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KPFM and SSRM, which relate to the following characterization reviews, will be introduced briefly in Section 10.2. Some recent nm-scale characterizations of perovskite, CIGS, and CdTe thin-film solar cell materials and devices will be reviewed in Sections 10.3-10.5 and closing remarks will be given in Section 10.6.
This chapter describes some photoluminescence (PL) approaches to characterize thin film solar cells with emphasis on time-resolved methods. Spectral PL analysis is complementary and was recently reviewed. Since TRPL is not very commonly used in PV characterization, we consider interface and bulk recombination in the test structures and in devices, briefly describe charge-carrier transport and recombination microscopy, and finally, summarize and compare some CdTe, CdSeTe, CIGS, kesterite, and perovskite EO characteristics.
The use of atmospheric pressure plasma to enhance catalytic chemical reactions involves complex surface processes induced by the interactions of plasma-generated fluxes with catalyst surfaces. Industrial implementation of plasma catalysis necessitates optimizing the design and realization of plasma catalytic reactors that enable chemical reactions that are superior to conventional thermal catalysis approaches. This requires the fundamental understanding of essential plasma-surface interaction mechanisms of plasma catalysis from the aspect of experimental investigation and theoretical analysis or computational modeling. In addition, experimental results are essential to validate the relative theoretical models and hypotheses of plasma catalysis that was rarely understood so far, compared to conventional thermal catalysis. This overview focuses on two important application areas, nitrogen fixation and methane reforming, and presents a comparison of important aspects of the state of knowledge of these applications when performed using either plasma-catalysis or conventional thermal catalysis. We discuss the potential advantage of plasma catalysis over thermal catalysis from the aspects of plasma induced synergistic effect and in situ catalyst regeneration. In-situ/operando surface characterization of catalysts in plasma catalytic reactors is a significant challenge since the high pressure of realistic plasma catalysis systems preclude the application of many standard surface characterization techniques that operate in a low-pressure environment. Here, we present a review of the status of experimental approaches to probe gas-surface interaction mechanisms of plasma catalysis, including an appraisal of demonstrated approaches for integrating surface diagnostic tools into plasma catalytic reactors.
Oxidation of metals can provide a functional oxide film or an oxide layer that can lead to material degradation causing performance limiting concerns. Because of the technological and economic impacts of oxidation, understanding early-stage oxidation growth and its mechanisms is of high importance. Unfortunately, poor empirical data during early-stage oxidation has left our current understanding of oxidation incomplete and woefully inadequate. Measurement techniques previously used for oxidation kinetics characterization include microbalance, x-ray diffraction, Fourier-transform infrared spectroscopy, and spectroscopic ellipsometry. However, these techniques lack the necessary time-resolution or vertical resolution in addition to not providing any spatial data, which is essential to early-stage oxidation studies to understand both oxidation kinetics and mechanisms. Here, a novel rapid and surface-sensitive data collection technique that provides three-dimensional spatial data of a growing oxide layer with sub-nanometer thickness resolution using white-light interferometry has been developed. This technique will be the first technique capable of providing real-time spatial results of early-stage oxidation growth. The effectiveness of this characterization tool is shown by observing the early-stage oxidation kinetics and film cracking of uranium heated to 90 °C under 500 torr of pure O 2 . Initially a parabolic oxidation growth is observed. Once the oxide layer reaches ≈ 30 nm, the epitaxial stresses between the uranium substrate and oxide film layer causes localized cracking in addition to the observed transition of the parabolic growth rate to a linear growth rate. The results of the white-light interferometry are verified by glancing incidence x-ray diffraction.
There is significant uncertainty in how global water supply will evolve in the future, due to uncertain climate, socioeconomic, and land use change drivers and variability of hydrologic processes. It is critical to characterize the potential impacts of uncertainty in future water supply given its importance for food and energy production. In this work, we introduce a framework that integrates stochastic hydrology and human-environmental systems to characterize uncertainty in future water supply and its multisector impacts. We develop a global stochastic watershed model and demonstrate that this model can generate a large ensemble of realizations of basin-scale runoff with global coverage that preserves the mean, variance, and spatial correlation of a historical benchmark. We couple this model with a well-known human-environmental systems model to explore the impacts of runoff variability on the water and agricultural sectors across spatial scales. We find that the impacts of future hydrologic variability vary across sectors and regions. Impacts are felt most strongly in the water and agricultural sectors for basins that are expected to have unsustainable water use in the future, such as the Indus River basin. For this basin, we find that the variability in future irrigation water withdrawals and irrigated cropland increase over time due to uncertainty in renewable water supply. We also use the Indus basin to show how our stochastic ensemble can be leveraged to explore the global multisector consequences of local extreme runoff conditions. This work introduces a novel technique to explore the propagation of future hydrologic variability across human and natural systems and spatial scales.
Establishing a solid taxonomic framework is crucial for enabling discovery and documentation efforts. This ensures effective communication between scientists as well as reproducibility of results between laboratories, and facilitates the exchange and preservation of biological material. Such framework can only be achieved by establishing clear criteria for taxa characterization and rank assignment. Within the anaerobic fungi (phylum Neocallimastigomycota), the need for such criteria is especially vital. Difficulties associated with their isolation, maintenance and long-term storage often result in limited availability and loss of previously described taxa. To this end, we provide here a list of morphological, microscopic, phylogenetic and phenotypic criteria for assessment and documentation when characterizing newly obtained Neocallimastigomycota isolates. We also recommend a polyphasic rank-assignment scheme for novel genus-, species- and strain-level designations for newly obtained Neocallimastigomycota isolates.
The development of a Pb-shielded fixture for the execution of a small-angle neutron scattering (SANS)-based workflow for interrogation of highly irradiated nuclear materials has been explored. The Pb shielding was specially designed to reduce the detected radioactivity from the specimen during SANS experiments, and the overall configuration is termed shielded magnetic SANS (SM-SANS). Two FeCrAl-based alloys, C35M and 125YF, were examined with the SM-SANS technique using a free-form size distribution locally monodisperse model in both the as-received and irradiated states. Quantitative values derived from the free-form size distribution were compared with atom probe tomography experiments. Microstructural and compositional parameters determined using the two characterization techniques were complements of each other. The results demonstrate that the SM-SANS technique is an effective means of characterizing nanoscale clustering in irradiated material systems and provides new avenues for investigating radioactive material microstructures.
As the lifetime of photovoltaic modules increases toward the goal of 50 years, accelerated stress testing is critical to assessing the viability of newer, improved, and often cheaper materials in the field. However, the validation of accelerated testing to reproduce field failure has remained elusive. The recent developments of more advanced stress testing protocols utilizing sequential and combined stressors have provided another opportunity for validation. Using a suite of mechanical, chemical, and structural characterization methods we report the development of our approach using a known bad backsheet “AAA.” We then apply this approach to PVDF-based backsheets to further confirm the generalizability of this approach. The outcome of this work is two-fold: (1) validation of advanced accelerated testing protocols which will enable the prediction of field failures in new materials, and (2) deeper insights into the degradation mechanisms observed through the extensive characterization allowing for improved materials engineering and development.
In this work, spatially resolved characterization methods are used to identify loss mechanisms for common p -type silicon solar cell architectures, including multicrystalline aluminum back surface field (Al-BSF), monocrystalline Al-BSF, monocrystalline passivated emitter and rear cells (PERC), and bifacial monocrystalline PERC. The characterization methods used in this work include suns-V OC , photoluminescence imaging, and spatially resolved external quantum efficiency and reflectance measurements. The optical and recombination losses are driven by the material properties, cell processing conditions, and device architecture. These losses are quantified and categorized in terms of underlying mechanisms (e.g., front reflectance, escape reflectance, front recombination, and parasitic optical absorption and recombination in the bulk and rear). The ability to create images of these loss parameters can be used to gain more insight into the materials and manufacturing processes used to produce solar cells, and examples are given in this work to illustrate how these images can help reveal the origin of defects.
Various wind turbine blade components, such as shear webs and skins, commonly use fiber-reinforced composite sandwich structures with a core material like balsa or foam. During manufacturing, core gap defects may result from the misalignment of adjacent foam or balsa core sheets in the blade mold. It is important to understand the influence that core gaps have on the structural integrity of wind turbine blades and how to mitigate their influence. This research characterized the effects of core gap defects at the manufacturing and mechanical levels for both epoxy and next-generation thermoplastic composites. Common repair methods were assessed and compared. Multiple defect sizes were compared using temperature data gathered with thermocouples embedded during manufacturing to core gap defect characteristics obtained using image-mapping techniques, optical microscopy, and mechanical characterization by long beam flexure. Results showed that peak exothermic temperatures during curing were closely related to core gap size. The long beam flexure tests determined that transverse core gaps under pure bending loads can have a substantial effect on the ultimate facesheet strength of both epoxy and thermoplastic composite sandwich structures (up to 25% strength reduction), although the size of the defect itself had less of an influence on the magnitude of the strength reduction. The supporting image-mapping techniques indicated that the distortion of the composite facesheets by the core gaps contributed to the premature failures. The repair methods used in this study did very little to improve the ultimate strength of the sandwich panels that previously had core gap defects. The repair of the thermoplastic panel resulted in a further loss in ultimate facesheet strength. This research demonstrated that there is a vital need for the development of a compatible thermoplastic polymer repair resin system and appropriate resin specific repair procedures for the next generation of recyclable thermoplastic wind blades.
This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the U.S DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement project by Battelle [Columbus, OH], presented by Mark Kelley. The project's objective was to characterize stress in the Utah FORGE EGS reservoir using three methods: a laboratory rock-core stress estimation combined with a Machine Learning approach for estimation of in-situ stress from field sonic-log data, a field based in-situ measurement (min-frac) approach, and a modeling approach. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.
The package contains the data layers used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”. Spatial data layers include: topography, wetland vegetation cover, time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. The study aims to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region
Various technologies have traditionally been used to monitor and describe hydraulic fractures from different perspectives. This work demonstrates the value of data integration for hydraulic fracture characterization when multiple data resources are available. The Hydraulic Fracturing Test Site 2 (HFTS 2) is a hydraulic fracturing research project in the Delaware Basin with multiple surveillance techniques including fiber optics sensing, microseismic, pressure/temperature gauges, etc. We integrated the multidisciplinary data from the HFTS-2 to characterize hydraulic fractures. The integrated data revealed interesting fracture propagation features including layering, vertical propagation affected by pore pressure gradient, and different microseismic activities due to difference in-situ conditions. Furthermore, these findings can be insightful for understanding hydraulic fracture propagation. The comparison among multiple surveillance data also helps us to evaluate the roles of various surveillance technologies and provides us experience to make informative decisions depending on different monitoring objectives.
The project Geologic Characterization of the South Georgia Rift Basin for Source Proximal CO2 Storage is one of 9 site characterization projects that were implemented as part of ARRA (American Recovery and Reinvestment Act). Data from this project was used to improve resolution of data in NATCARB in the area of study. Data related to this study has already been incorporated in NATCARB Atlas. The South Carolina Research Foundation and partners evaluated the feasibility of CCS in the Jurassic/ Triassic (J / TR) saline formations of the buried Mesozoic South Georgia Rift (SGR) Basin that extends from South Carolina into Georgia. The J / TR sequence, based on preliminary assessment of limited geologic and geophysical data, appears to have both the appropriate areal extent and multiple horizons to permanently and safely store CO2 The presence of several igneous rock layers within the sequence may potentially provide adequate seals to prevent upward CO2 migration into the Coastal Plain aquifer systems. Approximately 81 kilometers of 2-D seismic reflection data were collected by Bay Geophysical, Inc. to explore a portion of the SGR located in southern Georgia. The 81 kilometers were divided into two lines approximately 40.5 kilometers each, with Line 1 intersecting Georgia well GGS 3457. Line 2 intersects Line 1 at the southern portion of Line 1 to maximize the extent of coverage away from GGS-3457 (a deep well drilled in the 1980s for oil and gas exploration). This well had a set of usable logs, including gamma and neutron logs that provided promising results related to CO2 storage. Results showed sandstone with porosity values greater than 10 percent and a thickness of 120 meters. The design of the seismic shot was to extrapolate information away from the well and to better define the extent of the SGR and the necessary reservoir and caprock for a successful CO2 injection. A numerical simulation model of CO2 Injection and migration was developed based on the geology log for the GGS-3457 well. The simulation model was used to investigate the feasibility of injecting 30 million metric tons of CO2 into SGR J / TA sediments and integrity of the diabase layers as seals to prevent CO2 migration.
The project Characterization for CO2 Storage from Coal-fired Power Facilities in the Black Warrior Basin of Alabama is one of 9 site characterization projects that were implemented as part of ARRA (American Recovery and Reinvestment Act). Data from this project was used to improve resolution of data in NATCARB in the area of study. Data related to this study has already been incorporated in NATCARB Atlas.
Estimating the distance from the hydraulic fracture tip to the monitor well can be useful for fracture characterization, well spacing optimization, and preventing parent-child well interference. A heart-shaped signal is referred to as the extensional precursor of a fracture hit recorded by crosswell strain measurements and can serve as a vital tool for such estimation. This study incorporates the 3D displacement discontinuity method (DDM) to understand the impact of fracture geometry and monitor well offset on the heart-shaped signal’s characteristics. Results from numerical simulation and analytical solutions reveal a strong linear correlation between the spatial extent of the heart-shaped signal and the fracture tip distance. This relationship was further developed to predict tip distance using field data from the Hydraulic Fracture Test Site 2 (HFTS2). A reasonable approximation result from field data further validates the methodology. In addition, it is worth noting that the estimation accuracy depends on the ratio between fracture dimension and tip distance. The findings of this study offer a novel approach for real-time monitoring and characterizing hydraulic fracture propagation, which can be further used for well spacing optimization in unconventional and enhanced geothermal system reservoir development, as well as caprock integrity monitoring for carbon sequestration projects.
The Midwest Regional Carbon Sequestration Partnership (MRCSP) was founded in 2003 as part of the U.S. Department of Energy’s (DOE’s) Regional Carbon Sequestration Partnership initiative. Since its founding, MRCSP has made significant strides toward making CCUS a viable option for states in the region. The public/private consortium, funded through the DOE Regional Carbon Sequestration Initiative, brings together nearly 40 industry partners and 10 states. Battelle, as the project lead, oversees research, development and operations and coordinates activities among the partners. The incremental, phased approach has built a valuable knowledge base for the industry and paved the way for commercial-scale adoption of CCUS technologies. From 2008 to 2020, MRCSP Phase III focused on the development of large-scale injection projects. This report is part of a series of reports prepared under the Midwestern Regional Carbon Sequestration Partnership (MRCSP) Phase III (Development Phase). These reports summarize and detail the findings of the work conducted under the Phase III project. This report describes the efforts to drill and complete a well in the St. Peter sandstone for characterization and evaluation of storage potential in norther Michigan. However, as presented in this report, the well was not drilled and completed up to the intended target intervals due to drilling related difficulties. While the drilling issues were being resolved, the new Class VI injection well regulations were finalized by the US EPA. MRCSP efforts focused on evaluating CO 2 storage and containment using site characterization, modeling, and monitoring in conjunction with the ongoing CO 2 -EOR operations in the same area. This report describes work associated with the initial test well, the State Chester and MRCSP Well No. 1, drilled to a depth of about 7,968 feet TVD, and subsequently plugged and designated as temporarily abandoned. A high-level overview of data collected and analyzed is provided for shallower units.
Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations is a credible material model. Our ultimate vision is to interlace material characterization and model calibration in a real-time feedback loop, where the current model calibration results will drive the experiment to load regimes that add the most useful information to reduce parameter uncertainty. The current work investigated one key step to this Interlaced Characterization and Calibration (ICC) paradigm, using a finite load-path tree to incorporate history/path dependency of nonlinear material models into a network of surrogate models that replace computationally-expensive finite-element analyses. Our reference simulation was an elastoplastic material point subject to biaxial deformation with a Hill anisotropic yield criterion. Training data was generated using either a space-filling or adaptive sampling method, and surrogates were built using either Gaussian process or polynomial chaos expansion methods. Surrogate error was evaluated to be on the order of 10 ⁻5 and 10 ⁻3 percent for the space-filling and adaptive sampling training data, respectively. Direct Bayesian inference was performed with the surrogate network and with the reference material point simulator, and results agreed to within 3 significant figures for the mean parameter values, with a reduction in computational cost over 5 orders of magnitude. These results bought down risk regarding the surrogate network and facilitated a successful FY22-24 full LDRD proposal to research and develop the complete ICC paradigm.