Mobile In-Situ Imaging of Photovoltaic Modules
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The perovskite compound CsPbBr 3 has recently been discovered as a promising room-temperature semiconductor radiation detector, offering an inexpensive and easy-to-manufacture alternative to the current benchmark material Cd 1–x Zn x Te (CZT). The performance of CsPbBr 3 sensors is evaluated under harsh conditions, such as high radiation doses often found in industrial settings and extreme radiation in space. Results show minimal degradation in detector performance after exposure to 1 Mrad of Co-60 gamma radiation, with no significant change to energy resolution or hole mobility and lifetime. Additionally, many of the devices are still functional after being exposed to a 10 Mrad dose over 3 days, and those that do not survive can still be refabricated into working detectors. Notably, these results suggest that the failure mode in these devices is likely related to the interface between the electrode and material and their reaction, or the electrode itself and not the material itself. Overall, the study suggests that CsPbBr 3 has high potential as a reliable and efficient radiation detector in various applications, including those involving extreme fluxes and energies of gamma-ray radiation.
Plasma acceleration has emerged as a promising technology for future particle accelerators, particularly linear colliders. Significant progress has been made in recent decades toward high-efficiency and high-quality acceleration of electrons in plasmas. However, this progress does not generalize to the acceleration of positrons, as plasmas are inherently charge asymmetric. Here, we present a comprehensive review of historical and current efforts to accelerate positrons using plasma wakefields. Proposed schemes that aim to increase energy efficiency and beam quality are summarized and quantitatively compared. A dimensionless metric that scales with the luminosity-per-beam power is introduced, indicating that positron-acceleration schemes are currently below the ultimate requirement for colliders. The primary issue is electron motion; the high mobility of plasma electrons compared to plasma ions, which leads to nonuniform accelerating and focusing fields that degrade the beam quality of the positron bunch, particularly for high efficiency acceleration. Finally, we discuss possible mitigation strategies and directions for future research.
The Institute for Clean Energy Technology (ICET) at Mississippi State University is exploring the utility of machine learning in augmenting its mobile radiation surveying platforms, which are currently being developed as means to survey depleted uranium contaminated areas in support of remediation and decommissioning efforts. Mobile survey platforms provide a means to efficiently scan large areas of interest while reducing human exposure to radiation and other hazards. The survey platforms can also be used for scanning for any gamma emitting isotope in addition to depleted uranium. The spectral data that the platforms collect may be data starved with relatively low counts and poorly defined spectral features depending on the speed of the platforms and scintillation detector selection. Such data-starved spectra are difficult to use for isotope identification, requiring advanced knowledge of the possible radionuclides that could be present and environmental factors that could attenuate signals or introduce background noise. These factors in combination with the volume of survey data increases the time it takes to perform analysis of survey data when the source type is unknown. There are a number of algorithms in the field of machine learning that can be used to classify data that would be challenging and time-consuming for a human to identify. Supervised machine learning algorithms train models based on extensive amounts of human-labeled training data. Once sufficiently trained, these models can be used to quickly make high-fidelity predictions on new data. Convolutional neural networks are machine learning algorithms that excel in learning representations of 'shapes'. They do this by taking numerical input data and convolving them with spatial feature detectors referred to as filters. These filters are incrementally adjusted to reduce the prediction error on the data during the backpropagation step of training. Discussed in this paper is the development of a convolutional neural network classifier (CNNC) that can utilize spectral survey data for source discrimination and isotope identification. Bench-top laboratory experiments data using LaBr{sub 3}(Ce) scintillation detectors were used to train and evaluate the performance of the developed CNNC. The CNNC is capable of discriminating a variety of gamma emitting source types, differentiating different forms of uranium (depleted vs. natural), and estimating the amount of uranium for a known geometry. The discussed CNNC may be useful in scenarios where survey systems are deployed in situations where hazardous radioactive material maybe present, but the type is unknown. When used in remediation applications the CNNC can be used to screen-out false positives, helping reduce remediation costs. (authors)
The imbalanced carrier mobility remains a bottleneck for performance breakthrough in even those organic solar cells (OSCs) with recorded power conversion efficiencies (PCEs). Herein, a counter electrode doping strategy is proposed to reshape the internal potential distribution, which targets to extract the low mobility carriers at far end. Device simulations reveal that the key of this strategy is to partially dope the active layer with a certain depth, therefore it strengthens the electric field for low mobility carriers near counter electrode region while avoids zeroing the electric field near collection electrode region. Taking advantage of these, PCE enhancements are obtained from 15.4% to 16.2% and from 16.9% to 18.0%, respectively, via cathode p-doping and anode n-doping. Extending its application from opaque to semitransparent devices, the PCE of dilute cell rises from 10.5% to 12.1%, with a high light utilization efficiency (LUE) of 3.5%. The findings provide practical solutions to the core device physical problem in OSCs.
A collaborative network of more than 75 Clean Cities coalitions boosts the country's economic vitality, energy security, and quality of life by advancing the deployment of affordable, efficient, and clean transportation fuels and technologies. Coalitions provide the technical expertise local decision makers and fleets need to understand and implement alternative and renewable fuels, electric vehicles, idle-reduction measures, fuel economy improvements, new mobility choices, and emerging transportation technologies.
This fact sheet provides an overview of the U.S. Department of Energy's (DOE's) Vehicle Technologies Office Clean Cities Coalition Network, which boosts the country's economic vitality, energy security, and quality of life by advancing the deployment of affordable, efficient, and clean transportation fuels and technologies. Coalitions provide the technical expertise local decision makers and fleets need to understand and implement alternative and renewable fuels, electric vehicles, idle-reduction measures, fuel economy improvements, new mobility choices, and emerging transportation technologies.
This study focuses on requirements and challenges pertaining to advanced transportable nuclear energy systems, which are currently in development and feature varying degrees of mobility. These reactors mostly use high-assay low-enriched uranium (HALEU) fuel, and only limited refueling is required during their lifecycles. Due to the smaller source terms, higher burnup fuel, and potentially higher thermal efficiency involved, the waste inventory will be less than that generated by conventional light-water reactors. However, the relevant regulatory requirements must be met when transporting HALEU fuel (whether fresh or used) inside the reactor vessel—mainly the fuel material transportation, siting, and physical protection requirements outlined in US Nuclear Regulatory Commission (NRC) regulations 10 CFR 71 and 73. Spent nuclear fuel (SNF), as well as irradiated fuel (used during any interval of reactor operation), contains highly radioactive materials that generate heat and necessitate special precautions during transportation. This study addresses the challenges that this incurs, and outlines potential solutions.
A collaborative network of more than 75 Clean Cities coalitions boosts the country's economic vitality, energy security, and quality of life by advancing the deployment of affordable, efficient, and clean transportation fuels and technologies. Coalitions provide the technical expertise local decision makers and fleets need to understand and implement alternative and renewable fuels, electric vehicles, idle-reduction measures, fuel economy improvements, new mobility choices, and emerging transportation technologies.
Abstract Root phenotyping provides critical information to plant breeders for developing varieties with improved drought tolerance, greater root biomass, and greater nutrient use efficiency. Phenotyping roots in the natural environment is important for understanding the effect of the soil environment on root genotypic expressions. The goal of this work was to design and test a field‐scale mobile low‐field magnetic resonance imaging (LF‐MRI) Rhizotron that produces actionable root phenotyping data. We demonstrated this novel technology for root visualization and quantification using a LF‐MRI Rhizotron operating at 47 mT with two soil types. The LF‐MRI Rhizotron weights 453 kg, with a height of 90 cm, a diameter of 28 cm and an imaging field of view of 28 cm × 28 cm. The unit was operated in a Belk clay (Entic Hapluderts) and Weswood silt loam (Udifluventic Halustepts) generating 2‐D and 3‐D image data sets. The 2‐D image data had a collection time of 16.5 min per image at an image resolution of 2.2 mm per pixel. The 3‐D data had a collection time of 13 h per image with a 2.2 × 2.2 × 2.2 mm voxel resolution. Low‐field magnetic resonance imaging worked well for visualizing roots in moderate to high clay soils, demonstrating the potential for this technology; however, the broad application of this platform is hampered due to the prohibitively long scanning time to obtain 3‐D images. By increasing the field strength, and therefore the signal‐to‐noise ratio, faster scan times can enable a more useful system for root phenotyping.
Automated/autonomous vehicle fleet operations within automated mobility districts have been studied over the past five years by the National Renewable Energy Laboratory, a US Department of Energy federally funded research and development center. This paper extends the analysis in this third phase of research underway to include considerations for electric vehicle operations and charging within a fleet of right-sized automated vehicles providing on-demand public mobility services. The current focus of research is on the operational complexities and associated challenges for automated/autonomous vehicles employing electric drivetrains and the resultant need for an efficient battery charging process while vehicles are operating in an "on-demand" mode of service. The blossoming of microtransit with shared-ride and point-to-point dispatching of each vehicle instills complex operations, with multiple mobility-on-demand transit operating sites being deployed, studied, and analyzed across North America. As a starting point, the authors' experience over the past 20 years with the analysis of automated transit network systems operating on and within dedicated and protected transitways provides initial insights into the system-level operational implications for maintaining a sufficient battery charge for a fleet of automated vehicles. Lessons learned through the prior analyses of automated transit network systems operating in on-demand service are identified, along with the capital cost implications for the requisite operating fleet size and charging station infrastructure for various approaches. These costs are summarized in juxtaposition with the benefits of realizing the higher goals of reducing environmental impacts and energy use within automated mobility districts as automated/autonomous vehicle technology matures. Finally, the discussion addresses key aspects of battery-electric propulsion for managed fleets in fully automated operation that will be studied as the third phase of research continues.
Abstract The superior size and power scaling potential of ferroelectric-gated Mott transistors makes them promising building blocks for developing energy-efficient memory and logic applications in the post-Moore’s Law era. The close to metallic carrier density in the Mott channel, however, imposes the bottleneck for achieving substantial field effect modulation via a solid-state gate. Previous studies have focused on optimizing the thickness, charge mobility, and carrier density of single-layer correlated channels, which have only led to moderate resistance switching at room temperature. Here, we report a record high nonvolatile resistance switching ratio of 38,440% at 300 K in a prototype Mott transistor consisting of a ferroelectric PbZr 0.2 Ti 0.8 O 3 gate and an R NiO 3 ( R : rare earth)/La 0.67 Sr 0.33 MnO 3 composite channel. The ultrathin La 0.67 Sr 0.33 MnO 3 buffer layer not only tailors the carrier density profile in R NiO 3 through interfacial charge transfer, as corroborated by first-principles calculations, but also provides an extended screening layer that reduces the depolarization effect in the ferroelectric gate. Our study points to an effective material strategy for the functional design of complex oxide heterointerfaces that harnesses the competing roles of charge in field effect screening and ferroelectric depolarization effects.
This project focused on the development of metal halide perovskite-polymeric nanofiber nanocomposites for robust and efficient solar cells. Metal halide perovskite solar cells are a potentially disruptive technology with high efficiencies and low-cost solution processability amenable with roll-to-roll fabrication of flexible devices. However, perovskites exhibit extreme brittleness, high internal film stresses, and chemical instabilities that significantly limit service lifetimes under normal operating conditions. To date, most work addressing these shortcomings has been focused on encapsulating devices between thick layers of rigid materials such as glass, which increases the total weight and cost of devices. Furthermore, these rigid and heavy encapsulants do not translate well to emerging solar markets vital for national security, including integrated mobile power, emergency deployments for disaster relief and military engagement, and lightweight power for space. It is therefore critical to improve the thermomechanical and chemical stability of perovskite devices in a way that is compatible with flexible and lightweight substrates. In this project, we integrated nylon-6,6 nanofibers into MAPbI3 to mechanically reinforce the perovskite, demonstrating films that exhibited fracture energies 5-fold greater than pristine MAPbI3 while also maintaining similar power conversion efficiencies due to beneficial light scattering effects from the polymeric nanofibers. These results required successful development of approaches to control the deposition thickness of nanofiber mats down to 100s of nanometers, and to ensure the polymer nanofibers did not inhibit charge transport to the at the perovskite-transport layer interface.
The electric grid is in the beginning stage of a transformation, driven by a combination of shutdowns of coal-fired plants, commissioning of new natural-gas plants, and tremendous growth in energy supply from renewables such as wind, and solar. As utilities navigate this transformation, their progress is supported by advances in Operational Technologies (OT), and Informational Technologies (IT), such as automation, smart inverters, cloud computing, mobile computing, machine learning, big data analytics, which have the potential to enable advanced capabilities more efficiently and at a lower cost. This white paper focuses on the architectural considerations that will allow the industry to transition in a planned manner. It introduces and formalizes two architectural constructs –the data bus and the control bus. The data bus is responsible for carrying all non-operational models and information necessary to drive utility decisions. In contrast, the control bus is responsible for carrying all operational data and control actions taken at the local level, centralized level, or other levels in-between. The paper reviews those architectural considerations, their requirements and how they will evolve. This paper intends to provide a context for vendors, utilities, and their service providers to review and understand the changes that are coming and get ready for them.
The present study reports chemometric modeling of power conversion efficiency (PCE) of dye sensitized solar cells (DSSCs) using the biggest available data set till date which comprises around 1200 dyes covering 7 chemical classes. To extract the best structural features required for higher PCE, we have developed multiple partial least squares (PLS) quantitative structure-property relationship (QSPR) models for the Triphenylamine, Phenothiazine, Indoline, Porphyrin, Coumarin, Carbazole and Diphenylamine chemical classes using descriptors derived from the best subset selection method followed by selection of best five models in each dataset based on the Mean Absolute Error (MAE) values. The models were validated both internally and externally followed by the consensus predictions employing “Intelligent Consensus Predictor” tool to examine whether the quality of predictions can be improved with the “intelligent” selection of multiple PLS models. The quality of predictions for the respective external sets showed that the consensus models (CM) are better than the individual models (IM) in most of the cases. From the insights of the developed models, we concluded that attributes like a packed structure toward higher conductivity of electrons, auxiliary donor fragment of aromatic tertiary amines, number of thiophenes inducing the bathochromic shift and augmenting the absorption, presence of additional electron donors, enhancement of electron-donating abilities, number of non-aromatic conjugated C(sp 2 ) which helps as conjugation extension units to broaden the absorption and highly conjugated $π$-systems exert positive contributions to the PCE. On the contrary, features negatively contributing to PCE are the followings: fragments which lower the tendency of localized $π–π*$ transition, fragments related to larger volume and surface area of dyes along with hydrophobicity resulting in poor adhesion, fragment RC = N causing dye hydrolysis, steric hindrance for π electronic mobility, fragments enhancing polarity, etc. Finally, the identified features from the best QSPR model of the coumarin dataset was employed in designing of ten more efficient coumarin dyes (predicted %PCE ranging from 8.93 to 10.62) than the existing ones.
Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement are critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregate in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance or productivity of freight systems. Addressing the research need, this paper presents the “Freight Mobility Energy Productivity” metric to quantify freight productivity of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight productivity. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight productivity as a result of vehicle electrification. The relative differences in Freight Mobility Energy Productivity scores help identify which zones benefit from the vehicle powertrain technology improvement. The results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the productivity of existing as well as future freight trends and technologies.
Nanowire arrays present many unique advantages for solar-to-chemical energy conversion. One possible advantage is that photon recycling between neighboring nanowires has the potential to increase solar energy conversion efficiencies. Here, in this work, we explore three underlying mechanisms of optical and electronic coupling between neighboring nanowires─incident photon scattering, photon recycling, and charge-carrier transport from the photoexcited nanowire to the neighboring nanowire via the underlying substrate─using single nanowire-level microscopy and spectroscopy measurements. We present a comprehensive analysis of light absorption and emission of a single nanowire at open circuit, and subsequent re-absorption and re-emission by a neighboring nanowire. We developed a novel correlated single nanowire microspectroscopy and widefield imaging methodology to spatially resolve photon communication pathways between neighboring nanowires and selectively image re-emitted and reflected photons. We developed unique multiphysics models to couple wave optics and semiconductor photophysics to especially isolate contributions from photon recycling and electronic transport to photon emission from neighboring nanowires. By systematically varying the morphologies of the nanowires modeled, we identified pathways to maximize photon recycling between neighboring nanowires. We concluded that the measured photoluminescence is more strongly influenced by the diffusion of charge carriers as compared to photon recycling in materials with moderate-to-large charge-carrier mobilities (>10 cm 2 V –1 s –1 ), and that photon recycling dictates photoluminescence intensity only when the charge-carrier mobility is low (<1 cm 2 V –1 s –1 ). The experimental and simulation platforms developed herein for photon management strategies can be leveraged by the semiconductor photocatalysis community to enhance solar-to-chemical conversion efficiencies in semiconductor nanowire arrays.
Radioiodine ( 129 I) poses a risk to the environment due to its long half-life, toxicity, and mobility. It is found at the U.S. Department of Energy Hanford Site due to legacy releases of nuclear wastes to the subsurface where 129 I is predominantly present as iodate (IO 3 – ). To date, a cost-effective and scalable cleanup technology for 129 I has not been identified, with hydraulic containment implemented as the remedial approach. Here, novel high-performing sorbents for 129 I remediation with the capacity to reduce 129 I concentrations to or below the US Environmental Protection Agency (EPA) drinking water standard and procedures to deploy them in an ex-situ pump and treat (P&T) system are introduced. Furthermore, this includes implementation of hybridized polyacrylonitrile (PAN) beads for ex-situ remediation of IO 3 – -contaminated groundwater for the first time. Iron (Fe) oxyhydroxide and bismuth (Bi) oxyhydroxide sorbents were deployed on silica substrates or encapsulated in porous PAN beads. In addition, Fe–, cerium (Ce)–, and Bi–oxyhydroxides were encapsulated with anion-exchange resins. The PAN–bismuth oxyhydroxide and PAN–ferrihydrite composites along with Fe- and Ce-based hybrid anion-exchange resins performed well in batch sorption experiments with distribution coefficients for IO 3 – of >1000 mL/g and rapid removal kinetics. Of the tested materials, the Ce-based hybrid anion-exchange resin was the most efficient for removal of IO 3 – from Hanford groundwater in a column system, with 50% breakthrough occurring at 324 pore volumes. The functional amine groups on the parent resin and amount of active sorbent in the resin can be customized to improve the iodine loading capacity. These results highlight the potential for IO 3 – remediation by hybrid sorbents and represent a benchmark for the implementation of commercially available materials to meet EPA standards for cleanup of 129 I in a large-scale P&T system.