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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Substantial and overlooked greenhouse gas emissions from deep Arctic lake sediment - supporting data and code

This data package contains data, descriptions, and code-based analyses that were used to support conclusions drawn in “Substantial and overlooked greenhouse gas emissions from deep Arctic lake sediment”, by Freitas et al. (2025) (https://doi.org/10.1038/s41561-024-01614-y). The study evaluated greenhouse gas production along a deep sediment core (20 m) taken in 2018 from below Goldstream Lake, a field site approximately 15 km north of Fairbanks, Alaska.The file “ESSDive_NFreitas_2024_flmd.csv” includes an overview of all other csv files in this data package, namely: sediment descriptions (depth and type of sediment), sediment characterizations (bulk density, gravimetric water content, total carbon, etc.), calculated respiration and temperature sensitivity values associated with year-long incubations of the sediment core, and the R code used to process the dataset. Additional details regarding the content of these files and how the calculations were performed are described in the Methods section of this archive. The “data_dictionary_ESSDive_NFreitas_2024_dd.csv” is a data dictionary for all files included in the data package. Each row in the data dictionary represents a column name in a given file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).Data package updates- August 2024: Title was updated, R code now includes a process for calculating potential production across the whole sediment column, the "sediment_column_production_potentials_ESSDive_NFreitas_2024.csv" is the associated data output file, and the file level metadata and data dictionaries were updated to reflect the contents of the additional csv file.- October 2024: Title was updated, and a statement was added to Step 2 of the Methods that describes that samples were collected (and exported) in a responsible manner and in accordance with relevant permits and local laws.- January 2025: Updated the associated manuscript details in the Abstract (manuscript title, publication date, DOI link) and in the Related References section (full citation).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mineralogy of floodplain sediments from Meanders C, O, and Z in the East River Watershed, CO, USA

This dataset includes bulk X-ray diffraction data from floodplain sediments collected as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the role of biogeochemical cycling and other river corridor processes on riverine export of solutes. Sediment cores were collected from Meander C, Meander O, and Meander Z in July 2016 to September 2017 to depths of approximately 40-95 cm. Sample metadata including locations, depths, and sample dates are included in a csv file ("sample_list_and_locations.csv"). The file "diffraction_data.csv" contains raw diffraction data, and mineral quantification is in the file "mineral_abundance.csv". This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

In-Pile Instrumentation (I2) (2018 Report)

Energy demand is growing exponentially, renewing interest in nuclear technology as a reliable, carbon-free energy source. In alignment with the U.S. Department of Energy (DOE), Idaho National Laboratory’s (INL’s) primary mission is to discover, demonstrate and secure innovative nuclear energy solutions. The capability to monitor the conditions inside nuclear reactors core is considered essential to this development process. To enable such capability, the InPile Instrumentation (I2) program was conceived in 2017 as an additional element to DOE Crosscutting Technology Development activities under the Nuclear Energy Enabling Technology (NEET) program. This document reports on the first year of implementation of research activities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Integrated Distribution Planning

The contemporary distribution planning landscape is comprised of an increasing number of factors that require integration into the engineering of the modern electric grid. Expectations for electric utilities to accommodate heightened awareness of stakeholders' interest in things like decarbonization, resilience and equity are growing. As these interests are formed into objectives, many jurisdictions will experience increasing levels of load modifying technologies like DER, building and industrial electrification and electric vehicles which prove not only to challenge the capabilities of the grid; but the processes by which planning for it is traditionally done. Other related factors that strain the conventional distribution planning mold are the swelling amount and sources of data associated with these technologies and the need it creates for improved capabilities in the processes and tools that manage it. As the complexity of the distribution system expands, so will the distribution system's effects on the transmission and generation systems that it is a part of. Forecasting distribution system load and DER are examples of areas where this complexity will manifest, and harmonizing distribution forecasting with transmission and generation forecasting requires higher amounts of intentionality as these typically separate processes become a solitary one. Of course, core activities do not cease as a utility begins to integrate these other factors, and in this webinar we explore specifics of how distribution planning can be expected to evolve as progress towards Integrated Distribution System Planning is made.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Methane, ethane, and propane production in Greenland ice core samples and a first isotopic characterization of excess methane

Air trapped in polar ice provides unique records of the past atmospheric composition ranging from key greenhouse gases such as methane (CH 4 ) to short-lived trace gases like ethane (C 2 H 6 ) and propane (C 3 H 8 ). Recently, the comparison of CH 4 records obtained using different extraction methods revealed disagreements in the CH 4 concentration for the last glacial in Greenland ice. Elevated methane levels were detected in dust-rich ice core sections measured discretely, pointing to a process sensitive to the melt extraction technique. To shed light on the underlying mechanism, we performed targeted experiments and analyzed samples for methane and the short-chain alkanes ethane and propane covering the time interval from 12 to 42 kyr. Here, we report our findings of these elevated alkane concentrations, which scale linearly with the amount of mineral dust within the ice samples. The alkane production happens during the melt extraction step of the classic wet-extraction technique and reaches 14 to 91 ppb of CH 4 excess in dusty ice samples. We document for the first time a co-production of excess methane, ethane, and propane, with the observed concentrations for ethane and propane exceeding their past atmospheric background at least by a factor of 10. Independent of the produced amounts, excess alkanes were produced in a fixed molar ratio of approximately 14 : 2 : 1, indicating a shared origin. The measured carbon isotopic signature of excess methane is (-47.0 ± 2.9) ‰ and its deuterium isotopic signature is (-326 ± 57) ‰. With the co-production ratios of excess alkanes and the isotopic composition of excess methane we established a fingerprint that allows us to constrain potential formation processes. This fingerprint is not in line with a microbial origin. Moreover, an adsorption–desorption process of thermogenic gas on dust particles transported to Greenland does not appear very likely. Instead, the alkane pattern appears to be indicative of abiotic decomposition of organic matter as found in soils and plant leaves.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

On the Feasibility of Using Reduced-Precision Tensor Core Operations for Graph Analytics

Today’s data-driven analytics and machine learning workload have been largely driven by the General-PurposeGraphics Processing Units (GPGPUs). To accelerate dense matrix multiplications on the GPUs, Tensor Core Units (TCUs) have been introduced in recent years. In this paper, we study linear-algebra-based and vertex-centric algorithms for various graph kernels on the GPUs with an objective of applying this new hardware feature to graph applications. We identify the potential stages in these graph kernels that can be executed on the Tensor Core Units. In particular, we leverage the reformulation of the reduction and scan operations in terms of matrix multiplication [1]on the TCUs. We demonstrate that executing these operations on the TCUs, available inside different graph kernels, can assist in establishing an end-to-end pipeline on the GPGPUs without depending on hand-tuned external libraries and still can deliver comparable performance for various graph analytics.

Graph algorithms, GPU computing↗
DOE OSTI2025

Colloidal quantum dots for optoelectronics

Colloidal quantum dots (QDs) are semiconductor nanocrystals that have unique size-tunable optoelectronic properties and are suitable for wet processing. QD research aims to answer fundamental questions about the chemical and physical properties of nanoscale materials and use these tools for technological applications ranging from bio-imaging to quantum optics. At the core of this field is a set of synthetic, processing and analytical methods designed to produce QDs in uniform ensembles that meet the highest performance standards. Here, this Primer reviews QD fabrication methods with a focus on the applications of QDs in printed optoelectronics and quantum optics. After outlining the current state-of-the-art QD syntheses, the experimental and computational analysis of QDs is discussed. These topics are then connected to the methodologies, processes and concepts required for developing QD-based photodetectors, light-emitting devices and quantum optics applications. Special attention is paid to challenges in reproducibility and current limitations of the field, such as the need to balance non-restricted material composition with high performing technology while achieving long-term stability in QD devices under operating conditions. Finally, the ongoing advancement in QD synthesis, precise atomic-level analysis and computational methodologies are highlighted as key drivers towards rational QD design, particularly in understanding how structural changes under loading impact QD properties.

optical materials↗
DOE OSTI2020

Improving Time Step Convergence in an Atmosphere Model With Simplified Physics: The Impacts of Closure Assumption and Process Coupling

Convergence testing is a common practice in the development of dynamical cores of atmospheric models but is not as often exercised for the parameterization of sub-grid physics. An earlier study revealed that the stratiform cloud parameterizations in several predecessors of the Energy Exascale Earth System Model (E3SM) showed strong time-step sensitivity and slower-than-expected convergence when the model's time step was systematically refined. In this work, a simplified atmosphere model is configured that consists of the spectral-element dynamical core of the E3SM atmosphere model coupled with a large-scale condensation parameterization based on commonly used assumptions. This simplified model also resembles E3SM and its predecessors in the numerical implementation of process coupling and shows poor time-step convergence in short ensemble tests. We present a formal error analysis to reveal the expected time-step convergence rate and the conditions for obtaining such convergence. Numerical experiments are conducted to investigate the root causes of convergence problems. We show that revisions in the process coupling and closure assumption help to improve convergence in short simulations using the simplified model; the same revisions applied to a full atmosphere model lead to significant changes in the simulated long-term climate. This work demonstrates that causes of convergence issues in atmospheric simulations can be understood by combining analyses from physical and mathematical perspectives. Addressing convergence issues can help to obtain a discrete model that is more consistent with the intended representation of the physical phenomena.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Scalable Pattern Matching in Metadata Graphs via Constraint Checking

Pattern matching is a fundamental tool for answering complex graph queries. Unfortunately, existing solutions have limited capabilities: They do not scale to process large graphs and/or support only a restricted set of search templates or usage scenarios. Moreover, the algorithms at the core of the existing techniques are not suitable for today’s graph processing infrastructures relying on horizontal scalability and shared-nothing clusters, as most of these algorithms are inherently sequential and difficult to parallelize. In this article we present an algorithmic pipeline that bases pattern matching on constraint checking. The key intuition is that each vertex and edge participating in a match has to meet a set of constraints implicitly specified by the search template. These constraints can be verified independently and typically are less expensive to compute than searching the full template. The pipeline we propose generates these constraints and iterates over them to eliminate all the vertices and edges that do not participate in any match, thus reducing the background graph to a subgraph that is the union of all template matches—the complete set of all vertices and edges that participate in at least one match. Additional analysis can be performed on this annotated, reduced graph, such as full match enumeration, match counting, or computing vertex/edge centrality. Furthermore, a vertex-centric formulation for constraint checking algorithms exists, and this makes it possible to harness existing high-performance, vertex-centric graph processing frameworks. This technique (i) enables highly scalable pattern matching in metadata (labeled) graphs; (ii) supports arbitrary patterns with 100% precision; (iii) enables tradeoffs between precision and time-to-solution, while always selects all vertices and edges that participate in matches, thus offering 100% recall; and (iv) supports a set of popular data analytics scenarios. We implement our approach on top of HavoqGT, an open-source asynchronous graph processing framework, and demonstrate its advantages through strong and weak scaling experiments on massive scale real-world (up to 257 billion edges) and synthetic (up to 4.4 trillion edges) labeled graphs, respectively, and at scales (1,024 nodes / 36,864 cores), orders of magnitude larger than used in the past for similar problems. This article serves two purposes: First, it synthesises the knowledge accumulated during a long-term project. Second, it presents new system features, usage scenarios, optimizations, and comparisons with related work that strengthen the confidence that pattern matching based on iterative pruning via constraint checking is an effective and scalable approach in practice. The new contributions include the following: (i) We demonstrate the ability of the constraint checking approach to efficiently support two additional search scenarios that often emerge in practice, interactive incremental search and exploratory search. (ii) We empirically compare our solution with two additional state-of-the-art systems, Arabsque and TriAD. (iii) We show the ability of our solution to accommodate a more diverse range of datasets with varying properties, e.g., scale, skewness, label distribution, and match frequency. (iv) We introduce or extend a number of system features (e.g., work aggregation, load balancing, and the ability to cap the generated traffic) and design optimizations and demonstrate their advantages with respect to improving performance and scalability. (v) We present bottleneck analysis and insights into artifacts that influence performance. (vi) We present a theoretical complexity argument that motivates the performance gains we observe.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Connecting suborganismal data to bioenergetic processes: killifish embryos exposed to a dioxin-like compound

A core challenge for ecological risk assessment is to integrate molecular responses into a chain of causality to organismal or population level outcomes. Bioenergetic theory may be a useful approach for integrating suborganismal responses to predict organismal level responses that influence population dynamics. In this work, we describe a novel application of Dynamic Energy Budget (DEB), theory in the context of a toxicity framework (Adverse Outcome Pathways, AOP) to make quantitative predictions of chemical exposures to individuals, starting from suborganismal data. We use early life stage exposure of Fundulus heteroclitus to dioxin-like chemicals (DLCs) and connect AOP Key Events (KEs) to DEB processes through “damage” that is produced at a rate proportional to the internal toxicant concentration. We use transcriptomic data of fish embryos exposed to DLCs to translate molecular indicators of damage into changes in DEB parameters (damage increases somatic maintenance costs) and use DEB models to predict sublethal and lethal effects of young fish. By changing a small subset of model parameters, we predict the evolved tolerance to DLCs in some wild F. heteroclitus populations, a data set not used in model parameterization. The differences in model parameters points to reduced sensitivity and altered damage repair dynamics as contributing to this evolved resistance. Our methodology has potential extrapolation to untested chemicals of ecological concern.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Atomic-scale in situ observation of electron beam and heat induced crystallization of Ge nanoparticles and transformation of Ag@Ge core-shell nanocrystals

Crystallization of amorphous materials by thermal annealing has been investigated for numerous applications in the fields of nanotechnology, such as thin-film transistors and thermoelectric devices. The phase transition and shape evolution of amorphous germanium (Ge) and Ag@Ge core–shell nanoparticles with average diameters of 10 and 12 nm, respectively, were investigated by high-energy electron beam irradiation and in situ heating within a transmission electron microscope. The transition of a single Ge amorphous nanoparticle to the crystalline diamond cubic structure at the atomic scale was clearly demonstrated. Depending on the heating temperature, a hollow Ge structure can be maintained or transformed into a solid Ge nanocrystal through a diffusive process during the amorphous to crystalline phase transition. Selected area diffraction patterns were obtained to confirm the crystallization process. In addition, the thermal stability of Ag@Ge core–shell nanoparticles with an average core of 7.4 and a 2.1 nm Ge shell was studied by applying the same beam conditions and temperatures. The results show that at a moderate temperature (e.g., 385 °C), the amorphous Ge shell can completely crystallize while maintaining the well-defined core–shell structure, while at a high temperature (e.g., 545 °C), the high thermal energy enables a freely diffusive process of both Ag and Ge atoms on the carbon support film and leads to transformation into a phase segregated Ag–Ge Janus nanoparticle with a clear interface between the Ag and Ge domains. In conclusion, this study provides a protocol as well as insight into the thermal stability and strain relief mechanism of complex nanostructures at the single nanoparticle level with atomic resolution.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The CORDEX-CORE EXP-I Initiative: Description and Highlight Results from the Initial Analysis

Here we describe the first effort within the Coordinated Regional Climate Downscaling Experiment–Coordinated Output for Regional Evaluation, or CORDEX-CORE EXP-I. It consists of a set of twenty-first-century projections with two regional climate models (RCMs) downscaling three global climate model (GCM) simulations from the CMIP5 program, for two greenhouse gas concentration pathways (RCP8.5 and RCP2.6), over nine CORDEX domains at ~25-km grid spacing. Illustrative examples from the initial analysis of this ensemble are presented, covering a wide range of topics, such as added value of RCM nesting, extreme indices, tropical and extratropical storms, monsoons, ENSO, severe storm environments, emergence of change signals, and energy production. They show that the CORDEX-CORE EXP-I ensemble can provide downscaled information of unprecedented comprehensiveness to increase understanding of processes relevant for regional climate change and impacts, and to assess the added value of RCMs. The CORDEX-CORE EXP-I dataset, which will be incrementally augmented with new simulations, is intended to be a public resource available to the scientific and end-user communities for application to process studies, impacts on different socioeconomic sectors, and climate service activities. The future of the CORDEX-CORE initiative is also discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

How macroscopic limits on neutron-star baryon loss yield microscopic limits on non-standard-model baryon decay

We investigate how our baryon-loss limits from anomalous binary-pulsar period lengthening can be interpreted microscopically to yield specific constraints on the particle physics of baryon number violation within a neutron star. We focus on the possibility of anomalous baryon disappearance via dark baryon processes and on scenarios in which the produced dark-sector particles do not survive to influence the response of the star to baryon-number-violating effects. We flesh out the conditions for which this may occur, as well as other key assumptions. We then turn to the analysis of particle processes in the dense nuclear medium found at the core of a neutron star, employing the techniques of relativistic mean-field theory. Using our study of in-medium effects and limits on macroscopic baryon number violation, we extract limits on in-vacuum baryon-number-violating processes, and we determine them for various equations of state. We conclude by noting the implications of our results for models of dark-sector-enabled baryogenesis. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

xSDK: Building an ecosystem of highly efficient math libraries for exascale

Current efforts to build increasingly powerful computer architectures are opening up new avenues for more complex and higher fidelity simulations coupled with data analytics and learning, leading to new scientific insights and deeper understanding. At one extreme, exascale computers will be much faster than previous computer generations (performing 10 18 operations per second—that is, 1,000 times faster than petascale). To achieve these performance improvements, computer architectures are becoming increasingly complex, with deep memory hierarchies, very high node and core counts, and heterogeneous features such as graphics processing units (GPUs). Such architectural changes impact the full breadth of computing scales, as heterogeneity pervades even current-generation laptops, workstations, and moderate-sized clusters. While emerging advanced architectures provide unprecedented opportunities, they also present significant challenges for developers of scientific applications, such as multiphysics and multiscale codes, who must adapt their software to handle disruptive changes in architectures and new programming models that have not yet stabilized. Developers must consider increasing concurrency while reducing communication and synchronization, and other complexities such as the potential for using mixed precision to leverage the compute power available in low-precision tensor cores. On one hand, developers must implement new scientific capabilities, which in turn increase code complexity. On the other hand, the codes must be ported to new architectures, requiring the inclusion of new programming models and the restructuring of code to achieve good performance. Addressing these issues is beyond the capability of any single person or team—leading to the need for collaboration among many teams, who encapsulate their expertise in reusable software and work together to create sustainable software ecosystems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A time-dependent momentum-resolved scattering approach to core-level spectroscopies

While new light sources allow for unprecedented resolution in experiments with X-rays, a theoretical understanding of the scattering cross-section is lacking. In the particular case of strongly correlated electron systems, numerical techniques are quite limited, since conventional approaches rely on calculating a response function (Kramers-Heisenberg formula) that is obtained from a perturbative analysis of scattering processes in the frequency domain. This requires a knowledge of a full set of eigenstates in order to account for all intermediate processes away from equilibrium, limiting the applicability to small tractable systems. In this work, we present an alternative paradigm, recasting the problem in the time domain and explicitly solving the time-dependent Schrödinger equation without the limitations of perturbation theory: a faithful simulation of the scattering processes taking place in actual experiments, including photons and core electrons. We show how this approach can yield the full time and momentum resolved Resonant Inelastic X-Ray Scattering (RIXS) spectrum of strongly interacting many-body systems. We demonstrate the formalism with an application to Mott insulating Hubbard chains using the time-dependent density matrix renormalization group method, which does not require a priory knowledge of the eigenstates and can solve very large systems with dozens of orbitals. This approach can readily be applied to systems out of equilibrium without modification and generalized to other spectroscopies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

BETO 2021 Peer Review - Overview of the Chemical Catalysis for Bioenergy Consortium

Catalysis plays a central role in converting biomass and carbon-rich waste feedstocks into fuels and chemicals; however, critical catalysis challenges exist that are limiting commercialization of emerging bioenergy technologies. By leveraging unique U.S. Department of Energy National Laboratory capabilities and expertise, the Chemical Catalysis for Bioenergy consortium seeks to overcome these catalysis challenges and accelerate the catalyst and process development cycle. The foundation of the consortium consists of an integrated and collaborative portfolio of catalytic technologies and enabling capabilities, which positions ChemCatBio to address both technology-specific and overarching catalysis challenges across the development cycle from discovery to scale-up. The core catalysis projects target technological advancements for specific conversion processes, such as catalytic upgrading of biochemical process intermediates, catalytic fast pyrolysis, C1 and C2 upgrading, and electrochemical CO2 reduction, while the enabling technologies provide access to world-class capabilities and expertise in computational modeling, materials synthesis, advanced in situ and in operando catalyst characterization, and catalyst design tools.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Comparative modeling of the disregistry and Peierls stress for dissociated edge and screw dislocations in Al

Many elementary deformation processes in metals involve the motion of dislocations. The planes of glide and specific processes dislocations prefer depend heavily on their atomic core structures. Atomistic simulations are desirable for dislocation modeling but their application to even sub-micron scale problems is in general computationally costly. Accordingly, continuum-based approaches, such as the phase-field microelasticity, phase-field dislocation dynamics (PFDD), generalized Peierls–Nabarro (GPN) models, and the concurrent atomistic–continuum (CAC) method, have attracted increasing attention in the field of dislocation modeling because they well represent both short-range cores interactions and long-range stress fields of dislocations. To better understand their similarities and differences, it is useful to compare these methods in the context of benchmark simulations and predictions. In this paper, we apply the CAC method and different PFDD variants – one of them is equivalent to a GPN model – to simulate an extended (i.e., dissociated) dislocation in Al with initially pure edge or pure screw character in terms of the disregistry. CAC and discrete forms of PFDD are also employed to calculate the Peierls stress. By conducting comprehensive convergence studies, we quantify the dependence of these measures on time/grid resolution and simulation cell size. Several important but often overlooked differences between PFDD/GPN variants are clarified. In conclusion, our work sheds light on the advantages and limitations of each method, as well as the path towards enabling them to effectively model complex dislocation processes at larger length scales.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Spatially Resolved Evaluation of Accelerated Environmental Aging on Emerging Polypropylene-Based Photovoltaic Backsheets Using Raman Spectroscopy

For this work, accelerated aging was used to assess environmental degradation in emerging co-extruded polypropylene (PP)-based backsheets under three different environmental conditions (65°C/20% relative humidity (RH), 75°C/20% RH, and 75°C/50% RH). Although differential scanning calorimetry did not measure crystallinity changes with exposure, spatially resolved Raman spectroscopy identified crystallinity increases in the core layer of aged samples, indicating a heterogeneous postcrystallization process. The Raman results were in agreement with synchrotron-based microfocused wide-angle X-ray scattering measurements. Cross-sectional nanoindentation was used to correlate localized crystallinity shifts with changes in Young's modulus. A similar trend was found where increased modulus was measured in the core layer, supporting the relationship between modulus and crystallinity. Finally, dielectric characterization was used to assess the impact of these material property changes on performance. While changes in the backsheet material properties and dielectric performance were observed with accelerated aging, these shifts generally equilibrated with time, indicating overall stability in response to environmental stressors. Additionally, the identified heterogeneous material property changes indicate that spatially resolved crystallinity measurements may be a valuable early failure indicator to be used in the assessment of PV backsheet long-term durability.

36 MATERIALS SCIENCE↗