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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Effect of Confinement of SIA Cluster Diffusion by Impurities on Radiation Defect Accumulation due to 14MeV neutrons in Tungsten

Impurities acting as traps, reduce the self-interstitial atom (SIA) cluster mobility, thereby increasing recombination and in turn enhancing radiation-resistance of polycrystalline materials. However, at elevated temperatures, depending on SIA-trap (impurity) binding energy, SIA clusters may trap and detrap (detach) numerous times. In tungsten, SIA clusters of all sizes glide one-dimensionally (1D) along their Burgers vector direction. Hence, detrapped SIAs would retrace their path, confining their 1D-glide between traps. However, small SIA clusters can change the direction of their 1D-glide by overcoming a rotation barrier. In addition, impurities or solutes are known to reduce this barrier. A lower rotation barrier can result in detrapped SIA clusters that diffuse in a random 1D direction, effectively leading to 3D (net-3D) diffusion. Here we investigate the effect of two cases of SIA diffusion, namely confined-1D diffusion and net-3D diffusion, on the damage accumulation in polycrystalline tungsten under 14 MeV neutron irradiation using the object kinetic Monte Carlo method. Simulations were performed using cascade debris obtained from molecular dynamics simulations with primary knock-on atom (PKA) energies corresponding to 14 MeV neutrons. In the simulations with net-3D diffusion, SIA clusters of all sizes diffuse in a random 1D direction after the clusters detrap. While for the confined 1D diffusion case, SIA clusters larger than size 5 retain their original direction of 1D-glide. In both types of simulations, trapped vacancies are assumed to be permanently immobilized. Unexpectedly, the damage accumulation was lower with confined 1D diffusion than that with net-3D. We present a systematic comparison of the influence of the two cases of SIA diffusion on the radiation damage accumulation as a function of dose rate, detrapping barrier, and impurity concentration at 1025 K.

Accumulation and recovery of lattice damage, tungs↗
DOE OSTI2019

First-principles calculations of solute transport in zirconium: Vacancy-mediated diffusion with metastable states and interstitial diffusion

Zirconium alloys are the most widely used nuclear fuel cladding materials for light water power reactors where irradiation damage causes solute redistribution, leading to degradation of alloy properties such as corrosion resistance. Designing radiation-tolerant zirconium alloys requires a thorough understanding of the atomic-scale transport behavior of the alloying elements in Zr. In this work, we perform density function theory calculations to investigate the diffusion of Sn, Cr, Fe, Be, Al, and Ni in the hexagonal close-packed (HCP) Zr matrix. We develop a methodology to accurately model the metastable vacancy states along the basal migration path, known to occur in group IV metals. We compute the vacancy-mediated solute diffusion coefficients and drag ratios using the kinetic Monte Carlo method and an analytic Green's function method—the agreement between the two validates our methodology. The computed diffusion coefficients of Sn and Al show good agreement with the experimental data and we expect these solutes to diffuse via the vacancy-mediated mechanism. We use a Green's function approach, parameterized with data from density functional theory calculations, to compute the interstitial diffusion coefficients of Cr, Fe, Be, and Ni in the HCP Zr lattice. The computed diffusion coefficients of Cr, Ni, and Be agree with the experimental measurements within one order of magnitude, while those of Fe are within two orders of magnitude of the experimental measurements. The drag ratios for Cr, Fe, Be, and Ni are positive up to 1235 K, which suggests that nonequilibrium vacancy fluxes could drag these solutes toward sinks such as dislocation loops and grain boundaries. We also compute the transport coefficients without including the metastable states, and using the eight- and thirteen-frequency model. Our results show significant differences in drag ratio for the eight- and thirteen-frequency model predictions compared with the Green's function methodology, but smaller errors in the solute diffusivity. Combining interstitial and vacancy-mediated diffusivities, we predict the unusual result that increased vacancy concentration slows down solute diffusivity, while a sufficiently high vacancy concentration can change the dominant mechanism to an accelerated vacancy-mediated diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Code for the manuscript titled "Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces"

We would like to disclose two scripts, written in Jupyter notebook, in which we implement the "Blackout Diffusion Process" described in the manuscript "Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces" (LA-UR-23-20509), to be submitted to the International Conference of Machine Learning (ICML). The abstract of the manuscript is append below. == Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, which occur in many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward diffusion process. We relate the theory to the existing continuous-state Gaussian diffusion and identify the corresponding reverse-time stochastic process and score function in the continuous-time setting, and the reverse-time mapping in the discrete-time setting. As an example of this framework, we introduce "Blackout Diffusion", which learns to produce samples from an empty image instead of from noise. Numerical experiments on the CIFAR-10 dataset confirm the feasibility of generative diffusion modeling in a discrete space. Generalizing from specific (Gaussian) forward processes to a more general framework also sheds light on how to interpret generative diffusion models and their mathematical structure, which we comment on.

Lin, Yen Ting↗
DOE OSTI2019

Evaluation of diffusion kurtosis and diffusivity from baseline staging MRI as predictive biomarkers for response to neoadjuvant chemoradiation in locally advanced rectal cancer

Purpose: To evaluate the role of diffusion kurtosis and diffusivity as potential imaging biomarkers to predict response to neoadjuvant chemoradiation therapy (CRT) from baseline staging magnetic resonance imaging (MRI) in locally advanced rectal cancer (LARC). Materials and methods: This retrospective study included 45 consecutive patients (31 male/14 female) who underwent baseline MRI with high b-value sequences (up to 1500 mm/s{sup 2}) for LARC followed by neoadjuvant chemoradiation and surgical resection. The mean age was 57.4 years (range 34.2–72.9). An abdominal radiologist using open source software manually segmented T2-weighted images. Segmentations were used to derive diffusion kurtosis and diffusivity from diffusion-weighted images as well as volumetric data. These data were analyzed with regard to tumor regression grade (TRG) using the four-tier American Joint Committee on Cancer (AJCC) classification, TRG 0–3. Proportional odds regression was used to analyze the four-level ordinal outcome. A sensitivity analysis was performed using univariable logistic regression for binary TRG groups, TRG 0/1 (> 90% response), or TRG 2/3 (< 90% response). p < 0.05 was considered significant throughout. Results: In the univariable proportional odds regression analysis, higher diffusivity summary (D{sub sum}) values were observed to be significantly associated with higher odds of being in one or more favorable TRG group (TRG 0 or 1). In other words, on average, patients with higher D{sub sum} values were more likely to be in a more favorable TRG group. These results are mostly consistent with the sensitivity analysis, in which higher values for most D{sub sum} values [all but region of interest (ROI)-max D median (p = 0.08)] were observed to be significantly associated with higher odds of being TRG 0 or 1. Tumor volume of interest (VOI) and ROI volume, ROI kurtosis mean and median, and VOI kurtosis mean and median were not significantly associated with TRG. Conclusion: Diffusivity derived from the baseline staging MRI, but not diffusion kurtosis or volumetric data, is associated with TRG and therefore shows promise as a potential imaging biomarker to predict the response to neoadjuvant chemotherapy in LARC. Clinical relevance statement: Diffusivity shows promise as a potential imaging biomarker to predict AJCC TRG following neoadjuvant CRT, which has implications for risk stratification. Patients with TRG 0/1 have 5-year disease-free survival (DFS) of 90–98%, as opposed to those who are TRG 2/3 with 5-year DFS of 68–73%.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Lessons from HAWC pulsar wind nebulae observations: The diffusion constant is not a constant; pulsars remain the likeliest sources of the anomalous positron fraction; cosmic rays are trapped for long periods of time in pockets of inefficient diffusion

Recent TeV observations of nearby pulsars with the HAWC telescope have been interpreted as evidence that the diffusion of high-energy electrons and positrons within pulsar wind nebulae is highly inefficient compared to the rest of the interstellar medium. If the diffusion coefficient well outside the nebula is close to the value inferred for the region inside the nebula, high-energy electrons and positrons produced by the two observed pulsars could not contribute significantly to the local measured cosmic-ray flux. The HAWC Collaboration thus concluded that, under the assumption of isotropic and homogeneous diffusion, the two pulsars are ruled out as sources of the anomalous high-energy positron flux. Here, we argue that since the diffusion coefficient is likely not spatially homogeneous, the assumption leading to this conclusion is flawed. We solve the diffusion equation with a radially dependent diffusion coefficient, and show that the pulsars observed by HAWC produce potentially perfect matches to the observed high-energy positron fluxes. We also study the implications of inefficient diffusion within pulsar wind nebulae on Galactic scales, and show that cosmic rays are likely to have very long residence times in regions of inefficient diffusion. We describe how this prediction can be tested with studies of the diffuse Galactic emission.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2015

Validation of a Computational Model for the SLS Core Stage Oxygen Tank Diffuser Concept and the Low Profile Diffuser - An Advanced Development Design for the SLS

The Low Profile Diffuser (LPD) project originated as an award from the Marshall Space Flight Center (MSFC) Advanced Development (ADO) office to the Main Propulsion Systems Branch (ER22). The task was created to develop and test an LPD concept that could produce comparable performance to a larger, traditionally designed, ullage gas diffuser while occupying a smaller volume envelope. Historically, ullage gas diffusers have been large, bulky devices that occupy a significant portion of the propellant tank, decreasing the tank volume available for propellant. Ullage pressurization of spacecraft propellant tanks is required to prevent boil-off of cryogenic propellants and to provide a positive pressure for propellant extraction. To achieve this, ullage gas diffusers must slow hot, high-pressure gas entering a propellant tank from supersonic speeds to only a few meters per second. Decreasing the incoming gas velocity is typically accomplished through expansion to larger areas within the diffuser which has traditionally led to large diffuser lengths. The Fluid Dynamics Branch (ER42) developed and applied advanced Computational Fluid Dynamics (CFD) analysis methods in order to mature the LPD design from and initial concept to an optimized test prototype and to provide extremely accurate pre-test predictions of diffuser performance. Additionally, the diffuser concept for the Core Stage of the Space Launch System (SLS) was analyzed in a short amount of time to guide test data collection efforts of the qualification of the device. CFD analysis of the SLS diffuser design provided new insights into the functioning of the device and was qualitatively validated against hot wire anemometry of the exterior flow field. Rigorous data analysis of the measurements was performed on static and dynamic pressure data, data from two microphones, accelerometers and hot wire anemometry with automated traverse. Feasibility of the LPD concept and validation of the computational model were demonstrated by the test data.

Brodnick, Jacob↗
DOE OSTI2018

Carbon diffusion in molten uranium: an ab initio molecular dynamics study

In this work we used ab initio molecular dynamics (AIMD) within the framework of density functional theory (DFT) and the projector-augmented wave (PAW) method to study carbon diffusion in liquid uranium at temperatures above 1600 K. The electronic interactions of carbon and uranium were described using the local density approximation (LDA). The self-diffusion of uranium based on this approach is compared with literature computational and experimental results for liquid uranium. The temperature dependence of carbon and uranium diffusion in the melt was evaluated by fitting the resulting diffusion coefficients to an Arrhenius relationship. We found that the LDA calculated activation energy for carbon was nearly twice that of uranium: 0.55±0.03 eV for carbon compared to 0.32±0.04 eV for uranium. Structural analysis of the liquid uranium-carbon system is also discussed.

Molecular dynamics computer simulation, Nuclear Pr↗
DOE OSTI2021

Modifying Li + and Anion Diffusivities in Polyacetal Electrolytes: A Pulsed-Field-Gradient NMR Study of Ion Self-Diffusion

Polyacetal electrolytes have been demonstrated as promising alternatives to liquid electrolytes and poly(ethylene oxide) (PEO) for rechargeable lithium-ion batteries; however, the relationship between polymer structure and ion motion is difficult to characterize. Here, we study structure-property trends in ion diffusion with respect to polymer composition for a systematic series of five polyacetals with varying ratios of ethylene oxide (EO) to methylene oxide (MO) units, denoted as P(xEO-yMO), and PEO. We first use 7 Li and 19 F pulsed-field-gradient NMR spectroscopy to measure cation and anion self-diffusion, respectively, in polymer/lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt mixtures. At 90 °C, we observe modest changes in Li + diffusivity across all polymer compositions, while anion (TFSI - ) self-diffusion coefficients decrease significantly with increasing MO content. At a given reduced temperature (T - T g ), all polyacetal electrolytes exhibit faster Li + self-diffusion than PEO. Intriguingly, P(EO-MO) and P(EO-2MO) also show slower TFSI - anion self-diffusion than PEO at a given reduced temperature. Molecular dynamics simulations reveal that shorter distances between acetal oxygen atoms (O-CH 2 -O) compared to ether oxygens (O-CH 2 -CH 2 -O) promote more diverse, often asymmetric, Li + coordination environments. Finally, Raman spectra reveal that anion-rich ion clusters in P(EO-MO) and P(EO-2MO) lead to decreased anion diffusivity, which along with increased cation diffusivity, support the viability of polyacetals as high-performance polymer electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Tuning Li-Ion Diffusion in α-LiMn 1– x Fe x PO 4 Nanocrystals by Antisite Defects and Embedded β-Phase for Advanced Li-Ion Batteries

Olivine-structured LiMn1-xFexPO4 has become a promising candidate for cathode materials owing to its higher working voltage of 4.1 V and thus larger energy density than that of LiFePO4, which has been used for electric vehicles batteries with the advantage of high safety but disadvantage of low energy density due to its lower working voltage of 3.4 V. One drawback of LiMn1-xFexPO4 electrode is its relatively low electronic and Li-ionic conductivity with Li-ion one-dimensional diffusion. Herein, olivine-structured α-LiMn0.5Fe0.5PO4 nanocrystals were synthesized with optimized Li-ion diffusion channels in LiMn1-xFexPO4 nanocrystals by inducing high concentrations of Fe2+-Li+ antisite defects, which showed impressive capacity improvements of approaching 162, 127, 73, and 55 mAh g-1 at 0.1, 10, 50, and 100 C, respectively, and a long-term cycling stability of maintaining about 74% capacity after 1000 cycles at 10 C. By using high-resolution transmission electron microscopy imaging and joint refinement of hard X-ray and neutron powder diffraction patterns, we revealed that the extraordinary high-rate performance could be achieved by suppressing the formation of electrochemically inactive phase (β-LiMn1-xFexPO4, which is first reported in this work) embedded in α-LiMn0.5Fe0.5PO4. Because of the coherent orientation relationship between β- and α- phases, the β-phase embedded would impede the Li+ diffusion along the [100] and/or [001] directions that was activated by the high density of Fe2+-Li+ antisite (4.24%) in α-phase. Thus, by optimizing concentrations of Fe2+-Li+ antisite defects and suppressing β-phase-embedded olivine structure, Li-ion diffusion properties in LiMn1-xFexPO4 nanocrystals can be tuned by generating new Li+ tunneling. These findings may provide insights into the design and generation of other advanced electrode materials with improved rate performance.

25 ENERGY STORAGE↗
DOE OSTI2018

27 Al Pulsed Field Gradient, Diffusion–NMR Spectroscopy of Solvation Dynamics and Ion Pairing in Alkaline Aluminate Solutions

Here, pulsed field gradient nuclear magnetic resonance (PFG-NMR) measurements were successfully applied to the 27 Al (I = 5/2) nucleus in concentrated electrolytes to investigate the diffusion of aluminate ions [Al(OH) 4 – ] in simulant high-level nuclear waste (3 M NaOH) between 25 and 85 °C. The temperature-dependent diffusion coefficients obtained from 1 H, 23 Na, and 27 Al PFG-NMR were well fit by a Vogel–Fulcher–Tammann model and a power law equation. Comparison of 27 Al diffusion coefficients of 0.1 M Al(OH) 4 – in ~3 M MOH (where M = Na + , K + , (CH 3 ) 4 N + ) at room temperature varied in agreement with the expected changes in solution viscosity via Stokes–Einstein relationship, confirming that the dominant Al species at these conditions are Al(OH) 4 – monomers. This 27 Al PFG-NMR study extends an established methodology to a previously unexplored nucleus enabling this experimental technique to be leverage for exploring ion transport, speciation, and solution structure in concentrated electrolytes.

Diffusion↗
DOE OSTI2019

Diffusion and perfusion MR parameters to assess preoperative short-course radiotherapy response in locally advanced rectal cancer: a comparative explorative study among Standardized Index of Shape by DCE-MRI, intravoxel incoherent motion- and diffusion kurtosis imaging-derived parameters

Purpose: To assess preoperative short-course radiotherapy (SCR) tumor response in locally advanced rectal cancer (LARC) by means of Standardized Index of Shape (SIS) by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters derived from diffusion-weighted MRI (DW-MRI). Materials and methods: Thirty-four patients with LARC who underwent MRI scans before and after SCR followed by delayed surgery, retrospectively, were enrolled. SIS, ADC, IVIM parameters [tissue diffusion (D{sub t}), pseudo-diffusion (D{sub p}), perfusion fraction (f{sub p})] and DKI parameters [mean diffusivity (MD), mean of diffusional kurtosis (MK)] were calculated for each patient. IVIM parameters were estimated using two methods, namely conventional biexponential fitting (CBFM) and variable projection (VARPRO). After surgery, the pathological TNM and tumor regression grade (TRG) were estimated. For each parameter, percentage changes between before and after SCR were evaluated. Furthermore, an artificial neural network was trained for outcome prediction. Nonparametric sample tests and receiver operating characteristic curve (ROC) analysis were performed. Results: Fifteen patients were classified as responders (TRG ≤ 2) and 19 as not responders (TRG > 3). Seven patients had TRG 1 (pathological complete response, pCR). Mean and standard deviation values of pre-treatment CBFM D{sub p} and mean value of VARPRO D{sub p} pre-treatment showed statistically significant differences to predict pCR. (p value at Mann–Whitney test was 0.05, 0.03 and 0.008, respectively.) Exclusively SIS percentage change showed significant differences between responder and non-responder patients after SCR (p value << 0.001) and to assess pCR after SCR (p value << 0.001). The best results to predict pCR were obtained by VARPRO Fp mean value pre-treatment with area under ROC of 0.84, a sensitivity of 96.4%, a specificity of 71.4%, a positive predictive value (PPV) of 92.9%, a negative predictive value (NPV) of 83.3% and an accuracy of 91.2%. The best results to assess after treatment complete pathological response were obtained by SIS with an area under ROC of 0.89, a sensitivity of 85.7%, a specificity of 92.6%, a PPV of 75.0%, a NPV of 96.1% and an accuracy of 91.2%. Moreover, the best results to differentiate after treatment responders vs. non-responders were obtained by SIS with an area under ROC of 0.94, a sensitivity of 93.3%, a specificity of 84.2%, a PPV of 82.4%, a NPV of 94.1% and an accuracy of 88.2%. Promising initial results were obtained using a decision tree tested with all ADC, IVIM and DKI extracted parameter: we reached high accuracy to assess pathological complete response after SCR in LARC (an accuracy of 85.3% to assess pathological complete response after SCR using VARPRO D{sub p} mean value post-treatment, ADC standard deviation value pre-treatment, MD standard deviation value post-treatment). Conclusion: SIS is a hopeful DCE-MRI angiogenic biomarker to assess preoperative treatment response after SCR with delayed surgery. Furthermore, an important prognostic role was obtained by VARPRO F{sub p} mean value pre-treatment and by a decision tree composed by diffusion parameters derived by DWI and DKI to assess pathological complete response.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

On the Importance of Using Event-Specific Wave Diffusion Rates in Modeling Diffuse Electron Precipitation

A few to tens of keV electron precipitation that carries substantial energy source down to the upper atmosphere to create aurora is manifested as an important magnetosphere-ionosphere coupling process. The precipitation is usually caused by scattering processes associated with plasma waves in the magnetosphere. The scattering process is often quantified by wave diffusion rates that indicate how fast an electron is scattered. Global models commonly use diffusion coefficients that are derived from statistical wave models. However, due to the statistical nature, many localized, transient features could be smeared out. In this study, we investigate electron precipitation using event-specific diffusion coefficients that are obtained based on simultaneous in-situ measured/inferred, rather than statistical, chorus wave dynamics. We find that the application of the event-specific diffusion coefficients associated with a more dynamic and intense chorus wave model leads more electrons, particularly at several to tens of keV in the dawn-to-noon sector at L > 3, to precipitate than using statistical coefficients. Here, the new simulation roughly captures both the intensity and variability of the precipitating flux as detected by the NOAA/POES satellites. Ionospheric electron density in the lower E region (100–120 km) observed by the mid-latitude Millstone Hill radar is also much better reproduced, while the case using statistical diffusion coefficients underestimates the ionization rate. This study implies the importance of using event-specific diffusion rates in simulating the diffuse electron precipitation and understanding the magnetosphere-ionosphere coupling.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1992

Closed-ampoule diffusion of sulfur into Cd-doped InP substrates - Dependence of S profiles on diffusion temperature and time

In order to optimize the fabrication of n(+)-p InP solar cells made by closed-ampoule diffusion of sulfur into p-InP:Cd substrates, we have investigated the influence of diffusion conditions on sulfur diffusion profiles. We show that S diffusion in InP is dominated by the P vacancy mechanism and is not characterized by a complementary error function as expected for an infinite source diffusion. The S diffusion mechanism in p-InP is qualitatively explained by examining the depth profiles of S, P, and In in the emitter layer and by taking into account the presence and composition of different compounds found to form in the In-P-S-O-Cd system as a result of diffusion.

Faur, Mircea↗
DOE OSTI2023

Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces

Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used to generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, including many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward diffusion process using exact (as opposed to variational) analysis. We relate the theory to the existing continuous-state Gaussian diffusion as well as other approaches to discrete diffusion, and identify the corresponding reverse-time stochastic process and score function in the continuous-time setting, and the reverse-time mapping in the discrete-time setting. As an example of this framework, we introduce “Blackout Diffusion”, which learns to produce samples from an empty image instead of from noise. Numerical experiments on the CIFAR-10, Binarized MNIST, and CelebA datasets confirm the feasibility of our approach. Generalizing from specific (Gaussian) forward processes to discrete-state processes without a variational approximation sheds light on how to interpret diffusion models, which we discuss.

Santos, Javier E.↗