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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2010

Differential properties and attracting sets of a simplest skew product of interval maps

For a skew product of interval maps with a closed set of periodic points, the dependence of the structure of its {omega}-limit sets on its differential properties is investigated. An example of a map in this class is constructed which has the maximal differentiability properties (within a certain subclass) with respect to the variable x, is C{sup 1}-smooth in the y-variable and has one-dimensional {omega}-limit sets. Theorems are proved that give necessary conditions for one-dimensional {omega}-limit sets to exist. One of them is formulated in terms of the divergence of the series consisting of the values of a function of x; this function is the C{sup 0}-norm of the deviation of the restrictions of the fibre maps to some nondegenerate closed interval from the identity on the same interval. Another theorem is formulated in terms of the properties of the partial derivative with respect to x of the fibre maps. A complete description is given of the {omega}-limit sets of certain class of C{sup 1}-smooth skew products satisfying some natural conditions. Bibliography: 33 titles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An Intermediate-age Alpha-rich Galactic Population in K2

We explore the relationships between the chemistry, ages, and locations of stars in the Galaxy using asteroseismic data from the K2 mission and spectroscopic data from the Apache Point Galactic Evolution Experiment survey. Previous studies have used giant stars in the Kepler field to map the relationship between the chemical composition and the ages of stars at the solar circle. Consistent with prior work, we find that stars with high [α/Fe] have distinct, older ages in comparison to stars with low [α/Fe]. We provide age estimates for red giant branch (RGB) stars in the Kepler field, which support and build upon previous age estimates by taking into account the effect of α-enrichment on opacity. Including this effect for [α/Fe]-rich stars results in up to 10% older ages for low-mass stars relative to corrected solar mixture calculations. This is a significant effect that Galactic archeology studies should take into account. Looking beyond the Kepler field, we estimate ages for 735 RGB stars from the K2 mission, mapping age trends as a function of the line of sight. We find that the age distributions for low- and high-[α/Fe] stars converge with increasing distance from the Galactic plane, in agreement with suggestions from earlier work. We find that K2 stars with high [α/Fe] appear to be younger than their counterparts in the Kepler field, overlapping more significantly with a similarly aged low-[α/Fe] population. This observation may suggest that star formation or radial migration proceeds unevenly in the Galaxy.

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

Exponential Increase in Relative Biological Effectiveness Along Distal Edge of a Proton Bragg Peak as Measured by Deoxyribonucleic Acid Double-Strand Breaks

Purpose: To quantify the relative biological effectiveness (RBE) of the distal edge of the proton Bragg peak, using an in vitro assay of DNA double-strand breaks (DSBs). Methods and Materials: U2OS cells were irradiated within the plateau of a spread-out Bragg peak and at each millimeter position along the distal edge using a custom slide holder, allowing for simultaneous measurement of physical dose. A reference radiation signal was generated using photons. The DNA DSBs at 3 hours (to assess for early damage) and at 24 hours (to assess for residual damage and repair) after irradiation were measured using the γH2AX assay and quantified via flow cytometry. Results were confirmed with clonogenic survival assays. A detailed map of the RBE as a function of depth along the Bragg peak was generated using γH2AX measurements as a biological endpoint. Results: At 3 hours after irradiation, DNA DSBs were higher with protons at every point along the distal edge compared with samples irradiated with photons to similar doses. This effect was even more pronounced after 24 hours, indicating that the impact of DNA repair is less after proton irradiation relative to photons. The RBE demonstrated an exponential increase as a function of depth and was measured to be as high as 4.0 after 3 hours and as high as 6.0 after 24 hours. When the RBE-corrected dose was plotted as a function of depth, the peak effective dose was extended 2-3 mm beyond what would be expected with physical measurement. Conclusions: We generated a highly comprehensive map of the RBE of the distal edge of the Bragg peak, using a direct assay of DNA DSBs in vitro. Our data show that the RBE of the distal edge increases with depth and is significantly higher than previously reported estimates.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Resonating quantum three-coloring wave functions for the kagome quantum antiferromagnet

Motivated by the recent discovery of a macroscopically degenerate exactly solvable point of the spin-1/2 XXZ model for J z /J = –1/2 on the kagome lattice —a result that holds for arbitrary magnetization—we develop an exact mapping between its exact ”quantum three-coloring” wave functions and the characteristic localized and topological magnons. This map, involving ”resonating two-color loops,” is developed to represent exact many-body ground state wave functions for special high magnetizations. Using this map we show that these exact ground state solutions are valid for any J z /J ≥ –1/2. This demonstrates the equivalence of the ground-state wave function of the Ising, Heisenberg, and XY regimes all the way to the J z /J = –1/2 point for these high magnetization sectors. In the hardcore bosonic language, this means that a certain class of exact many-body solutions, previously argued to hold for purely repulsive interactions (J z ≥ 0), actually hold for attractive interactions as well, up to a critical interaction strength. For the case of zero magnetization, where the ground state is not exactly known, we perform density matrix renormalization group calculations. Based on the calculation of the ground state energy and measurement of order parameters, we provide evidence for a lack of any qualitative change in the ground state on finite clusters in the Ising (J z >> J), Heisenberg (J z = J), and XY (J z = 0) regimes, continuing adiabatically to the vicinity of the macroscopically degenerate J z /J = –1/2 point. Furthermore, these findings offer a framework for recent results in the literature and also suggest that the J z /J = –1/2 point is an unconventional quantum critical point whose vicinity may contain the key to resolving the spin-1/2 kagome problem.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

Here, we propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules. In particular, we define two classes of SympNets: the LA-SympNets composed of linear and activation modules, and the G-SympNets composed of gradient modules. Correspondingly, we prove two new universal approximation theorems that demonstrate that SympNets can approximate arbitrary symplectic maps based on appropriate activation functions. We then perform several experiments including the pendulum, double pendulum and three-body problems to investigate the expressivity and the generalization ability of SympNets. The simulation results show that even very small size SympNets can generalize well, and are able to handle both separable and non-separable Hamiltonian systems with data points resulting from short or long time steps. In all the test cases, SympNets outperform the baseline models, and are much faster in training and prediction. We also develop an extended version of SympNets to learn the dynamics from irregularly sampled data. This extended version of SympNets can be thought of as a universal model representing the solution to an arbitrary Hamiltonian system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

Modeling Ring-Opening Polymerization with Machine-Learned Committors

Many important industrial and biological processes depend on ring-opening polymerization (ROP), including large-scale production polymers, such as nylon 6, and a variety of biodegradable polymers and high-performance resins. Large-scale simulations (>200K atoms) of ROP were performed using a deep neural network-based method that captured the effect of the local chemical environment of the reactive sites using training data from quantum chemical methods. The utility of this method was demonstrated by modeling the cationic ROP of bis-benzoxazine, a promising resin for use in advanced high-temperature application composites (>200°C). Density functional theory calculations were used to map out the various ring opening and polymerization reaction paths that occur during the thermal processing of bis-benzoxazine. The probabilities of these reactions, as a function of local atomic configurations, were used to construct committor functions using a neural network approach. The learned committor functions were then used to determine when a particular reaction occurred within the REACTER framework for reactive molecular dynamics simulations. The experimentally observed exotherm that occurs during bis-benzoxazine ROP was captured in an effective manner in the simulations by imposing a local heating of the system that corresponds to the enthalpy of reaction for each reaction. The dependence of the final polymer morphology on the ROP reaction temperature was analyzed at the molecular scale. The results presented in this work indicate that machine-learned committor functions are a promising approach for incorporating high-fidelity reactivity criteria that capture the effect of local chemical environments into large-scale polymerization simulations.

machine learning, polymer simulations, molecular d↗
DOE OSTI2024

Grass Evolutionary Lineages Can Be Identified Using Hyperspectral Leaf Reflectance

Hyperspectral remote sensing has the potential to map numerous attributes of the Earth’s surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C 4 ), Chloridoideae (C 4 ), and Pooideae (C 3 ). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. Here, we assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf-level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite-based spectrometers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Robust Cross-correlation-based Measurement of Clump Sizes in Galaxies

Stars form in molecular complexes that are visible as giant clouds (∼10{sup 5−6} M{sub ⊙}) in nearby galaxies and as giant clumps (∼10{sup 8−9} M{sub ⊙}) in galaxies at redshifts z≈1–3. Theoretical inferences on the origin and evolution of these complexes often require robust measurements of their characteristic size, which is hard to measure at limited resolution and often ill-defined due to overlap and quasi-fractal substructure. We show that maximum and luminosity-weighted sizes of clumps seen in star formation maps (e.g., Hα) can be recovered statistically using the two-point correlation function (2PCF) if an approximate stellar surface density map is taken as the normalizing random field. After clarifying the link between Gaussian clumps and the 2PCF analytically, we design a method for measuring the diameters of Gaussian clumps with realistic quasi-fractal substructure. This method is tested using mock images of clumpy disk galaxies at different spatial resolutions and perturbed by Gaussian white noise. We find that the 2PCF can recover the input clump scale at ∼20% accuracy, as long as this scale is larger than the spatial resolution. We apply this method to the local spiral galaxy NGC 5194, as well as to three clumpy turbulent galaxies from the DYNAMO-HST sample. In both cases, our statistical measurements of Hα clump size agree with previous measurements and with the estimated Jeans lengths. However, the new measurements are free from subjective choices when fitting individual clumps.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2003

Neural Development Under Conditions of Spaceflight

One of the key tasks the developing brain must learn is how to navigate within the environment. This skill depends on the brain's ability to establish memories of places and things in the environment so that it can form cognitive maps. Earth's gravity defines the plane of orientation of the spatial environment in which animals navigate, and cognitive maps are based on this plane of orientation. Given that experience during early development plays a key role in the development of other aspects of brain function, experience in a gravitational environment is likely to be essential for the proper organization of brain regions mediating learning and memory of spatial information. Since the hippocampus is the brain region responsible for cognitive mapping abilities, this study evaluated the development of hippocampal structure and function in rats that spent part of their early development in microgravity. Litters of male and female Sprague-Dawley rats were launched into space aboard the Space Shuttle Columbia on either postnatal day eight (P8) or 14 (P14) and remained in space for 16 days. Upon return to Earth, the rats were tested for their ability to remember spatial information and navigate using a variety of tests (the Morris water maze, a modified radial arm maze, and an open field apparatus). These rats were then tested physiologically to determine whether they exhibited normal synaptic plasticity in the hippocampus. In a separate group of rats (flight and controls), the hippocampus was analyzed using anatomical, molecular biological, and biochemical techniques immediately postlanding. There were remarkably few differences between the flight groups and their Earth-bound controls in either the navigation and spatial memory tasks or activity-induced synaptic plasticity. Microscopic and immunocytochemical analyses of the brain also did not reveal differences between flight animals and ground-based controls. These data suggest that, within the developmental window studied, microgravity has minimal long-term impact on cognitive mapping function and cellular substrates important for this function. Any differences due to development in microgravity were transient and returned to normal soon after return to Earth.

Kosik, Kenneth S.↗
DOE OSTI2014

Sharper Fermi LAT images: instrument response functions for an improved event selection

The Large Area Telescope (LAT) on the Fermi Gamma-ray Space Telescope has a point-spread function (PSF) with large tails, consisting of events affected by tracker inefficiencies, inactive volumes, and hard scattering; these tails can make source confusion a limiting factor. The parameter CTBCORE, available in the publicly available Extended Fermi LAT data (available at http://fermi.gsfc.nasa.gov/ssc/data/access/), estimates the quality of each event's direction reconstruction; by implementing a cut in this parameter, the tails of the PSF can be suppressed at the cost of losing effective area. We implement cuts on CTBCORE and present updated instrument response functions derived from the Fermi LAT data itself, along with all-sky maps generated with these cuts. Having shown the effectiveness of these cuts, especially at low energies, we encourage their use in analyses where angular resolution is more important than Poisson noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Asymmetric dose–volume optimization with smoothness control for rotating-shield brachytherapy

Purpose: It is important to reduce fluence map complexity in rotating-shield brachytherapy (RSBT) inverse planning to improve delivery efficiency while maintaining plan quality. This study proposes an efficient and effective RSBT dose optimization method which enables to produce smooth fluence maps. Methods: Five cervical cancer patients each with a high-risk clinical-target-volume (HR-CTV) larger than 40 cm{sup 3} were considered as the test cases. The RSBT source was a partially shielded electronic brachytherapy source (Xoft Axxent™). The anchor RSBT plans generated by the asymmetric dose–volume optimization with smoothness control (ADOS) method were compared against those produced by the dose–surface optimization (DSO) method and inverse-planning with simulated annealing (IPSA). Either L{sub 1}-norm or L{sub 2}-norm was used to measure the smoothness of a fluence map in the proposed ADOS method as one weighted term of the objective function. Uniform dwell-time scaling was applied to all plans such that HR-CTV D{sub 90} was maximized without violating the D{sub 2cc} tolerances of the rectum, bladder, and sigmoid colon. The quality of the anchor plans was measured with HR-CTV D{sub 90} of the anchor plans. Single-shielded RSBT [(S-RSBT), RSBT with single, fix sized delivery window] and dynamic-sheilded RSBT [(D-RSBT), RSBT with dynamically varying sized delivery window] delivery plans generated based on the anchor plans were also measured, with delivery time constraints of 10, 20, and 30 min/fraction (fx). Results: The average HR-CTV D{sub 90} values of the anchor plans achieved by the ADOS, DSO, and IPSA methods were 111.5, 94.2, and 107.4 Gy, respectively, where the weighting parameter β used in ADOS with L{sub 2}-norm was set to be 100. By using S-RSBT sequencing and 20 min/fx delivery time, the corresponding D{sub 90} values were 88.8, 81.9, and 83.4 Gy; while using D-RSBT sequencing with 20 min/fx delivery time, the corresponding D{sub 90} values were 91.4, 88.3, and 78.9 Gy, respectively. The average optimization times for ADOS, DSO, and IPSA were, respectively, 77, 4, and 1800 s. By using L{sub 1}-norm instead of L{sub 2}-norm in the ADOS method, the optimization time was increased by 20 s, while the D{sub 90} was reduced by 6.8 Gy on average. ADOS-L1 was found to be more sensitive to the weighting parameter than ADOS-L2. If β was increased to 10 000, the D{sub 90} drop with ADOS-L1 was 38 Gy, while the drop with ADOS-L2 is 13 Gy. Conclusions: The ADOS method had a reasonable optimization time cost, while achieving comparable RSBT dose plans as the IPSA method, which is of much higher time complexity. Compared to the DSO and IPSA methods, ADOS is able to generate anchor plans which are more suitable for RSBT delivery while preserving the high quality of the original plans. Compared to ADOS-L1, ADOS-L2 is able to achieve better quality of anchor plans more efficiently.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2008

Modern Era Retrospective Restrospective-Analysis for Research and Applications (MERRA) Data and Services at the GES DISC

The Modern Era Retrospective-analysis for Research and Applications (MERRA) dataset is a NASA satellite era, 30 year (1979 - present), reanalysis using the Goddard Earth Observing System Data Assimilation System, Version 5 (GEOS-5). The project, run out of NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center, provides the science and application communities with a state-of-the-art global analysis with emphasis on improved estimates of the hydrological cycle over a broad range of weather and climate time scales. MERRA products are generated as a long-term synthesis that places the NASA EOS suite of observations in a climate context. The MERRA analysis is performed at a horizontal resolution of 2/3 longitude x 1/2 latitude (540x361 global gridpoints) with observational analyses every 6 hours. The MERRA output data will include 3 dimensional state fields for every 6 hourly analysis cycle on 42 pressure levels (or 72 terrain following model coordinate levels) from the surface through the stratosphere. Several data products are specifically designed to support chemistry and stratosphere transport modeling. The 2 dimensional surface and atmospheric diagnostics (numbering 259) are being stored on the native grid at 1 hourly intervals. These include radiation and vertical integrals of the atmosphere for water and energy budget studies and also surface diagnostics where the diurnal cycle is important. The one hourly surface and near surface data product will also facilitate research on the integrated analysis of Earth system observations in the land, ocean and cryosphere. The MERRA products are archived and distributed by the Goddard Earth Sciences Data and Information Services Center (GES DISC) through its Modeling DISC Web (MDISC) portal. Multiple data access methods and services are available for MERRA data through MDISC: (1) Mirador offers a quick, comprehensive search of MERRA and all GES DISC archived data holdings, allowing searches on keywords, location names or latitude/longitude box, and date/time, with responses within a few seconds. (2) Giovanni is a GES DISC developed Web application that provides data visualization and analysis online. Giovanni features popular visualizations such as latitude-longitude maps, animations, cross sections, profiles, time series, etc. and some basic statistical analysis functions such as scatter plots and correlation coefficient maps. Users are able to download results in several different formats, including Google Earth. (3) On-the-fly parameter subsetting of data within a spatial/temporal window is provided through a simple select and click Web page. (4) MERRA data are also available via OPeNDAP, GrADS Data Server (GDS) and can be converted to netCDF on the fly.

Berrick, Stephen W.↗
DOE OSTI2014

Searching for primordial non-Gaussianity in Planck CMB maps using a combined estimator

The extensive search for deviations from Gaussianity in cosmic microwave background radiation (CMB) data is very important due to the information about the very early moments of the universe encoded there. Recent analyses from Planck CMB data do not exclude the presence of non-Gaussianity of small amplitude, although they are consistent with the Gaussian hypothesis. The use of different techniques is essential to provide information about types and amplitudes of non-Gaussianities in the CMB data. In particular, we find interesting to construct an estimator based upon the combination of two powerful statistical tools that appears to be sensitive enough to detect tiny deviations from Gaussianity in CMB maps. This estimator combines the Minkowski functionals with a Neural Network, maximizing a tool widely used to study non-Gaussian signals with a reinforcement of another tool designed to identify patterns in a data set. We test our estimator by analyzing simulated CMB maps contaminated with different amounts of local primordial non-Gaussianity quantified by the dimensionless parameter f{sub NL}. We apply it to these sets of CMB maps and find ∼> 98% of chance of positive detection, even for small intensity local non-Gaussianity like f{sub NL} = 38±18, the current limit from Planck data for large angular scales. Additionally, we test the suitability to distinguish between primary and secondary non-Gaussianities: first we train the Neural Network with two sets, one of nearly Gaussian CMB maps (|f{sub NL}| ≤ 10) but contaminated with realistic inhomogeneous Planck noise (i.e., secondary non-Gaussianity) and the other of non-Gaussian CMB maps, that is, maps endowed with weak primordial non-Gaussianity (28 ≤ f{sub NL} ≤ 48); after that we test an ensemble composed of CMB maps either with one of these non-Gaussian contaminations, and find out that our method successfully classifies ∼ 95% of the tested maps as being CMB maps containing primordial or secondary non-Gaussianity. Furthermore, we analyze the foreground-cleaned Planck maps obtaining constraints for non-Gaussianity at large-angles that are in good agreement with recent constraints. Finally, we also test the robustness of our estimator including cut-sky masks and realistic noise maps measured by Planck, obtaining successful results as well.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

On the influence of over-parameterization in manifold based surrogates and deep neural operators

Constructing accurate and generalizable approximators (surrogate models) for complex physico-chemical processes exhibiting highly non-smooth dynamics is challenging. The main question is what type of surrogate models we should construct and should these models be under-parameterized or over-parameterized. In this work, we propose new developments and perform comparisons for two promising approaches: manifold-based polynomial chaos expansion (m-PCE) and the deep neural operator (DeepONet), and we examine the effect of over-parameterization on generalization. While m-PCE enables the construction of a mapping by first identifying low-dimensional embeddings of the input functions, parameters, and quantities of interest (QoIs), a neural operator learns the nonlinear mapping via the use of deep neural networks. Here, we demonstrate the performance of these methods in terms of generalization accuracy by solving the 2D time-dependent Brusselator reaction-diffusion system with uncertainty sources, modeling an autocatalytic chemical reaction between two species. We first propose an extension of the m-PCE by constructing a mapping between latent spaces formed by two separate embeddings of the input functions and the output QoIs. To further enhance the accuracy of the DeepONet, we introduce weight self-adaptivity in the loss function. We demonstrate that the performance of m-PCE and DeepONet is comparable for cases of relatively smooth input-output mappings. However, when highly non-smooth dynamics is considered, DeepONet shows higher approximation accuracy. We also find that for m-PCE, modest over-parameterization leads to better generalization, both within and outside of distribution, whereas aggressive over-parameterization leads to over-fitting. In contrast, an even highly over-parameterized DeepONet leads to better generalization for both smooth and non-smooth dynamics. Furthermore, we compare the performance of the above models with another recently proposed operator learning model, the Fourier Neural Operator, and show that its over-parameterization also leads to better generalization. Taken together, our studies show that m-PCE can provide very good accuracy at very low training cost, whereas a highly over-parameterized DeepONet can provide better accuracy and robustness to noise but at higher training cost. In both methods, the inference cost is negligible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

The climatological distribution of extreme Arctic winds and implications for ocean and sea ice processes

Abstract Some of the strongest near‐surface winds on Earth form in the Arctic and sub‐Arctic due to intense midlatitude cyclones and mesoscale processes, and these strong surface winds have important impacts on ocean and sea ice processes. We examine the climatological distribution of over‐ocean, near‐surface wind speeds within a Pan‐Arctic domain for 18 years (1990–2007) in four gridded data sets: the European Centre for Medium‐Range Weather Forecasts Interim reanalysis (ERA‐I), the Climate Forecast System Reanalysis, version 2 of the Common Ocean‐Ice Reference Experiment data set, and a regional climate simulation generated using the Weather Research and Forecasting (WRF) model run at 50 km (WRF50) horizontal resolution with ERA Interim as lateral boundary conditions. We estimate probability density functions, the annual cycle, and map the 50th and 99th percentile winds. We then perform the same statistical analysis of winds for 2 years when 10 km WRF data are available (June 2005 to May 2007); despite the much shorter time period, the Pan‐Arctic statistics are very similar to those from the 18 year analysis. We repeat the wind speed statistical analysis within a subdomain surrounding Greenland and find that WRF10 has consistently larger maximum wind speeds, but this difference only appears at wind speed percentiles higher than 99%. Differences in the 99th percentile wind speeds are spatially heterogeneous. An investigation of surface fluxes within WRF50 and WRF10 reveals unrealistically large sensible heat fluxes along the sea ice edge, and the geographic distribution and magnitude of these fluxes is shown to be sensitive to sea ice representation in WRF.

Hughes, Mimi↗
DOE OSTI2014

Universal Design Principles for Cascade Heterojunction Solar Cells with High Fill Factors and Internal Quantum Efficiencies Approaching 100%

Cascade heterojunction (CHJ) organic solar cells have recently emerged as an alternative to conventional bulk heterojunctions and series-connected tandems due to their significant promise for high internal quantum efficiency (IQE) and broad spectral coverage. However, CHJ devices thus far have also exhibited poor fill factor (FF), resulting in minimal enhancements (or even decreases) in power conversion efficiency (PCE) when compared with single heterojunction (SHJ) cells. In this study, the major variables controlling the CHJ maximum power point and FF are determined using a combinatorial approach. By matching the maximum power point voltage (VMPP) of the constituent parallel-connected heterojunctions (subjunctions) and minimizing the injection barriers intrinsic to CHJs, high FF and PCE can be achieved. Optimized CHJ devices are demonstrated with >99% IQE in the interlayer and a 46% increase in PCE compared to a SHJ reference (4.1% versus 2.8%). Devices with a transparent exciton dissociation layer (EDL)/interlayer/acceptor structure are employed, such that each CHJ has absorption efficiency identical to its interlayer/acceptor SHJ counterpart. Using these results, a clear map of performance as a function of material parameters is developed, providing straightforward, universal design rules to guide future engineering of molecules and layer architectures for CHJ organic photovoltaic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Kernel Manifolds: Nonlinear‐Augmentation Dimensionality Reduction Using Reproducing Kernel Hilbert Spaces

This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs, and more generally feature map-based nonlinear corrections, augment linear dimensionality reduction with a nonlinear correction term in the reconstruction map to overcome approximation accuracy limitations of purely linear approaches. While feature map-based approaches typically learn a least squares optimal polynomial correction term, we generalize this approach by learning an optimal nonlinear correction from a user-defined reproducing kernel Hilbert space. Our approach allows one to impose arbitrary nonlinear structure on the correction term, including polynomial structure, and includes feature map and radial basis function-based corrections as special cases. Furthermore, our method has relatively low training cost and has monotonically decreasing error as the latent space dimension increases. In conclusion, we compare our approach to proper orthogonal decomposition and several recent QM approaches on data from several example problems.

kernel methods↗
DOE OSTI2023

Grain boundary softening from stress assisted helium cavity coalescence in ultrafine-grained tungsten

The formation of helium cavities in coarse-grained materials produces hardening proportional to the number density and size of the cavities and due to the interaction of dislocations with intragranular helium defects. In nanostructured metals containing a high density of interfacial sinks, preferential cavity formation in the grain boundaries instead produces softening that is often attributed to enhanced interfacial plasticity. Here, employing two grades of ultrafine-grained tungsten, we explore this effect using targeted implantation studies to map cavity evolution as a function of the irradiation conditions and quantify its impact on the mechanical response through nanoindentation. Softening is reported at implantation temperatures above the threshold for preferential grain boundary cavity formation but at a sufficiently low fluence prior to the growth of intragranular cavities. Collective changes in the mean cavity size, density, and morphology beneath a residual impression on an implanted surface indicate that cavity coalescence accompanied the reduction in hardness. Complementary atomistic simulations demonstrate that, in tungsten grain structures exhibiting softening, grain boundary bubble coalescence is driven by stress concentrations that further act to localize strain in the grain boundaries through cooperative deformation processes involving local atomic shuffling and sliding, dislocation emission, and even the nucleation of unstable twinning events.

36 MATERIALS SCIENCE↗