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At least 811 records · Page 45

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DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗

NASA NTRS1999

A Neural Network Aero Design System for Advanced Turbo-Engines

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.

Sanz, Jose M.↗

DOE OSTI2021

System and method for optimizing hydrocarbon production from subsurface reservoirs

A method is described for subsurface hydrocarbon reservoir characterization including receiving a time-lapse electromagnetic (EM) dataset and a flow dataset; inverting the time-lapse EM dataset using a parametric inversion that models steel well casings to determine a volume of fluid-changed reservoir; inverting the time-lapse EM dataset and the flow dataset using a joint inversion that honors the volume of the fluid-changed reservoir to determine relative permeability and capillary pressure; and characterizing flow characteristics in the volume of the fluid-changed reservoir. The method may be executed by a computer system.

Hoversten, Gary Michael↗

DOE OSTI2016

Robotic path-finding in inverse treatment planning for stereotactic radiosurgery with continuous dose delivery

Purpose: Continuous dose delivery in radiation therapy treatments has been shown to decrease total treatment time while improving the dose conformity and distribution homogeneity over the conventional step-and-shoot approach. The authors develop an inverse treatment planning method for Gamma Knife® Perfexion™ that continuously delivers dose along a path in the target. Methods: The authors’ method is comprised of two steps: find a path within the target, then solve a mixed integer optimization model to find the optimal collimator configurations and durations along the selected path. Robotic path-finding techniques, specifically, simultaneous localization and mapping (SLAM) using an extended Kalman filter, are used to obtain a path that travels sufficiently close to selected isocentre locations. SLAM is novelly extended to explore a 3D, discrete environment, which is the target discretized into voxels. Further novel extensions are incorporated into the steering mechanism to account for target geometry. Results: The SLAM method was tested on seven clinical cases and compared to clinical, Hamiltonian path continuous delivery, and inverse step-and-shoot treatment plans. The SLAM approach improved dose metrics compared to the clinical plans and Hamiltonian path continuous delivery plans. Beam-on times improved over clinical plans, and had mixed performance compared to Hamiltonian path continuous plans. The SLAM method is also shown to be robust to path selection inaccuracies, isocentre selection, and dose distribution. Conclusions: The SLAM method for continuous delivery provides decreased total treatment time and increased treatment quality compared to both clinical and inverse step-and-shoot plans, and outperforms existing path methods in treatment quality. It also accounts for uncertainty in treatment planning by accommodating inaccuracies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Nondestructive Evaluation of Concrete: Elastic Property Imaging Through Full Waveform Inversion

Concrete is a major construction material worldwide and plays a crucial role in the nuclear industry. The elastic properties of concrete are prone to change and degrade while in service, as it is often subjected to extreme operational and environmental conditions. An accurate evaluation of concrete's elastic properties is thus essential to ensure structural integrity and safety. This is especially true for concrete in nuclear power plants, where irradiation effects significantly impact concrete mechanical properties. There are various methods to assess these properties, with ultrasound-based techniques showing high potential due to their nondestructive nature, cost-effectiveness, and safety. While several nondestructive evaluation methods exist, most rely on idealizations such as assuming homogeneous material and plane wavefronts. In this work, we address these issues by introducing an ultrasound-based nondestructive method aimed at reconstructing spatially varying images of concrete mechanical properties. By accurately modeling wave physics, including scattering and reflection, we overcome several of the aforementioned idealizations and aim to utilize the full waveform for imaging material properties through depth, resulting in more reliable images. Full waveform inversion (FWI) was first introduced by geophysicists to reconstruct subsurface elastic property images. The goal is to minimize the difference between simulated and recorded wavefield signals, often through gradient-based optimization algorithms. While FWI is primarily conducted using the acoustic approximation of the wave equation, few works focus on elastic FWI, where the goal is to reconstruct images of not only the pressure wave speed but also the shear wave speed and density (or their equivalents). This work explores the potential of using elastic FWI to predict concrete mechanical properties as an initial effort for a more accurate monitoring of concrete conditions in service. Reconstructing images of different elastic parameters enables more specificity and accurate condition diagnosis. This paper will detail this approach and provide examples demonstrating the effectiveness of elastic FWI in reconstructing comprehensive maps of concrete mechanical properties.

42 - ENGINEERING↗

NASA NTRS1962

Interplanetary Trajectory Optimization with Powerlimited Propulsion Systems

A trajectory-optimization process is described in which the optimum thrust equations are derived using the calculus of variations. The magnitude of the thrust is constrained within an upper and a lower bound, but the thrust direction is arbitrary. This formulation allows both the constant-thrust program and the variable-thrust program to be considered. For the constant-thrust program, certain propulsion-system parameters are optimized for maximum final vehicle mass. This theory has been used to study interplanetary missions to Venus and Mars using a power-limited propulsion system. Both one-way and round trip rendezvous trajectories are considered. The analysis employs a two-body inverse-square force-field model of three dimensions. An iterative routine used to solve the two-point boundary-value problem is described in the Appendix.

TRAJECTORY↗

DOE OSTI2019

Efficient optimal reconstruction of linear fields and band-powers from cosmological data

We present an efficient implementation of Wiener filtering of real-space linear field and optimal quadratic estimator of its power spectrum Band-powers. We first recast the field reconstruction into an optimization problem, which we solve using quasi-Newton optimization. We then recast the power spectrum estimation into the field marginalization problem, from which we obtain an expression that depends on the field reconstruction solution and a determinant term. We develop a novel simulation based method for the latter. We extend the simulations formalism to provide the covariance matrix for the power spectrum. We develop a flexible framework that can be used on a variety of cosmological fields and present results for a variety of test cases, using simulated examples of projected density fields, projected shear maps from galaxy lensing, and observed Cosmic Microwave Background (CMB) temperature anisotropies, with a wide range of map incompleteness and variable noise. For smaller cases where direct numerical inversion is possible, we show that our solution matches that created by direct Wiener Filtering at a fraction of the overall computation cost. Even more significant reduction of computational is achieved by this implementation of optimal quadratic estimator due to the fast evaluation of the Hessian matrix. This technique allows for accurate map and power spectrum reconstruction with complex masks and nontrivial noise properties.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Full waveform inversion for time-distance helioseismology

Inferring interior properties of the Sun from photospheric measurements of the seismic wavefield constitutes the helioseismic inverse problem. Deviations in seismic measurements (such as wave travel times) from their fiducial values estimated for a given model of the solar interior imply that the model is inaccurate. Contemporary inversions in local helioseismology assume that properties of the solar interior are linearly related to measured travel-time deviations. It is widely known, however, that this assumption is invalid for sunspots and active regions and is likely for supergranular flows. Here, we introduce nonlinear optimization, executed iteratively, as a means of inverting for the subsurface structure of large-amplitude perturbations. Defining the penalty functional as the L {sub 2} norm of wave travel-time deviations, we compute the total misfit gradient of this functional with respect to the relevant model parameters at each iteration around the corresponding model. The model is successively improved using either steepest descent, conjugate gradient, or the quasi-Newton limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Performing nonlinear iterations requires privileging pixels (such as those in the near field of the scatterer), a practice that is not compliant with the standard assumption of translational invariance. Measurements for these inversions, although similar in principle to those used in time-distance helioseismology, require some retooling. For the sake of simplicity in illustrating the method, we consider a two-dimensional inverse problem with only a sound-speed perturbation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Inverse design of equilibrium cluster fluids applied to a physically informed model

Inverse design strategies have proven highly useful for the discovery of interaction potentials that prompt self-assembly of a variety of interesting structures. However, often the optimized particle interactions do not have a direct relationship to experimental systems. In this work, we show that Relative Entropy minimization is able to discover physically meaningful parameter sets for a model interaction built from depletion attraction and electrostatic repulsion that yield self-assembly of size-specific clusters. Here, we then explore the sensitivity of the optimized interaction potentials with respect to deviations in the underlying physical quantities, showing that clustering behavior is largely preserved even as the optimized parameters are perturbed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

TU-F-12A-04: Differential Radiation Avoidance of Functional Liver Regions Defined by 99mTc-Sulfur Colloid SPECT/CT with Proton Therapy

Purpose: Radiotherapy for hepatocellular carcinoma patients is conventionally planned without consideration of spatial heterogeneity in hepatic function, which may increase risk of radiation-induced liver disease. Pencil beam scanning (PBS) proton radiotherapy (pRT) plans were generated to differentially decrease dose to functional liver volumes (FLV) defined on [{sup 99m}Tc]sulfur colloid (SC) SPECT/CT images (functional avoidance plans) and compared against conventional pRT plans. Methods: Three HCC patients underwent SC SPECT/CT scans for pRT planning acquired 15 min post injection over 24 min. Images were reconstructed with OSEM following scatter, collimator, and exhale CT attenuation correction. Functional liver volumes (FLV) were defined by liver:spleen uptake ratio thresholds (43% to 90% maximum). Planning objectives to FLV were based on mean SC SPECT uptake ratio relative to GTV-subtracted liver and inversely scaled to mean liver dose of 20 Gy. PTV target coverage (V{sub 95}) was matched between conventional and functional avoidance plans. PBS pRT plans were optimized in RayStation for single field uniform dose (SFUD) and systematically perturbed to verify robustness to uncertainty in range, setup, and motion. Relative differences in FLV DVH and target dose heterogeneity (D{sub 2}-D{sub 98})/D50 were assessed. Results: For similar liver dose between functional avoidance and conventional PBS pRT plans (D{sub mean}≤5% difference, V{sub 18Gy}≤1% difference), dose to functional liver volumes were lower in avoidance plans but varied in magnitude across patients (FLV{sub 70%max} D{sub mean}≤26% difference, V{sub 18Gy}≤8% difference). Higher PTV dose heterogeneity in avoidance plans was associated with lower functional liver dose, particularly for the largest lesion [(D{sub 2}-D{sub 98})/D{sub 50}=13%, FLV{sub 90%max}=50% difference]. Conclusion: Differential avoidance of functional liver regions defined on sulfur colloid SPECT/CT is feasible with proton therapy. The magnitude of benefit appears to be patient specific and dependent on tumor location, size, and proximity to functional volumes. Further investigation in a larger cohort of patients may validate the clinical utility of functional avoidance planning of HCC radiotherapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Deep learning for accelerated all-dielectric metasurface design

Deep learning has risen to the forefront of many fields in recent years, overcoming challenges previously considered intractable with conventional means. Materials discovery and optimization is one such field, but significant challenges remain, including the requirement of large labeled datasets and one-to-many mapping that arises in solving the inverse problem. Here we demonstrate modeling of complex all-dielectric metasurface systems with deep neural networks, using both the metasurface geometry and knowledge of the underlying physics as inputs. Our deep learning network is highly accurate, achieving an average mean square error of only 1.16 × 10 -3 and is over five orders of magnitude faster than conventional electromagnetic simulation software. We further develop a novel method to solve the inverse modeling problem, termed fast forward dictionary search (FFDS), which offers tremendous controls to the designer and only requires an accurate forward neural network model. These techniques significantly increase the viability of more complex all-dielectric metasurface designs and provide opportunities for the future of tailored light matter interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1975

Waveguide CO2 laser gain: Dependence on gas kinetic and discharge properties

Using a simple rate equation approach the gas kinetic and discharge properties of waveguide CO2 lasers were examined. The dependence was calculated of the population inversion and laser small signal gain on gas pressure, gas mixture, pumping rate (discharge current), tube bore diameter, and wall temperature. At higher pressures the gain is optimized by using more helium rich mixtures and smaller bore diameters. The dependence of laser tunability on the gas kinetic properties and cavity losses was determined, it was found that for loss cavities the laser tunability may substantially exceed the molecular fullwidth at half maximum. The more helium rich gas mixtures give greater tunability when cavity losses are small and less tunability when cavity losses are large. The role of the various gases in the waveguide CO2 laser is the same as that in conventional devices, by contrast with conventional lasers, the waveguide laser transition is homogeneously broadened. The dependence of gain on gas pressure and other kinetic and discharge properties differs substantially from that predicted by scaling results from conventional low pressure lasers.

Cohen, S. C.↗

DOE OSTI2023

Optimizing shift selection in multilevel Monte Carlo for disconnected diagrams in lattice QCD

The calculation of disconnected diagram contributions to physical signals is a computationally expensive task in Lattice QCD. To extract the physical signal, the trace of the inverse Lattice Dirac operator, a large sparse matrix, must be stochastically estimated. Because the variance of the stochastic estimator is typically large, variance reduction techniques must be employed. Multilevel Monte Carlo (MLMC) methods reduce the variance of the trace estimator by utilizing a telescoping sequence of estimators. Frequency Splitting is one such method that uses a sequence of inverses of shifted operators to estimate the trace of the inverse lattice Dirac operator, however there is no a priori way to select the shifts that minimize the cost of the multilevel trace estimation. Here we present a sampling and interpolation scheme that is able to predict the variances associated with Frequency Splitting under displacements of the underlying space time lattice. The interpolation scheme is able to predict the variances to high accuracy and therefore chooses shifts that correspond to an approximate minimum of the cost for the trace estimation. We show that Frequency Splitting with the chosen shifts displays significant speedups over multigrid deflation, and that these shifts can be used for multiple configurations within the same ensemble with no penalty to performance.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1990

Gamma guidance of trajectories for coplanar, aeroassisted orbital transfer

The optimization and guidance of trajectories for coplaner, aeroassisted orbital transfer (AOT) from high Earth orbit (HEO) to low Earth orbit (LEO) are examined. In particular, HEO can be a geosynchronous Earth orbit (GEO). It is assumed that the initial and final orbits are circular, that the gravitational field is central and is governed by the inverse square law, and that at most three impulses are employed: one at HEO exit, one at atmospheric exit, and one at LEO entry. It is also assumed that, during the atmospheric pass, the trajectory is controlled via the lift coefficient. The presence of upper and lower bounds on the lift coefficient is considered. First, optimal trajectories are computed by minimizing the total velocity impulse (hence, the propellant consumption) required for AOT transfer. The sequential gradient-restoration algorithm (SGRA) is used for optimal control problems. The optimal trajectory is shown to include two branches: a relatively short descending flight branch (branch 1) and a long ascending flight branch (branch 2). Next, attention is focused on guidance trajectories capable of approximating the optimal trajectories in real time, while retaining the essential characteristics of simplicity, ease of implementation, and reliability. For the atmospheric pass, a feedback control scheme is employed and the lift coefficient is adjusted according to a two-stage gamma guidance law. Further improvements are possible via a modified gamma guidance which is more stable with respect to dispersion effects arising from navigation errors, variations of the atmospheric density, and uncertainties in the aerodynamic coefficients than gamma guidance trajectory. A byproduct of the studies on dispersion effects is the following design concept. For coplaner aeroassisted orbital transfer, the lift-range-to-weight ratio appears to play a more important role than the lift-to-drag ratio. This is because the lift-range-to-weight ratio controls mainly the minimum altitude (hence, the peak heating rate) of the guidance trajectory; on the other hand, the lift-to-drag ratio controls mainly the duration of the atmospheric pass of the guidance trajectory.

Miele, A.↗

DOE OSTI2014

Characterization of zinc oxide thin film using atomic force microscopy and optimized X-ray reflectivity by genetic algorithm

For the nanostructured thin films, the study of surface physical parameters is often of crucial importance. For example, the transport properties of ZnO thin film are strongly affected by the geometry of this film. In this study sol—gel ZnO thin film was spin coated on glass substrate. The inverse problem of thin film characterization is formulated as a parameter identification problem in which a set of parameters corresponding to the thin film property can be found by minimizing fitness functions formulated using theoretical and experimental X-ray reflectivity. Atomic force microscopy (AFM) measurements used as an independent procedure for extracting structural parameters of the film and accurate interpretation of X-ray reflectivity data. After applying Fractal calculations on the AFM measured data the Genetic algorithm (GA) method was applied for extracting other important structural parameters. Optimization of specular X-ray reflectivity by GA method gives information along the depth such as thickness, roughness, and electron density profile of layers in the film.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Predicting radiative transport properties of plasma sprayed porous ceramics

The typical yttria-stabilized zirconia material for making the thermal barrier coatings (TBCs) is intrinsically semitransparent to thermal radiation, and the unique disordered microstructures in TBCs make them surprisingly highly scattering. To quantitatively understand the influence of disordered microstructures, this paper presents a quantitative prediction on the radiative properties, especially the transport scattering coefficient of plasma sprayed TBC based on microstructure analysis and rigorous electromagnetic theory. The impact of the porosity, shape, size, and orientation of different types of voids on transport scattering coefficient is comprehensively investigated under the discrete dipole approximation. An inverse model integrating these factors together is then proposed to quantitatively connect transport scattering coefficient with microstructural information, which is also validated by available experimental data. Afterwards, an optimization procedure is carried out based on this model to obtain the optimal size and orientation distribution of the microscale voids to achieve the maximal radiation insulation performance at different operating temperatures, providing guidelines for practical coating design and fabrication. This work suggests that the current model is effective and also efficient for connecting scattering properties to microstructures and can be implemented as a quantitative tool for further studies like non-destructive infrared imaging as well as micro/nanoscale thermal design of TBCs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

TH-A-19A-04: Latent Uncertainties and Performance of a GPU-Implemented Pre-Calculated Track Monte Carlo Method

Purpose: Assessing the performance and uncertainty of a pre-calculated Monte Carlo (PMC) algorithm for proton and electron transport running on graphics processing units (GPU). While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from recycling a limited number of tracks in the pre-generated track bank is missing from the literature. With a proper uncertainty analysis, an optimal pre-generated track bank size can be selected for a desired dose calculation uncertainty. Methods: Particle tracks were pre-generated for electrons and protons using EGSnrc and GEANT4, respectively. The PMC algorithm for track transport was implemented on the CUDA programming framework. GPU-PMC dose distributions were compared to benchmark dose distributions simulated using general-purpose MC codes in the same conditions. A latent uncertainty analysis was performed by comparing GPUPMC dose values to a “ground truth” benchmark while varying the track bank size and primary particle histories. Results: GPU-PMC dose distributions and benchmark doses were within 1% of each other in voxels with dose greater than 50% of Dmax. In proton calculations, a submillimeter distance-to-agreement error was observed at the Bragg Peak. Latent uncertainty followed a Poisson distribution with the number of tracks per energy (TPE) and a track bank of 20,000 TPE produced a latent uncertainty of approximately 1%. Efficiency analysis showed a 937× and 508× gain over a single processor core running DOSXYZnrc for 16 MeV electrons in water and bone, respectively. Conclusion: The GPU-PMC method can calculate dose distributions for electrons and protons to a statistical uncertainty below 1%. The track bank size necessary to achieve an optimal efficiency can be tuned based on the desired uncertainty. Coupled with a model to calculate dose contributions from uncharged particles, GPU-PMC is a candidate for inverse planning of modulated electron radiotherapy and scanned proton beams. This work was supported in part by FRSQ-MSSS (Grant No. 22090), NSERC RG (Grant No. 432290) and CIHR MOP (Grant No. MOP-211360)

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2017

Pyrite Iron Sulfide Solar Cells Made from Solution Final Technical Report

This document summarizes research done under the SunShot Next Generation PV II project entitled, “Pyrite Iron Sulfide Solar Cells Made from Solution,” award number DE-EE0005324, at the University of California, Irvine, from 9/1/11 thru 11/30/16. The project goal was to develop iron pyrite (cubic FeS 2 ) as an absorber layer for solution-processible p-n heterojunction solar cells with a pathway to >20% power conversion efficiency. Project milestones centered around seven main Tasks: (1) make device-quality pyrite thin-films from solar ink; (2) develop an ohmic bottom contact with suitable low resistivity; (3) produce a p-n heterojunction with VOC > 400 mV; (4) make a solar cell with >5% power conversion efficiency; (5) use alloying to increase the pyrite band gap to ~1.2-1.4 eV; (6) produce a p-n heterojunction with VOC > 500 mV; and finally (7) make a solar cell with >10% power conversion efficiency. In response to project findings, the Tasks were amended midway through the project to focus particular effort on passivating the surface of pyrite in order to eliminate excessively-strong surface band bending believed to be responsible for the low VOC of pyrite diodes. Major project achievements include: (1) development and detailed characterization of several new solution syntheses of high-quality thin-film pyrite, including two “molecular ink” routes; (2) demonstration of Mo/MoS 2 bilayers as good ohmic bottom contacts to pyrite films; (3) fabrication of pyrite diodes with a glass/Mo/MoS 2 /pyrite/ZnS/ZnO/AZO layer sequence that show VOC values >400 mV and as high as 610 mV at ~1 sun illumination, although these high VOC values ultimately proved irreproducible; (4) established that ZnS is a promising n-type junction partner for pyrite; (5) used density functional theory to show that the band gap of pyrite can be increased from ~1.0 to a more optimal 1.2-1.3 eV by alloying with oxygen; (6) through extensive measurements of ultrahigh-purity pyrite single crystals, proved the existence of a conductive, hole-rich inversion layer at the surface of n-type pyrite crystals and established that the inversion layer is the likely reason for pyrite’s low VOC; (7) developed several surface passivation treatments to reduce the surface hole density, but not enough to expect a significant increase in VOC; (8) by controlling the single crystal growth conditions, reduced the concentration of near-surface deep donors by a factor of ~1000, which should be sufficient to avoid thermionic field emission (i.e., tunneling) across the pyrite surface and thereby increase pyrite VOC. Recent project results will be described in forthcoming peer-reviewed publications.

14 SOLAR ENERGY↗