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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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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Adaptive X-ray imaging with reinforcement learning

X-ray imaging is a powerful technique to scan samples in a variety of contexts including biological, environmental and materials science, but commonly requires a synchrotron light source to produce X-rays at sufficient intensity. As these facilities are expensive to operate, the available beam time is limited and always in high demand. Particularly if the illuminated samples are sparse, standard raster scanning methods can be time-consuming, with a majority of that time being spent on areas of the image that carry little information. To increase the efficiency and maximize the information gain for a given time budget, we split the scanning process into a series of steps where previous measurements are used to inform the decision making and adapt the exposure distribution at later stages of the sequence. We formulate this task as a reinforcement learning problem where the goal is to produce a sequence of exposure maps that maximize a predefined scalar metric. We demonstrate the potential of this approach in simulations where the adaptive illumination can accelerate the measurement process by up to an order of magnitude compared with standard raster scanning. Finally, we present the first results from deploying the trained agents on an X-ray fluorescence beamline at the Stanford Synchrotron Radiation Lightsource.

Reinforcement Learning↗
DOE OSTI2024

Attribute-Aware RBFs: Interactive Visualization of Time Series Particle Volumes Using RT Core Range Queries

Smoothed-particle hydrodynamics (SPH) is a mesh-free method used to simulate volumetric media in fluids, astrophysics, and solid mechanics. Visualizing these simulations is problematic because these datasets often contain millions, if not billions of particles carrying physical attributes and moving over time. Radial basis functions (RBFs) are used to model particles, and overlapping particles are interpolated to reconstruct a high-quality volumetric field; however, this interpolation process is expensive and makes interactive visualization difficult. Existing RBF interpolation schemes do not account for color-mapped attributes and are instead constrained to visualizing just the density field. To address these challenges, we exploit ray tracing cores in modern GPU architectures to accelerate scalar field reconstruction. We use a novel RBF interpolation scheme to integrate per-particle colors and densities, and leverage GPU-parallel tree construction and refitting to quickly update the tree as the simulation animates over time or when the user manipulates particle radii. We also propose a Hilbert reordering scheme to cluster particles together at the leaves of the tree to reduce tree memory consumption. Finally, we reduce the noise of volumetric shadows by adopting a spatially temporal blue noise sampling scheme. Our method can provide a more detailed and interactive view of these large, volumetric, time-series particle datasets than traditional methods, leading to new insights into these physics simulations.

Particle Volumes↗
DOE OSTI2022

Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs

Accurate prediction of geothermal reservoir responses to alternative energy production scenarios is critical for optimizing the development of the underlying resources. While the conventional physics-based models offer a comprehensive prediction tool, data-driven models provide an efficient alternative to build fit-for-purpose predictive models by extracting and using the statistical patterns in the collected data to make predictions. The recurrent neural network (RNN) is a data-driven model that is commonly applied to predict time series sequences. This paper presents a variant of RNN that also utilizes the efficiency of convolutional neural networks (CNN) for the prediction of energy production from geothermal reservoirs. Specifically, a CNN–RNN architecture is developed that takes historical well controls as input (features) and their corresponding production response data as output (labels) to learn an input-output mapping that can predict the future well production responses/performance for any given future well control inputs. The model is paired with a labeling scheme to handle real field disturbances that create data gaps. In addition to the model structure, we introduce a thorough workflow for applying the model, which includes data pre-processing, feature selection, as well as different training strategies for short-term and long-term prediction. Finally, the performance and accuracy of the model are evaluated by applying it to multiple datasets, including a field reservoir model.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Imaging Bragg Edge Analysis TooLs for Engineering Structures (iBeatles)

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) provides pulsed neutrons with energies varying from epithermal to cold. In preparation for VENUS, the neutron imaging beamline to be located at beam port 10, we have performed a series of experiments focused on wavelength-dependent radiography and computed tomography for a broad range of applications, from materials science to biological tissues.One of the time-of-flight (TOF) techniques that is of interest to the scientific community is the 2-dimensional mapping of phases and average crystalline plane orientation in samples both ex-situ and during applied stresses such as tensile loading and heating. This technique is known as Bragg edgeimaging and relies on the identification of changes of transmission values, fitting of the edge to measure its displacement, and thus identify the shift in lattice parameter due to stresses. One of the challenges of TOF imaging measurements is the amount of data and the inability to observe Bragg edge shifts in real time during an experiment. Thus, we have been focusing on creating a Python-based interface that allows fast data processing and instantaneous mapping and fitting of the Bragg edges, and their evolution through time. Python libraries and Jupyter notebooks have been implemented to facilitate decision making during an experiment. The advantage of the notebooks is the possibility to guide an experiment as they can quickly process and display Bragg edge data. These notebooks can be used independently, or can be combined in a Python Graphical User Interface (GUI) tool called iBeatles. This interface permits visualization and fitting of the Bragg edges, and ultimately back-projects the fitting results onto the radiographs to display a strain map. Assuming data collection has sufficient statistics, the strain mapping analysis can be performed on a pixel-by-pixel basis. This development is a step forward toward a better user experience at the future VENUS beamline in terms of live feedback and productivity. Analysis that used to take days of switching between different applications can now be done in minutes within the

Bilheux, JeanChristophe [Oak Ridge National Labora↗
DOE OSTI2021

Self-Supervised Anomaly Detection via Neural Autoregressive Flows with Active Learning

Many self-supervised methods have been proposed with the target of image anomaly detection. These methods often rely on the paradigm of data augmentation with predefined transformations such as flipping, cropping, and rotations. However, it is not straightforward to apply these techniques for non-image data, such as time series or tabular data, while the performance of the existing deep approaches has been under our expectation on tasks beyond images. In this work, we propose a novel active learning (AL) scheme that relied on neural autoregressive flows (NAF) for self-supervised anomaly detection, specifically on small-scale data. Unlike other generative models such as GANs or VAEs, flow-based models allow to explicitly learn the probability density and thus can assign accurate likelihoods to normal data which makes it usable to detect anomalies. The proposed NAF-AL method is achieved by efficiently generating random samples from latent space and transforming them into feature space along with likelihoods via invertible mapping. The samples with lower likelihoods are selected and further checked by outlier detection using Mahalanobis distance. The augmented samples incorporating with normal samples are used for training a better detector so as to approach decision boundaries. Compared with random transformations, NAF-AL can be interpreted as a likelihood-oriented data augmentation that is more efficient and robust. Extensive experiments show that our approach outperforms existing baselines on multiple time series and tabular datasets, and a real-world application in advanced manufacturing, with significant improvement on anomaly detection accuracy and robustness over the state-of-the-art.

Zhang, Jiaxin↗
DOE OSTI2025

Rapid Oxidation of Uranium Steel Alloys: Combustion Synthesis

This project explored the use of combustion synthesis as a rapid, high-temperature method for oxidizing uranium-bearing steel alloys. Traditional laboratory-scale synthesis methods often fail to replicate the thermal and kinetic conditions experienced by real-world particulates, particularly those formed under rapid quenching or high-temperature scenarios. Combustion synthesis offers a promising alternative by enabling fast, localized heating and flexible precursor selection. A series of targeted experiments were conducted using a U 2 NiCrFe 4 alloy as the precursor. The alloy was oxidized using combustion synthesis reactions fueled by uranyl nitrate and glycine, achieving peak temperatures exceeding 1,200 °C. Postreaction analysis using scanning electron microscopy (SEM), elemental mapping, and Raman spectroscopy revealed the formation of iron-based oxides, with limited but detectable evidence of uranium oxide phases such as UO 2 . The results indicate that under the rapid reaction and cooling conditions of combustion synthesis, iron oxides form preferentially, but uranium oxide formation is kinetically limited. These findings validate combustion synthesis as a viable method for simulating the oxidation behavior of uranium steels in extreme environments and lay the groundwork for future studies aimed at enhancing uranium oxide formation through higher temperatures or modified precursor compositions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
DOE OSTI2023

Schneider Springs Fire Study 2023 for Ecosystem Respiration Rates: Surface Water Chemistry and Hydrologic Sensor Data across the Yakima River Basin, Washington, USA (v2)

This dataset supports a broader study examining the drivers of spatial variability in wildfire impacts across the Yakima River Basin. Data provided within this dataset were generated from sample collection across 17 total sites (8 sites affected by a recent wildfire, 9 sites unaffected by a recent wildfire) within multiple rivers throughout the Yakima River Basin in Washington, USA from May-July 2023. Fire affected sites are defined as those affected by the 2021 Schneider Springs Fire, based on the drainage area of the streams being within the 2021 Schneider Springs Fire burn perimeter or not (Figure 1, below). The contents include surface water geochemistry data (dissolved organic carbon; total dissolved nitrogen; total suspended solids); short-term sonde data (specific conductivity; turbidity; pH; chlorophyll A; temperature); stream depth data; stream velocity; manual chamber open channel respiration data; sensor time-series data (oxygen; water pressure; barometric pressure); field metadata (including qualitative information on in stream and river corridor characteristics); and environmental context photos taken in the field. The dataset also includes a summary file of the sensor data and plots of the sensor data. Sensors were only recovered at 15 out of the 17 sites, and not all sensors were recovered at all 15 sites (see Methods section for more details), therefore all data does not exist at all sites. Data from a 2022 study at the same sites, as well as additional sites, can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566. The data package was originally published in November 2023. It was updated in June 2025 (v2; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one folder with field photos and one main data folder with two subfolders. The main data folder consists of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) field protocol; (5) readme; (6) international generic sample number (IGSN) mapping file; and (7) stream depth and averages. The sensor data subfolder consists of (1) sensor installation methods summary; (2) stream velocity; and (3) six subfolders. The BarotrollAtm (barometric pressure; temperature), DepthHOBO (water pressure; temperature), MantaRiver (specific conductivity; turbidity; pH; chlorophyll A; temperature), EXO (specific conductivity; pH; temperature), miniDOT (dissolved oxygen; temperature), and miniDOTManualChamber (dissolved oxygen; temperature) contain time-series data, plots, and summary files. The sample data subfolder consists of (1) total suspended solids (TSS) data; (2) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (3) total dissolved nitrogen (TN) data and averages; and (4) methods codes. All files are .csv, .pdf, .jpg, .jpeg, or .mov.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Priming antibody responses to the fusion peptide in rhesus macaques

Immunodominance of antibodies targeting non-neutralizing epitopes and the high level of somatic hypermutation within germinal centers (GCs) required for most HIV broadly neutralizing antibodies (bnAbs) are major impediments to the development of an effective HIV vaccine. Rational protein vaccine design and non-conventional immunization strategies are potential avenues to overcome these hurdles. Here, we report using implantable osmotic pumps to continuously deliver a series of epitope-targeted immunogens to rhesus macaques over the course of six months to prime and elicit antibody responses against the conserved fusion peptide (FP). GC responses and antibody specificities were tracked longitudinally using lymph node fine-needle aspirates and electron microscopy polyclonal epitope mapping (EMPEM), respectively, to show antibody responses to the FP/N611 glycan hole region were primed, although exhibited limited neutralization breadth. Application of cryoEMPEM delineated key residues for on-target and off-target responses that can drive the next round of structure-based vaccine design.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Local Resistance Measurement for Degradation of c-Si Heterojunction with Intrinsic Thin Layer (HIT) Solar Modules

Silicon heterojunction with intrinsic thin layer (or HIT) modules typically degrade at a rate of less than 1% annually in solar fields with dominant degradation in open-circuit voltage and some degradation in series resistance. However, detailed mechanisms can differ from module to module. Here, we study increases in local series resistance that occur over long-term field deployment, indicated by cell areas where the photoluminescence intensity does not degrade but the electroluminescence degrades significantly. To directly measure the local series resistance, we have cored out the local electroluminescence-degraded area, and we measured the sheet resistance by 4-point-probe and local nm-scale resistance using scanning spreading resistance microscopy (SSRM). The results by 4-point-probe show scattered sheet resistance that can be caused, for example, by nonuniform current paths through the transparent conductive oxide layer, the a-Si:H emitter, or the near-junction c-Si inversion layer. In contrast, the SSRM results indicate a relatively uniform and non-degraded resistivity on smaller nanometer spatial scales. SSRM is an atomic force microscopy-based two-terminal resistance mapping technique that measures the local resistance in nm-volume beneath the probe. The consistent resistances measured on the control and degraded samples can exclude the degradation of transparent conductive oxide resistance.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

Cation reordering instead of phase transitions: Origins and implications of contrasting lithiation mechanisms in 1D ζ- and 2D α-V 2 O 5

Substantial improvements in cycle life, rate performance, accessible voltage, and reversible capacity are required to realize the promise of Li-ion batteries in full measure. Here, we have examined insertion electrodes of the same composition (V 2 O 5 ) prepared according to the same electrode specifications and comprising particles with similar dimensions and geometries that differ only in terms of their atomic connectivity and crystal structure, specifically two-dimensional (2D) layered α-V 2 O 5 that crystallizes in an orthorhombic space group and one-dimensional (1D) tunnel-structured ζ-V 2 O 5 crystallized in a monoclinic space group. By using particles of similar dimensions, we have disentangled the role of specific structural motifs and atomistic diffusion pathways in affecting electrochemical performance by mapping the dynamical evolution of lithiation-induced structural modifications using ex situ scanning transmission X-ray microscopy, operando synchrotron X-ray diffraction measurements, and phase-field modeling. We find the operation of sharply divergent mechanisms to accommodate increasing concentrations of Li-ions: a series of distortive phase transformations that result in puckering and expansion of interlayer spacing in layered α-V 2 O 5 , as compared with cation reordering along interstitial sites in tunnel-structured ζ-V 2 O 5 . By alleviating distortive phase transformations, the ζ-V 2 O 5 cathode shows reduced voltage hysteresis, increased Li-ion diffusivity, alleviation of stress gradients, and improved capacity retention. The findings demonstrate that alternative lithiation mechanisms can be accessed in metastable compounds by dint of their reconfigured atomic connectivity and can unlock substantially improved electrochemical performance not accessible in the thermodynamically stable phase.

25 ENERGY STORAGE↗
DOE OSTI2019

Identification of Plasma Current Center by Neural Network Inference in EAST

For the efficient and safe operation of experimental advanced superconducting tokamak (EAST), it is necessary to accurately identify and control the plasma current and its central position. In this article, neural network is used to identify the position of the plasma current center. The model trained by the basic back-propagation neural network can well match the relationship between the electromagnetic diagnostic signals and plasma current center positions. Both noisy simulation data and experimental data are applied to train and test the neural network inference model. Adding 0.1% noise to the training data is proven to improve the noise immunity of the inference model. Basic neural networks trained with both noisy simulation data and actual experimental data show good results with sufficient inputs; however, in both cases, the performance degrades significantly when only the poloidal field coil currents are given as inputs. For this kind of time-series problem, the dynamic neural network containing delay and feedback architecture is introduced, and an improved model requiring much fewer inputs is trained and tested for current center inference. Furthermore, some parameters of this model are compared and analyzed in this article. With suitable neural network architecture, the mapping between the controlled variables (poloidal field coil currents) and response variables (plasma current center) can be well-established.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Field studies of PERC and Al-BSF PV module performance loss using power and I-V timeseries

We have studied the degradation of both full-sized modules and minimodules with PERC and Al-BSF cell variations in fields while considering packaging strategies. We demonstrate the implementations of data-driven tools to analyze large numbers of modules and volumes of timeseries data to obtain the performance loss and degradation pathways. This data analysis pipeline enables quantitative comparison and ranking of module variations, as well as mapping and deeper understanding of degradation mechanisms. The best performing module is a half-cell PERC, which shows a performance loss rate ( PLR ) of −0.27 ± 0.12% per annum (%/ a ) after initial losses have stabilized. Minimodule studies showed inconsistent performance rankings due to significant power loss contributions via series resistance, however, recombination losses remained stable. Overall, PERC cell variations outperform or are not distinguishable from Al-BSF cell variations.

Curran, Alan J.↗
DOE OSTI2022

Classification of Wetland Vegetation Based on NDVI Time Series from the HLS Dataset

Natural wetlands are intrinsically heterogeneous and typically composed of a mosaic of ecosystem patches with different vegetation types. Hydrological and biogeochemical processes in wetlands vary strongly among these ecosystem patches. To date, most remote sensing classification approaches for wetland vegetation either rely on coarse images that cannot capture the spatial variability of wetland vegetation or rely on very-high-resolution multi-spectral images that are detailed but very sporadic in time (less than once per year). This study aimed to use NDVI time series, generated from NASA’s HLS dataset, to classify vegetation patches. We demonstrate our approach at a temperate, coastal lake, estuarine marsh. To classify vegetation patches, a standard time series library of the four land-cover patch types was built from referencing specific locations that were identified as “pure” pixels. These were identified using a single-time high-resolution image. We calculated the distance between the HLS-NDVI time series at each pixel and the “pure”-pixel standards for each land-cover type. The resulting true-positive classified rate was >73% for all patch types other than water lily. The classification accuracy was higher in pixels of a more uniform composition. A set of vegetation maps was created for the years 2016 to 2020 at our research site to identify the vegetation changes at the site as it is affected by rapid water elevation increases in Lake Erie. Our results reveal how changes in water elevation have changed the patch distribution in significant ways, leading to the local extinction of cattail by 2019 and a continuous increase in the area cover of water lily patches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Modeling Single and Two-Phase Transport in Thin Porous Layers Using a Composite Continuum-Pore Network Formulation

In this work, a composite continuum-pore network formulation is presented to model single and two-phase transport thin porous layers, such as gas diffusion layers in polymer electrolyte fuel cells (PEFCs) and active electrodes in redox flow batteries (RFBs). The formulation can be integrated into CFD codes, thus combining the ease of implementation of continuum-based modeling and the computational power of pore-network modeling. The composite model includes a control volume (CV) mesh at the layer scale, which embeds a cubic pore network [1,2]. The pore-network model is used to determine analytically local anisotropic effective transport properties (local effective diffusivity and permeability), which are mapped onto the CV mesh to simulate transport in the porous transport layer [3,4]. Good agreement is found between the predicted global effective transport properties (global effective diffusivity and permeability) under dry and wet conditions and previous experimental data reported in the literature for Toray TGP-H series carbon paper. Water saturation distributions are also compared with results obtained using X-ray computed tomography [4]. [1] P.A. García-Salaberri, I.V. Zenyuk, J.T. Gostick, A.Z. Weber, Modeling Gas Diffusion Layer in Polymer Electrolyte Fuel Cells Using a Continuum-Based Pore-Network Formulation, ECS Trans. 97 (2020) 615. [2] P.A. García-Salaberri, Modeling diffusion and convection in thin porous transport layers using a composite continuum-network model: Application to gas diffusion layers in polymer electrolyte fuel cells, Int J. Heat Mass Transf. (2020), submitted. [3] P.A. García-Salaberri, J.T. Gostick, G. Hwang, A.Z. Weber, M. Vera, Effective diffusivity in partially-saturated carbon-fiber gas diffusion layers: Effect of local saturation and application to macroscopic continuum models, J. Power Sources 296 (2015) 440–453. [4] P.A. García-Salaberri, G. Hwang, M. Vera, A.Z. Weber, J.T. Gostick, Effective diffusivity in partially-saturated carbon-fiber gas diffusion layers: Effect of through-plane saturation distribution, Int. J. Heat Mass Transf. 86 (2015) 319–333.

Garcia-Salaberri, Pablo↗
DOE OSTI2023

Robust Optimized Pulse Schemes for Atomic Fountain Interferometry

The robustness of an atomic fountain interferometer with respect to variations in the initial velocity of the atoms and deviations from the optimal pulse amplitude is examined. We numerically simulate the dynamics of an interferometer in momentum space with a maximum separation of and map out the expected signal contrast depending on the variance of the initial velocity distribution and the value of the laser field amplitude. We show that an excitation scheme based on rapid adiabatic passage significantly enhances the expected signal contrast, compared to the commonly used scheme consisting of a series of and pulses. We demonstrate further substantial increase of the robustness by using optimal control theory to identify splitting and swapping pulses that perform well on an ensemble average of pulse amplitudes and velocities. Our results demonstrate the ability of optimal control to significantly enhance future implementations of atomic fountain interferometry.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

An Assessment of the Influence of Uncertainty in Temporally Evolving Streamflow Forecasts on Riverine Inundation Modeling

Continental-scale river forecasting platforms forecast streamflow at reaches that can be used as boundary conditions to drive a local-scale flood inundation model. Uncertainty accumulated during this process stems not only from any part of the forecasting chain but can also be caused by the daily variations in weather forcing that keeps evolving as the event advances. This work aims to examine the influence of the evolving forecast streamflow on predicting the maximum inundation for extreme floods. A diagnostic case study was made on the basis of a hindcast of Hurricane Matthew striking the eastern U.S. in 2016. The U.S. National Water Model was one-way coupled to a hydrodynamic inundation model through a developed automated workflow. Although the river forcing has significantly mismatched hydrographs versus observations, the simulated peak water surface elevations and maximum extents were validated to be comparable with the observations, which indicates that the inundation model may not be sensitive to the inherited uncertainty from the weather forcing. Moreover, the uncertainty of the forecast streamflow time series caused only one order of magnitude fewer variations in inundation prediction; this dampening effect may become clearer for extreme events with large areas inundated. In addition, the forecast total volume of stream discharge appears to be an important metric for assessing the performance of river forcing for inundation mapping, as a linear correlation between the total volume and the accuracy of the predicted peak water surface elevation and maximum extent was found, with the coefficients of determination all above 0.8. Extra best-practice experience of running similar operational tasks demonstrated the tradeoff between the cost and accuracy gain.

flood, national water model, RIFT↗
DOE OSTI2024

Quantitative assessment of selenium diffusion and passivation in CdSeTe solar cells probed by spatially resolved cathodoluminescence

The introduction of selenium in CdSeTe/CdTe solar cells has led to improved device performances attributed to the passivation of bulk defects. In this work, high-resolution cathodoluminescence experiments are performed on a series of CdSeTe/CdTe thin films with different Se concentrations to quantify the mechanisms and the passivation role of Se. We demonstrate a universal dependence between the Se concentration and the radiative efficiency and a ten-fold enhancement of the luminescence between CdTe and CdSe 0.4 Te 0.6 . Raw luminescence maps are converted into maps of the Se concentration, revealing its graded profile within the stack. We demonstrate the diffusion of Se along CdTe grain boundaries induced by the cadmium chloride annealing treatment and determine the diffusion coefficients, which are more than eight times higher at grain boundaries than in grain interiors. These results provide microscopic insights into the distribution of Se and its impact on the passivation of CdSeTe/CdTe solar cells.

14 SOLAR ENERGY↗