduce movement time, dramatically enabling reduction in the overall time required to run the baseline BO-GP method.

Gaussian process regression↗
DOE OSTI2016

Investigation of dissimilar metal welds by energy-resolved neutron imaging

A nondestructive study of the internal structure and compositional gradient of dissimilar metal-alloy welds through energy-resolved neutron imaging is described in this paper. The ability of neutrons to penetrate thick metal objects (up to several cm) provides a unique possibility to examine samples which are opaque to other conventional techniques. The presence of Bragg edges in the measured neutron transmission spectra can be used to characterize the internal residual strain within the samples and some microstructural features, e.g. texture within the grains, while neutron resonance absorption provides the possibility to map the degree of uniformity in mixing of the participating alloys and intermetallic formation within the welds. In addition, voids and other defects can be revealed by the variation of neutron attenuation across the samples. This paper demonstrates the potential of neutron energy-resolved imaging to measure all these characteristics simultaneously in a single experiment with sub-mm spatial resolution. Two dissimilar alloy welds are used in this study: Al autogenously laser welded to steel, and Ti gas metal arc welded (GMAW) to stainless steel using Cu as a filler alloy. The cold metal transfer variant of the GMAW process was used in joining the Ti to the stainless steel in order to minimize the heat input. The distributions of the lattice parameter and texture variation in these welds as well as the presence of voids and defects in the melt region are mapped across the welds. The depth of the thermal front in the Al–steel weld is clearly resolved and could be used to optimize the welding process. As a result, a highly textured structure is revealed in the Ti to stainless steel joint where copper was used as a filler wire. The limited diffusion of Ti into the weld region is also verified by the resonance absorption.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Deep Learning Systems for Increased Safeguards Surveillance Review Productivity

Nuclear safeguards inspectors expend significant time and maintain intense focus in reviewing video surveillance for safeguards relevant events. To increase efficiency and reduce the time burden of safeguards inspectors performing surveillance data review, this paper presents a novel deep learning (DL) systems concept to integrate generalized DL models into the safeguards surveillance review workflow. The Agency is investigating several DL algorithms for object recognition, localization, tracking, and flagging relevant activities. The project team is working closely with nuclear safeguards inspectors to identify review use cases (based on specific safeguards objectives) and collect their associated requirements. We focused on CANDU and LWR Nuclear Power Plants (NPPs) and their associated dry storage areas as these present a particularly heavy burden on the inspector surveillance review process due to the number of these facilities under safeguards worldwide. Initial DL algorithm results on safeguards data are promising. Using a convolutional neural network, the team attained a mean average precision (mAP) of 92.9% identifying and localizing spent fuel (SF) casks from a 475 surveillance image dataset. Further, the team had initial success in training a recurrent neural network to identify reactor area activities in video clips, successfully indicating when SF casks enter or exit a pool. We discuss how such DL algorithms would be integrated into the Next Generation Surveillance Review (NGSR) software application. Another issue impacting review productivity is the long time inspectors may have to wait when running these algorithms in NGSR. We present a concept to pre-process remotely collected surveillance data with DL models as the data arrives to IAEA headquarters so that results are already available when starting a new review in NGSR. The proposed DL system concept shows a pathway and workflow for increasing an inspector’s surveillance review productivity by quickly and accurately identifying declared and undeclared safeguards relevant objects and activities in large quantities of surveillance imagery data.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2016

Environmental vulnerability assessment using Grey Analytic Hierarchy Process based model

Environmental management of an area describes a policy for its systematic and sustainable environmental protection. In the present study, regional environmental vulnerability assessment in Hirakud command area of Odisha, India is envisaged based on Grey Analytic Hierarchy Process method (Grey–AHP) using integrated remote sensing (RS) and geographic information system (GIS) techniques. Grey–AHP combines the advantages of classical analytic hierarchy process (AHP) and grey clustering method for accurate estimation of weight coefficients. It is a new method for environmental vulnerability assessment. Environmental vulnerability index (EVI) uses natural, environmental and human impact related factors, e.g., soil, geology, elevation, slope, rainfall, temperature, wind speed, normalized difference vegetation index, drainage density, crop intensity, agricultural DRASTIC value, population density and road density. EVI map has been classified into four environmental vulnerability zones (EVZs) namely: ‘low’, ‘moderate’ ‘high’, and ‘extreme’ encompassing 17.87%, 44.44%, 27.81% and 9.88% of the study area, respectively. EVI map indicates that the northern part of the study area is more vulnerable from an environmental point of view. EVI map shows close correlation with elevation. Effectiveness of the zone classification is evaluated by using grey clustering method. General effectiveness is in between “better” and “common classes”. This analysis demonstrates the potential applicability of the methodology. - Highlights: • Environmental vulnerability zone identification based on Grey Analytic Hierarchy Process (AHP) • The effectiveness evaluation by means of a grey clustering method with support from AHP • Use of grey approach eliminates the excessive dependency on the experience of experts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Technical Note: Implementation of biological washout processes within GATE/GEANT4—A Monte Carlo study in the case of carbon therapy treatments

Purpose: The imaging of positron emitting isotopes produced during patient irradiation is the only in vivo method used for hadrontherapy dose monitoring in clinics nowadays. However, the accuracy of this method is limited by the loss of signal due to the metabolic decay processes (biological washout). In this work, a generic modeling of washout was incorporated into the GATE simulation platform. Additionally, the influence of the washout on the β{sup +} activity distributions in terms of absolute quantification and spatial distribution was studied. Methods: First, the irradiation of a human head phantom with a {sup 12}C beam, so that a homogeneous dose distribution was achieved in the tumor, was simulated. The generated {sup 11}C and {sup 15}O distribution maps were used as β{sup +} sources in a second simulation, where the PET scanner was modeled following a detailed Monte Carlo approach. The activity distributions obtained in the presence and absence of washout processes for several clinical situations were compared. Results: Results show that activity values are highly reduced (by a factor of 2) in the presence of washout. These processes have a significant influence on the shape of the PET distributions. Differences in the distal activity falloff position of 4 mm are observed for a tumor dose deposition of 1 Gy (T{sub ini} = 0 min). However, in the case of high doses (3 Gy), the washout processes do not have a large effect on the position of the distal activity falloff (differences lower than 1 mm). The important role of the tumor washout parameters on the activity quantification was also evaluated. Conclusions: With this implementation, GATE/GEANT 4 is the only open-source code able to simulate the full chain from the hadrontherapy irradiation to the PET dose monitoring including biological effects. Results show the strong impact of the washout processes, indicating that the development of better models and measurement of biological washout data are essential.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Autonomous Nanoparticle Synthesis Guided by In Situ Multiscale Structural Characterization

Autonomous synthesis platforms promise rapid exploration of vast parameter spaces; yet, integrating in situ structural characterization in closed-loop synthesis optimization remains challenging. We demonstrate a realization of such a closed-loop platform coupled with a droplet-flow microreactor, in situ X-ray scattering methods (SAXS/WAXS), and Gaussian process optimization to synthesize citrate-reduced Au nanoparticles with targeted characteristics. The system efficiently explored ∼19,000 synthesis recipes through 365 experiments, achieving precise control over size (4–60 nm) and polydispersity (σ < 0.11) across large citrate/gold ratios, exceeding traditional synthesis boundaries (1–10). Beyond confirming classical Turkevich–Frens trends, partial-dependence analysis revealed strong nonlinear coupling among precursor, citrate, and pH effects. Combining quantitative SAXS/WAXS analysis with electron microscopy characterization, we uncovered that crystallite size (d c ) and particle size (d) follow d c = 0.18d + β, where synthesis chemistry controls the intercept β while maintaining a universal slope. This parallel-band structure enables independent tuning of crystallite domain size at fixed particle diameter through a combination of chloride, gold precursor, citrate, and pH contributions (cross-validated Spearman ρ = 0.7 ± 0.1). High-resolution electron microscopy shows multiple lattice-fringe orientations within single particles, directly confirming polycrystalline domains and the ability to tune d c at the fixed d. The platform’s validation includes indistinguishable static versus flowing measurements, stable droplet transport at 100 °C, and <5% run-to-run variation, establishing a robust framework for mapping and controlling multiscale nanoparticle structure across expansive chemical spaces. In conclusion, the developed closed-loop platform can be applied to a borad range of nanosyntheis processes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2013

Single-faced GRAYQB™: a radiation mapping device

GrayQb{trademark} is a novel technology that has the potential to characterize radioactively contaminated areas such as hot cells, gloveboxes, small and large rooms, hallways, and waste tanks. The goal of GrayQb{trademark} is to speed the process of decontaminating these areas, which reduces worker exposures and promotes ALARA considerations. The device employs Phosphorous Storage Plate (PSP) technology as its primary detector material. PSPs, commonly used for medical applications and non-destructive testing, can be read using a commercially available scanner. The goal of GrayQb{trademark} technology is to locate, quantify, and identify the sources of contamination. The purpose of the work documented in this report was to better characterize the performance of GrayQb{trademark} in its ability to present overlay images of the PSP image and the associated visual image of the location being surveyed. The results presented in this report are overlay images identifying the location of hot spots in both controlled and field environments. The GrayQb{trademark} technology has been mainly tested in a controlled environment with known distances and source characteristics such as specific known radionuclides, dose rates, and strength. The original concept for the GrayQb{trademark} device involved utilizing the six faces of a cube configuration and was designed to be positioned in the center of a contaminated area for 3D mapping. A smaller single-faced GrayQb{trademark}, dubbed GrayQb SF, was designed for the purpose of conducting the characterization testing documented in this report. This lighter 2D version is ideal for applications where entry ports are too small for a deployment of the original GrayQb™ version or where only a single surface is of interest. The shape, size, and weight of these two designs have been carefully modeled to account for most limitations encountered in hot cells, gloveboxes, and contaminated areas. GrayQb{trademark} and GrayQb{trademark} SF share the same fundamental detection system design (e.g., pinhole and PSPs). Therefore, performance tests completed on the single face GrayQB in this report is also applicable to the six- faced GrayQB (e.g., ambient light sensitivity and PSP response). This report details the characterization of the GrayQb{trademark} SF in both an uncontrolled environment; specifically, the Savannah River Site (SRS) Plutonium Fuel Form Facility in Building 235-F (Metallurgical Building) and controlled testing at SRS’s Health Physics Instrument Calibration Facility and SRS’s R&D Engineering Imaging and Radiation Systems Building. In this report, the resulting images from the Calibration Facility were obtained by overlaying the PSP and visual images manually using ImageJ. The resulting images from the Building 235-F tests presented in this report were produced using ImageJ and applying response trends developed from controlled testing results. The GrayQb{trademark} technology has been developed in two main stages at Savannah River National Laboratory (SRNL): 1) the GrayQb{trademark} development was supported by SRNL’s Laboratory Directed Research and Development Program and 2) the GrayQb{trademark} SF development and its testing in Building 235-F were supported by the Office of Deactivation and Decommissioning and Facility Engineering (EM-13), U.S. Department of Energy – Office of Environmental Management.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Impact of Backing Plate and Thermal Boundary Conditions for High-Speed Friction Stir Welding of 25-mm Thick Aluminum Alloy 7175-T79

Here this study demonstrates high-speed (150 mm/min) single pass friction stir butt joining of 25 mm thick aluminum alloy 7175-T79. To understand the impact of quenching and cooling rate on process responses, joint strength, and grain size distributions across the weld thickness, we performed a series of friction stir welding (FSW) trials in air and with a trailing water spray using steel and composite backing plates (BP). Welds made in air and trailing water spray exhibit significantly different hardness distributions across the nugget, heat affected zone (HAZ), and HAZ minimum hardness as evidenced by detailed microhardness mapping. The effect of trailing water spray (TWS) on joint efficiency overshadows the influence of BP composition due to the vastly different contribution to quenching. TWS also resulted in a multifaceted effect on FSW such as lowering processing temperature, increasing X and Z forces while lowering Y force, and narrow heat affecting zone. Finally, digital image correlation (DIC)-based fracture mode analysis and grain size measurements correlate with the hardness distribution.

42 ENGINEERING↗
DOE OSTI2012

Probing the Quark Sea and Gluons: the Electron-Ion Collider Projects

EIC is the generic name for the nuclear science-driven Electron-Ion Collider presently considered in the US. Such an EIC would be the world’s first polarized electron-proton collider, and the world’s first e-A collider. Very little remains known about the dynamical basis of the structure of hadrons and nuclei in terms of the fundamental quarks and gluons of Quantum Chromodynamics (QCD). A large community effort to sharpen a compelling nuclear science case for an EIC occurred during a ten-week program taking place at the Institute for Nuclear Theory (INT) in Seattle from September 13 to November 19, 2010. The critical capabilities of a stage-I EIC are a range in center-of-mass energies from 20 to 70 GeV and variable, full polarization of electrons and light ions (the latter both longitudinal and transverse), ion species up to A=200 or so, multiple interaction regions, and a high luminosity of about 10{sup 34} electron-nucleons per cm{sup 2} and per second. The physics program of such a stage-I EIC encompass inclusive measurements (ep/A{yields}e'+X), which require detection of the scattered lepon and/or the full scattered hadronic debris with high precision, semi-inclusive processes (ep/A{yields}e'+h+X), which require detection in coincidence with the scattered lepton of at least one (current or target region) hadron; and exclusive processes (ep/A{yields}e'+N'/A'+{gamma}/m), which require detection of all particles in the reaction. The main science themes of an EIC are to i) map the spin and spatial structure of quarks and gluons in nucleons, ii) discover the collective effects of gluons in atomic nuclei, and (iii) understand the emergence of hadronic matter from color charge. In addition, there are opportunities at an EIC for fundamental symmetry and nucleon structure measurements using the electroweak probe. To truly make headway to image the sea quarks and gluons in nucleons and nuclei, the EIC needs high luminosity over a range of energies as more exclusive scattering probabilities are small, and any integrated detector/interaction region design needs to provide uniform coverage to detect spectator and diffractive products. This is because e-p and even more e-A colliders have a large fraction of their science related to what happens to the nucleon or ion beams. As a result, the philosophy of integration of complex detectors into an extended interaction region faces challenging constraints. Designs feature crossing angles between the protons or heavy ions during collisions with electrons, to remove potential problems for the detector induced by synchrotron radiation. Designs allocate quite some detector space before the final-focus ion quads, at the cost of luminosity, given that uniform detection coverage is a must for deep exclusive and diffractive processes. The integrated EIC detector/interaction region design at JLab focused on establishing full acceptance for such processes over a wide range of proton energies (20-100 GeV) with well achievable interaction region magnets. The detector design at BNL uses the higher ion beam energies to achieve good detection efficiency for instance for protons following a DVCS reaction, for proton beam energies starting from 100 GeV. Following a recommendation of the 2007 US Nuclear Science Long-Range Planning effort, the DOE Office of Nuclear Physics (DOE/NP) has allocated accelerator R&D funds to lay the foundation for a polarized EIC. BNL, in association with JLab and DOE/NP, has also established a generic detector R&D program to address the scientific requirements for measurements at a future EIC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Data‐Efficient Generation of Synthetic Microstructures of Polymer‐Bonded Energetic Material With Fine‐Tuned Stable Diffusion

Among current deep learning approaches for synthetic image generation, diffusion-based models stand out in terms of algorithmic stability and ability to retain high-fidelity image features with detailed resolution. Here, in this work, we employ Dreambooth, a method for fine-tuning Stable Diffusion, on X-ray CT images of microstructure of the polymer-bonded form (PBX) of a commonly used high explosive, Pentaerythritol tetranitrate (PETN), which yields generative models for creating synthetic PBX images. The models developed here represent five classes (or ‘lots’) of microstructures and demonstrate successful generation of images of each class with high fidelity, as verified by computed classification accuracy of ∼ 94% or higher. Data augmentation afforded by such image synthesis can be used to more reliably decipher underlying statistics, build processing-structure correlations, recognize off-normal structural anomalies, and identify age-related changes. Ideas related to converting image data into appropriate density mapping and performing mesoscale simulation or surrogate modeling of detonation are also discussed.

Dreambooth↗
DOE OSTI2020

Towards controlled synthesis of 2D crystals by chemical vapor deposition (CVD)

We report the emergence of two-dimensional (2D) materials has captured the imagination of researchers since graphene was first exfoliated from graphite in 2004. Their exotic properties give rise to many exciting potential applications in advanced electronic, optoelectronic, energy and biomedical technologies. Scalable growth of high quality 2D materials is crucial for their adoption in technological applications the same way the arrival of high quality silicon single crystals was to the semiconductor industry. A huge amount of effort has been devoted to grow large-area, highly crystalline 2D crystals such as graphene and transition metal dichalcogenides (TMDs) through various methods. While CVD growth of wafer-scale monolayer graphene and TMDs has been demonstrated, considerable challenges still remain. In this perspective, we advocate for the focus on the crystal growth morphology as an underpinning for understanding, diagnosing and controlling the CVD process and environment for 2D material growth. Like snowflakes in nature, 2D crystals exhibit a rich variety of morphologies under different growth conditions. The mapping of crystal shapes in the growth parameter space “encodes” a wealth of information, the deciphering of which will lead to better understanding of the fundamental growth mechanism and materials properties. To this end, we envision a collective effort by the 2D materials community to establish the correlation between crystal shapes and the intrinsic thermodynamic and kinetic parameters for CVD reactions through integrated crystal growth experiment, database development and machine learning assisted predictive modeling, which will pave a robust path towards controlled synthesis of 2D materials and heterostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluation of automated stability testing in machining through closed-loop control and Bayesian machine learning

Here, this paper describes a system for automated identification of the optimal stable cutting parameters in milling through Bayesian machine learning and closed-loop control. The closed-loop control system consists of a process monitoring architecture, an analysis framework, and a feedback mechanism. The analysis framework consists of a Bayesian machine learning algorithm that learns a stability map given test results. The learned stability map is used to select parameters for stability testing using an expected improvement in the material removal rate criterion. The test parameters are communicated to the machine controller to complete the test cut through a feedback mechanism. The test cuts were monitored using an audio signal; the stability of the test cut was determined by analyzing the frequency content of the audio signal. The test result was fed back to the Bayesian learning algorithm to complete the loop. Experimental results demonstrate that the system can identify the optimal stable parameters without information about the cutting force model or the structural dynamics. The system provides a low-cost method for optimal stable parameter identification in an industrial environment.

Chatter↗
DOE OSTI2020

Accessing Atomic-scale Phosphorus Dopant Distribution in Precise Silicon Devices by Advanced STEM Imaging and Spectroscopy

The structural and chemical characterization at the atomic-scale plays a critical role in understanding the structure-property relationship in precise electrical devices such as those produced by atomic-precision advanced manufacturing (APAM). APAM, utilizing hydrogen lithography in a scanning tunneling microscope, offers a potential pathway to ultra-efficient transistors, and has been developed to produce phosphorus (P)-based donor devices integrated into bare Si substrates. Structural characterization of the buried, Si with P dopant (Si:P) delta-layer in the devices by scanning transmission electron microscopy (STEM), however, is a challenge due to similar atomic number and low concentration of the P dopants. In this paper, we describe several efforts of utilizing advanced STEM imagining and spectroscopic techniques to quantify the Si:P deltalayers. STEM imaging combining low-angle and high-angle annular dark-field (LAADF, HAADF) detectors as well as atomic-scale elemental mapping using energy-dispersive X-ray spectroscopy (EDS) are used to reveal the P and defect distribution across the delta-layer processed under various thermal conditions.

47 OTHER INSTRUMENTATION↗