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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Behavior of Na and RbF-Treated CdS/Cu(In,Ga)Se 2 Solar Cells with Stress Testing under Heat, Light, and Junction Bias

In this work, the effects of Na and RbF alkali treatment on the metastability behavior of CdS/Cu(In,Ga)Se 2 solar cells are investigated with stress factors of heat, junction bias, and illumination. Four device types with and without Na or RbF treatments are subjected to heat- and light-soaking under open- and short-circuit (OC, SC) junction bias. Low-Na devices show a higher bandgap due to increased minimum Ga content, higher recombination current, and lower open-circuit voltage (V OC ). Devices with RbF post-deposition treatment (PDT) show an improvement in net doping density ≈10 16 cm –3 , V OC , and efficiency. Heat- and light-soaking under OC junction bias provokes an increase in net carrier concentration and V OC irrespective of the alkali treatments. After SC stress, a decrease in V OC and net carrier concentration is observed, which can be stabilized by RbF-PDT. An increase in Na and oxygen concentration in CIGS is observed for baseline and low-Na devices, respectively, after OC stress. The oxygen concentration in CdS decreases after heat- and light-soaking for devices without RbF-PDT, whereas it remains unchanged for devices with RbF-PDT. The atomic concentration profiles in CIGS significantly stabilize as a function of stress with the addition of RbF-PDT.

14 SOLAR ENERGY↗
DOE OSTI2023

Stability of Cu(In x Ga 1− x )Se 2 Solar Cells Utilizing RbF Postdeposition Treatment under a Sulfur Atmosphere

Alkali halide postdeposition treatments (PDTs) have become a key tool to maximize efficiency in Cu(In x Ga 1− x )Se 2 (CIGS) photovoltaics. RbF PDTs have emerged as an alternative to the more common Na‐ and K‐based techniques. This study utilizes temperature‐dependent current–voltage ( JVT ) measurements to study a unique RbF PDT performed in a S atmosphere. The samples are measured before and after 6 months in a desiccator to study device stability. Both samples contain Na and K which diffuse from the soda–lime glass substrate. A reference sample and a RbF + S PDT sample both show the development of a rear contact barrier after aging. The contact barrier is higher for the RbF + S PDT sample, leading to decreased current in forward bias. Series resistance is also higher in the RbF + S PDT device which leads to lower fill factor. However, after aging the reference sample has a larger decrease in open‐circuit voltage ( V OC ). Ideality factor measurements suggest Shockley–Read–Hall recombination dominates both samples. V OC versus temperature and a temperature‐dependent activation energy model are used to calculate diode activation energies for each sample condition. Both techniques produce similar values that indicate recombination primarily occurs within the bulk absorber.

14 SOLAR ENERGY↗
DOE OSTI2022

Evaluating Recombination Mechanisms in RbF Treated Cu(In${}_\mathrm{x}$Ga$_\mathrm{1-x}$)Se$_{2}$ Solar Cells

Rubidium fluoride (RbF) postdeposition treatment (PDT) has been shown to improve the performance of Cu(In x Ga 1-x )Se 2 (CIGS) photovoltaic devices. Here, in this study, temperature-dependent current voltage (JVT) and time-resolved photoluminescence (TRPL) experiments were combined with modeling using the solar cell capacitance simulator (SCAPS) computer code to investigate the effect of the RbF PDT. Two devices, one as-deposited and one with RbF PDT, were deposited by a three stage coevaporation process. JVT measurements suggest the dominant recombination mechanism may be tunneling-enhanced recombination via bandtail states, but that defect states in the bandgap can also be important. RbF PDT is shown to decrease the characteristic energy of the bandtails. TRPL data show an increase in the minority carrier lifetime after RbF PDT, leading to an improved open-circuit voltage. SCAPS modeling indicates that the dominant recombination mechanism is dependent on the specific defect makeup of a device, suggesting that small changes in processing conditions can impact device behavior. This explains the observation that, for some devices, defect states in the gap dominate while others, as is the case here, appear to be dominated by bandtails.

14 SOLAR ENERGY↗
DOE OSTI2022

Setting boundaries on the recipe for a successful RbF post-deposition treatment of CIGS

RbF post-deposition treatments have been explored in the literature for increasing the open-circuit voltage, fill factor, and hence the efficiency of Cu(In,Ga)Se 2 solar cells. However, given the few papers documenting the experimental steps, it was difficult to quickly reproduce the results. This contribution describes some of the optimization steps that led to a successful RbF PDT based on device performance. Here we present results that put boundaries on the temperatures of the RbF cell and the lamp (for sample heating) setpoint. The best recipe for our specific growth process is documented in detail so that others may copy the procedure and possibly arrive at a successful RbF PDT in a reasonable time.

14 SOLAR ENERGY↗
DOE OSTI2024

Improved VOC in RbF-Treated Cu(In,Ga)Se2 Solar Cells via Passivation of Recombination Centers

Cu(In,Ga)Se 2 (CIGS) solar cells have benefited in recent years from the addition of heavy alkali elements, such as Rb, which increase the solar cell open-circuit voltage ( V OC ). To investigate the source of this improvement, here, we compare samples with and without Rb to perform a quantitative comparison of electronic defects and minority carrier lifetime. Deep-level transient and optical spectroscopy measurements were performed on two sets of rubidium fluoride (RbF)-treated and untreated CIGS, and three distinct traps were identified regardless of RbF treatment. The RbF treatment was found to reduce the concentration of the H2 trap, which was previously found to act as a recombination center and is located preferentially at CIGS grain boundaries. Time-resolved photoluminescence measurements showed an increase in effective lifetime after RbF and nearly all lifetime improvement resulted from reductions in bulk recombination. The observed V OC improvement is well correlated with increased minority carrier lifetime and acceptor concentration, which led to increases and decreases in electron and hole quasi-Fermi levels, respectively.

Cu(In Ga)Se2 (CIGS)↗
DOE OSTI2023

Defects in RbF - Treated Cu(InxGal-x)Se2 Solar Cells and Their Impact on Voc

Cu(In,Ga)Se2 solar cell efficiency is limited by VOC due in large part to bulk defects limiting lifetime, but alkali treatments such as RbF recover some of the VOC loss. In this work, defects in RbF-treated and untreated CIGS were quantitatively characterized using DLTS and DLOS, and three main defects were identified in each sample. The RbF-PDT resulted in a large decrease in the mid-gap trap concentration, which was accompanied by a large improvement in minority carrier lifetime. This lifetime improvement combined with a change in doping accounted for a significant portion of the VOC improvement in the RbF CIGS.

charge carrier lifetime↗
DOE OSTI2025

X-Ray and Electron Spectroscopy of the CdS/(Ag,Cu)(In,Ga)Se 2 Interface With RbF Treatment

The chemical and electronic structure of the CdS/(Ag,Cu)(In,Ga)Se 2 (CdS/ACIGSe) interface for thin-film solar cells, involving an absorber with a bulk [Ag]/([Ag]+[Cu]) (AAC) ratio of 0.06, a state-of-the-art RbF post-deposition treatment (PDT), and a chemical-bath deposited CdS buffer layer, is studied. To gain a detailed and depth-resolved picture of the CdS/ACIGSe interface, synchrotron- and laboratory-based hard X-ray, soft X-ray, and UV photoelectron spectroscopy, inverse photoemission spectroscopy, and X-ray emission spectroscopy are combined. Compared to the bulk of the absorber, a Cu- and Ga-poor ACIGSe surface is found, with a slightly increased AAC ratio. Strong evidence of a Rb–In–Se species (possibly with some Ag) at the absorber surface is compiled, with a corresponding band gap of 2.79 ± 0.12 eV. This finding is in clear contrast to comparable Ag-free Cu(In,Ga)Se 2 absorbers with RbF-PDT. The Rb–In–Se surface species is not removed by the (wet-chemical) CdS deposition process, while some Se diffuses into the CdS layer and segregates at its surface. The CdS buffer layer shows a band gap of 2.48 ± 0.12 eV, and a cliff (≈ -0.4 eV) is determined in the conduction band alignment at the interface between the Rb–In–Se species and the CdS buffer.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on RbF by Materials Project

RbF is Halite, Rock Salt structured and crystallizes in the cubic Fm-3m space group. The structure is three-dimensional. Rb1+ is bonded to six equivalent F1- atoms to form a mixture of corner and edge-sharing RbF6 octahedra. The corner-sharing octahedral tilt angles are 0°. All Rb–F bond lengths are 2.87 Å. F1- is bonded to six equivalent Rb1+ atoms to form a mixture of corner and edge-sharing FRb6 octahedra. The corner-sharing octahedral tilt angles are 0°.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on RbF by Materials Project

RbF is Tetraauricupride structured and crystallizes in the cubic Pm-3m space group. The structure is three-dimensional. Rb1+ is bonded in a body-centered cubic geometry to eight equivalent F1- atoms. All Rb–F bond lengths are 2.99 Å. F1- is bonded in a body-centered cubic geometry to eight equivalent Rb1+ atoms.

36 MATERIALS SCIENCE↗
NASA NTRS1992

A fatigue damage estimator using RBF, backpropagation, and CID4 neural algorithms

Fatigue damage estimation using neural networks is described in the paper. Attention is focused on the method of data generation for both the training and test data used by radial basis function (RBF), backpropagation, and CID4 algorithms used in this study. The performance results of the three neural algorithms are analyzed in terms of their strengths and weaknesses in training.

Cios, Krzysztof J.↗
DOE OSTI2023

Generalized moving least squares vs. radial basis function finite difference methods for approximating surface derivatives

Approximating differential operators defined on two-dimensional surfaces is an important problem that arises in many areas of science and engineering. Over the past ten years, localized meshfree methods based on generalized moving least squares (GMLS) and radial basis function finite differences (RBF-FD) have been shown to be effective for this task as they can give high orders of accuracy at low computational cost, and they can be applied to surfaces defined only by point clouds. However, there have yet to be any studies that perform a direct comparison of these methods for approximating surface differential operators (SDOs). The first purpose of this work is to fill that gap. For this comparison, we focus on an RBF-FD method based on polyharmonic spline kernels and polynomials (PHS+Poly) since they are most closely related to the GMLS method. Additionally, we use a relatively new technique for approximating SDOs with RBF-FD called the tangent plane method since it is simpler than previous techniques and natural to use with PHS+Poly RBF-FD. Further, the second purpose of this work is to relate the tangent plane formulation of SDOs to the local coordinate formulation used in GMLS and to show that they are equivalent when the tangent space to the surface is known exactly. The final purpose is to use ideas from the GMLS SDO formulation to derive a new RBF-FD method for approximating the tangent space for a point cloud surface when it is unknown. For the numerical comparisons of the methods, we examine their convergence rates for approximating the surface gradient, divergence, and Laplacian as the point clouds are refined for various parameter choices. We also compare their efficiency in terms of accuracy per computational cost, both when including and excluding setup costs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Nonlinear Matrix Approximation with Radial Basis Function Components

We introduce and investigate matrix approximation by decomposition into a sum of radial basis function (RBF) components. An RBF component is a generalization of the outer product between a pair of vectors, where an RBF function replaces the scalar multiplication between individual vector elements. Even though the RBF functions are positive definite, the summation across components is not restricted to convex combinations and allows us to compute the decomposition for any real matrix that is not necessarily symmetric or positive definite. We formulate the problem of seeking such a decomposition as an optimization problem with a nonlinear and non-convex loss function. Several modern versions of the gradient descent method, including their scalable stochastic counterparts, are used to solve this problem. We provide extensive empirical evidence of the effectiveness of the RBF decomposition and that of the gradient-based fitting algorithm. While being conceptually motivated by singular value decomposition (SVD), our proposed nonlinear counterpart outperforms SVD by drastically reducing the memory required to approximate a data matrix with the same L2 error for a wide range of matrix types. For example, it leads to 2 to 6 times memory save for Gaussian noise, graph adjacency matrices, and kernel matrices. Moreover, this proximity-based decomposition can offer additional interpretability in applications that involve, e.g., capturing the inner low-dimensional structure of the data, retaining graph connectivity structure, and preserving the acutance of images.

Rebrova, Elizaveta↗
DOE OSTI2024

Adaptive Methods for Radial Basis Functions

Radial basis functions (RBFs) are a powerful tool for constructing high-order accurate reduced representations of scattered data in arbitrary dimension and on manifolds. We present a method of constructing data approximations in which we utilize a functional tail to capture a global background profile and a RBF neural network (NN) to capture the smaller-scale features. In the RBF NN the RBF centers, matrix shape parameters were selected adaptively for each RBF. We also utilized a geodesic notion of distance on the manifold on which the data lies, e.g., the spherical geodesic for data on the sphere. Although each of these ideas have been been investigated separately in previous works, their combination into a single algorithm is novel. We defined a machine learning problem in which these properties are learned to minimize the data reduction error. We demonstrate the algorithm for applications of scattered data reduction in the plane and on the sphere.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Radial basis function network learns ceramic processing and predicts related strength and density

Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The 'nodes-at-data-points' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12 percent and density with an average error of less than 2 percent. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of ceramic materials.

Cios, Krzysztof J.↗
NASA NTRS2012

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

Yao, Weigang↗