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Results for “Analytical”
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.
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Tracking Nanoparticle Degradation across Fuel Cell Electrodes by Automated Analytical Electron Microscopy
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Experimental and Analytical Approaches for Improving the Resolution of Randomly Barcoded Transposon Insertion Sequencing (RB-TnSeq) Studies
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Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data
Abstract Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science.
An analytic theory for the degree of Arctic Amplification
Arctic Amplification (AA), the amplified surface warming in the Arctic relative to the global mean, is a robust and impactful feature of climate change. While the basic physical picture of AA has been depicted, a clear understanding of how the degree of AA is determined has not been established. Here, by deciphering the intricate role of atmospheric heat transport (AHT), we build a two-box energy-balance model of AA and derive that the degree of AA is a simple nonlinear function of the Arctic and global feedbacks, the meridional heterogeneity in radiative forcing, and the partial sensitivities of AHT to global mean warming and meridional warming gradient. The formula captures the varying degree of AA in individual climate models and attributes the variation to specific physical factors. It further conveys a concise picture of how essential physics mutually determine the degree of AA and limit the range within 1.5~3.5. Our results articulate AHT as both forcing and feedback to AA, highlighting its partial sensitivities instead of total change as the key parameters for understanding AA. We also find that the effect of lapse rate feedback, a widely-recognized major contributor to AA, is fully offset by the effect of water vapor feedback.
Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets
Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.
The Superfund Research Program Analytics Portal: linking environmental chemical exposure to biological phenotypes
The OSU/PNNL Superfund Research Program represents a longstanding collaboration to quantify Polycyclic Aromatic Hydrocarbons (PAHs) at various superfund sites in the Pacific Northwest and assess their potential impact on human health. To link the chemical measurements to biological activity, we describe the use of the zebrafish as a high-throughput developmental model of human exposure that provides quantitative measurements of the biological ramifications of exposure to toxicants. Toward this end, we have linked over 150 PAHs found at Superfund sites to the effect of these same chemicals in zebrafish, creating a rich dataset that links environmental exposure to biological response. To quantify this response, we have implemented a dose-response modelling pipeline to calculate benchmark dose parameters which enable potency comparison across over 500 chemicals and 10 zebrafish-specific phenotypes. Our portal provides public access to this dataset via an interactive web site designed to support exploration and re-use of these data by the scientific community at http://srp.pnnl.gov.
Morphologically-Directed Raman Spectroscopy as an Analytical Method for Subvisible Particle Characterization in Therapeutic Protein Product Quality
Abstract Subvisible particles (SVPs) are a critical quality attribute of injectable therapeutic proteins (TPs) that needs to be controlled due to potential risks associated with drug product quality. The current compendial methods routinely used to analyze SVPs for lot release provide information on particle size and count. However, chemical identification of individual particles is also important to address root-cause analysis. Herein, we introduce Morphologically-Directed Raman Spectroscopy (MDRS) for SVP characterization of TPs. The following particles were used for method development: (1) polystyrene microspheres, a traditional standard used in industry; (2) photolithographic (SU-8); and (3) ethylene tetrafluoroethylene (ETFE) particles, candidate reference materials developed by NIST. In our study, MDRS rendered high-resolution images for the ETFE particles (> 90%) ranging from 19 to 100 μm in size, covering most of SVP range, and generated comparable morphology data to flow imaging microscopy. Our method was applied to characterize particles formed in stressed TPs and was able to chemically identify individual particles using Raman spectroscopy. MDRS was able to compare morphology and transparency properties of proteinaceous particles with reference materials. The data suggests MDRS may complement the current TPs SVP analysis system and product quality characterization workflow throughout development and commercial lifecycle.
A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel
Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.
An analytical model of “Electron-Only” magnetic reconnection rates
Abstract “Electron-only” reconnection, which is both uncoupled from the surrounding ions and much faster than standard reconnection, is arguably ubiquitous in turbulence. One critical step to understanding the rate in this novel regime is to model the outflow speed that limits the transport of the magnetic flux, which is super ion Alfvénic but significantly lower than the electron Alfvén speed based on the asymptotic reconnecting field. Here we develop a simple model to determine this limiting speed by taking into account the multiscale nature of reconnection, the Hall-mediated electron outflow speed, and the pressure buildup within the small system. The predicted scalings of rates and various key quantities compare well with fully kinetic simulations and can be useful for interpreting the observations of NASA’s Magnetospheric-Multiscale (MMS) mission and other ongoing missions.
In situ hemolysis in a three-dimensional paper-based device for quantification of intraerythrocytic analytes
Quantitative in situ hemolysis is achieved for samples of whole blood using a chemical treatment without additional user-steps or sample preparation.
Analytic stability boundaries for compressional and global Alfvén eigenmodes driven by fast ions. II. Interaction via Landau resonance
We report that conditions for net fast ion drive are derived for beam-driven, co-propagating, sub-cyclotron compressional (CAE) and global (GAE) Alfvén eigenmodes driven by the Landau resonance with super-Alfvénic fast ions. Approximations applicable to realistic neutral beam distributions and mode characteristics observed in spherical tokamaks enable the derivation of marginal stability conditions for these modes. Such conditions successfully reproduce the stability boundaries found from numerical integration of the exact expression for local fast ion drive/damping. Coupling between the CAE and GAE branches of the dispersion due to finite ω/ω ci and |k ∥ /k ⊥ | is retained and found to be responsible for the existence of the GAE instability via this resonance. Finally, encouraging agreement is demonstrated between the approximate stability criterion, simulation results, and a database of NSTX observations of co-CAEs.
Analytical model of plasma response to external magnetic perturbation in absence of no-slip condition
Recent simulation and experimental results suggest that the magnetic island and flow on a resonant surface often do not satisfy the “no-slip” condition in the steady state. A new theoretical model on a nonlinear plasma response to external magnetic perturbation in the absence of a no-slip condition is proposed. The model is composed of the equations for the evolution of both the width and phase of the magnetic island due to forced reconnection driven by the external magnetic perturbation, and the force-balance equation for the plasma flow. When the island width is much less than the resistive layer width, the island growth is governed by the linear Hahm–Kulsrud–Taylor solution in the presence of time-dependent plasma flow. In the other regime when the island width is much larger than the resistive layer width, the evolution of both the island width and phase can be described using the Rutherford theory. The island solution is used to construct the quasilinear electromagnetic force, which, together with the viscous one, contributes to the nonlinear variation in plasma flow. The no-slip condition assumed in the conventional error field theory is not imposed here, where the island oscillation frequency depends on but does not necessarily equal to the plasma flow frequency at the rational surface.
Analytic insights into nonlocal energy transport. III. steady state Fokker Planck theory in spherical and planar geometry
This paper develops an approximate steady state Fokker Planck theory for energetic electron transport in a spherical laser fusion target. First, we apply the theory to only a small population of electrons, which is specified at the outset. Then, one determines the nonlocal electron energy flux of these electrons at a particular position. These energetic electrons may either come from the tail of the thermal distribution function or be generated by an instability. This paper develops two approximate methods of solution, which we call the “characteristic method” and “sparse eigenfunction.” The former works only in planar geometry and the latter in both planar and spherical geometry. Comparison of the two methods in planar geometry shows that even though the approximations are very different, they give about the same result, increasing their credibility. For the example we have chosen, spherical effects are not important for electrons from the tail of the distribution function but may well be for instability generated electrons, which have much higher energy. Comparing planar to spherical, one finds an additional spherical barrier protecting the fuel. It turns out that the associated fuel preheat a Fokker Planck model predicts is considerably less than that predicted by the Krook models as developed at both NRL and other places.
Frequency-dependent sensitivity of AC elastocaloric effect measurements explored through analytical and numerical models
We present a comprehensive study of the frequency-dependent sensitivity for measurements of the AC elastocaloric effect by applying both exactly soluble models and numerical methods to the oscillating heat flow problem. These models reproduce the finer details of the thermal transfer functions observed in experiments, considering here representative data for single-crystal Ba(Fe1−xCox)2As2. Based on our results, we propose a set of practical guidelines for experimentalists using this technique. This work establishes a baseline against which the frequency response of the AC elastocaloric technique can be compared and provides intuitive explanations of the detailed structure observed in experiments.