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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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This is an evaluation of composite materials for heliostat cost reduction, including the motivation, reference heliostat model, characteristic wind loads, selection of pultruded structural composites, and technoeconomic analysis using equivalent deformation.
This poster explores reusable substrates for PV in large-scale space installations.
Emerging applications such as thermal energy grid storage, waste heat recovery and portable power generation require efficient thermophotovoltaic (TPV) converters tuned to temperatures near 1000 Degrees Celsius or below. Metamorphic GaInAs with larger lattice constants than InP present a promising option for these needs. The ability to grow these devices on GaAs substrates instead of more expensive InP would enhance the scalability of these devices. In this talk, we present inverted metamorphic Ga0.3In0.7As photovoltaic converters with sub-0.60 eV bandgaps grown on InP and GaAs substrates. These devices are realized using InAsP or GaInP/InAsP compositionally graded buffers which exhibit threading dislocation densities of 1.3 +/- 0.6 x 106 cm-2 and 8.9 +/- 1.7 x 106 cm-2 on InP and GaAs, respectively. Despite this difference in defect density, the devices generate similar open-circuit voltages of 0.386 V and 0.383 V, respectively, under irradiance producing a short-circuit current density of -10 A/cm2, with bandgap-voltage offsets of 0.20 and 0.21 V. We estimate their thermophotovoltaic efficiency using these measurements coupled with broadband reflectance measurments. The InP-based cell is estimated to yield 1.09 W/cm2 at 1100 Degrees Celsius vs. 0.92 W/cm2 for the GaAs-based cell, with TPV efficiencies of 16.8 vs. 9.2%. Both devices are limited by sub-bandgap absorption, which we assess largely occurs in the graded buffers. We estimate that the 1100 Degrees Celsius thermophotovoltaic efficiencies would increase to 24.0% and 20.7% in structures with the graded buffer removed, if previously demonstrated reflectance is achieved. These devices also have application as laser power converters in the 2.0-2.3 um atmospheric window. We estimate efficiencies of 36.8% and 32.5% under 2.0 um monochromatic irradiance of 1.86 W/cm2 and 2.81 W/cm2, respectively.
The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. Our ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.
Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.
Abstract Properties can be tailored by tuning composition in high-order composition spaces. For spaces with complex phase behavior, modeling the properties as a function of composition and phase distribution remains a formidable challenge. We present materials structure–property factorization (MSPF) as an approach to automate modeling of such data and identify synergistic phase interactions. MSPF is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks (DRNets) to matrix factorization-based modeling of the representative properties of each phase in a dataset. MSPF is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation, which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity relationships. Comparing the measured photoactivity to a learned model for non-interacting phases, synergistic phase interactions are identified to guide further photoactivity optimization and understanding. MSPF identifies synergistic interactions of a BiVO 4 -like phase with both Cu 2 V 2 O 7 -like and CuV 2 O 6 -like phases, creating avenues for understanding complex photoelectrocatalysts.
Meteorites contain prebiotic, bio-relevant organic compounds including amino acids. Their syntheses could result from diverse sources and mechanisms and provide a window on the conditions and materials present in the early solar system. Here we constrain alanine’s synthetic history in the Murchison meteorite using site-specific 13 C/ 12 C measurements, reported relative to the VPDB standard. The δ 13 C VPDB values of -29 ± 10‰, 142 ± 20‰, and -36 ± 20‰ for the carboxyl, amine-bound, and methyl carbons, respectively, are consistent with Strecker synthesis of interstellar-medium-derived aldehydes, ammonia, and low-δ 13 C nebular or interstellar-medium-derived CN. We report experimentally measured isotope effects associated with Strecker synthesis, and use them to constrain the δ 13 C values of the alanine precursors, which we then use to construct a model that predicts the molecular-average δ 13 C values of 19 other organic compounds of prebiotic significance found in Murchison if they were made by our proposed synthetic network. Finally, most of these predictions agree with previous measurements, suggesting that interstellar-medium-derived aldehydes and nebular and/or pre-solar CN could have served as substrates for synthesis of a wide range of prebiotic compounds in the early solar system.
This roadmap on Nanotechnology for Catalysis and Solar Energy Conversion focuses on the application of nanotechnology in addressing the current challenges of energy conversion: 'high efficiency, stability, safety, and the potential for low-cost/scalable manufacturing' to quote from the contributed article by Nathan Lewis. This roadmap focuses on solar-to-fuel conversion, solar water splitting, solar photovoltaics and bio-catalysis. It includes dye-sensitized solar cells (DSSCs), perovskite solar cells, and organic photovoltaics. Smart engineering of colloidal quantum materials and nanostructured electrodes will improve solar-to-fuel conversion efficiency, as described in the articles by Waiskopf and Banin and Meyer. Semiconductor nanoparticles will also improve solar energy conversion efficiency, as discussed by Boschloo et al in their article on DSSCs. Perovskite solar cells have advanced rapidly in recent years, including new ideas on 2D and 3D hybrid halide perovskites, as described by Spanopoulos et al 'Next generation' solar cells using multiple exciton generation (MEG) from hot carriers, described in the article by Nozik and Beard, could lead to remarkable improvement in photovoltaic efficiency by using quantization effects in semiconductor nanostructures (quantum dots, wires or wells). These challenges will not be met without simultaneous improvement in nanoscale characterization methods. Terahertz spectroscopy, discussed in the article by Milot et al is one example of a method that is overcoming the difficulties associated with nanoscale materials characterization by avoiding electrical contacts to nanoparticles, allowing characterization during device operation, and enabling characterization of a single nanoparticle. Besides experimental advances, computational science is also meeting the challenges of nanomaterials synthesis. The article by Kohlstedt and Schatz discusses the computational frameworks being used to predict structure–property relationships in materials and devices, including machine learning methods, with an emphasis on organic photovoltaics. The contribution by Megarity and Armstrong presents the 'electrochemical leaf' for improvements in electrochemistry and beyond. In addition, biohybrid approaches can take advantage of efficient and specific enzyme catalysts. These articles present the nanoscience and technology at the forefront of renewable energy development that will have significant benefits to society.
In the design of organic solar cells, there has been a need for materials with high power conversion efficiencies. Scharber’s model is commonly used to predict efficiency; however, it exhibits poor performance with new non-fullerene acceptor (NFA) devices, since it was designed for fullerene-based devices. In this work, an empirical model is proposed that can be a more accurate alternative for NFA organic solar cells. Additionally, many screening studies use computationally expensive methods. A model based on using semiempirical simplified time-dependent density functional theory (sTD-DFT) as an alternative method can accelerate the calculations and yield a similar accuracy. The models presented in this paper, termed organic photovoltaic efficiency predictor (OPEP) models, have shown significantly lower errors than previous models, with OPEP/B3LYP yielding errors of 1.53% and OPEP/sTD- DFT of 1.55%. As a result, the proposed computational models can be used for the fast and accurate screening of new high-efficiency NFAs/donor pairs.
The elucidation of structure-to-function relationships for two-dimensional (2D) hybrid perovskites remains a primary challenge for engineering efficient perovskite-based devices. By combining insights from theory and experiment, we describe the introduction of bifunctional ligands that are capable of making strong hydrogen bonds within the organic bilayer. We find that stronger intermolecular interactions draw charge away from the perovskite layers, and we have formulated a simple and intuitive computational descriptor, the charge separation descriptor (CSD), that accurately describes the relationship between the Pb-I-Pb angle, band gap, and in-plane charge transport with the strength of these interactions. A higher CSD value correlates to less distortion of the Pb-I-Pb angle, a reduced band gap, and higher in-plane mobility of the perovskite. These improved material properties result in improved device characteristics of the resulting solar cells.
The project, High-Efficiency Recycled Silicon Solar Cell, addresses the critical challenge of end-of-life (EoL) photovoltaic (PV) waste, which is expected to reach 78 million tons by 2050. Traditional recycling methods for recovering valuable materials like silver from silicon solar cells are inefficient, environmentally harmful, and economically unfeasible. Therefore, presently waste solar cells end up as landfills. This project aimed to develop a green, scalable, laser-based method for silver recovery, avoiding hazardous chemicals and minimizing operational costs. This research contributes to the circular economy in the renewable energy sector by enhancing material recovery from PV waste.
It is certain that perovskite materials must be a game‐changer in the solar industry as long as their stability reaches a level comparable with the lifetime of a commercialized Si photovoltaic. However, the operational stability of perovskite solar cells and modules still remains unresolved, especially when devices operate in practical energy‐harvesting modes represented by maximum power point tracking under 1 sun illumination at ambient conditions. This review article covers from fundamental aspects of perovskite instability including chemical decomposition pathways under light soaking and electrical bias, to recent advances and techniques that effectively prevent such degradation of perovskite solar cells and modules. In particular, fundamental causes for permanent degradation due to ion migration and trapped charges are overviewed and explain their interplay between ions and charges. Based on the degradation mechanism, recent advances on the strategies are discussed to slow down the degradation during operation for a practical use of perovskite‐based solar devices.
The structural stability of the metal halide perovskite (MHP) absorber material is crucial for the long-term solar cell stability in this thin-film photovoltaic technology. Here, we use mixed A-site FA0.83Cs0.17PbI3 to demonstrate that nanoscale compositional heterogeneity can serve as initiation sites for more macroscale, irreversible phase segregation, which causes device performance degradation. Probing compositional heterogeneity on length scales that has not been detected with conventional characterization techniques, we analyze the tetragonal to cubic phase transition behavior to indirectly determine the level of nanoscale compositional heterogeneity in the initial films. Further, we show that the thermal annealing conditions of the MHP layer during film processing influence the initial nanoscale compositional heterogeneity, and changing these processing conditions can be used to improve the device performance stability. The insights into structural degradation mechanisms initiated by nanoscale compositional heterogeneity and the proposed mitigation strategies will help guide the way toward long-term stable MHP solar cells.
II–VI semiconductors are used in numerous electro-optical applications. For example, CdTe-based solar technology is cost competitive with other electricity generation sources, yet there is still significant room to improve. Carrier lifetime has historically been well below the radiative recombination limit. Lifetimes reaching beyond 100 ns can significantly enhance performance and enable novel device structures. Here, double heterostructures (DHs) with passivated interfaces demonstrate lifetimes exceeding 1 µs, yet this appears only for CdSeTe and not for CdTe DHs. We compare the passivation mechanisms in CdTe and CdSeTe DHs. CdSeTe lifetimes on the order of 1 µs correspond to a combination of superior intragrain lifetime, extremely low grain boundary recombination and greater Te4+ interfacial presence compared to CdTe.
We demonstrate the uniqueness of Dynamic Hydride Vapor Phase Epitaxy (D-HVPE) to deposit III-V materials for high efficiency and potentially low-cost photovoltaics applications. Unlike traditional HVPE, D-HVPE allows for the deposition of relatively complex heterostructures and devices at high growth rates. We recently achieved record growth rates > 500 µm/h for GaAs and >200 µm/h for GaInP in our D-HVPE, and have not yet reached a growth rate limit for either material. We grew single junction GaAs solar cells at growth rates up to 309 µm/h without significant degradation of solar cell performance. We also demonstrate growth of Al-containing III-V materials in HVPE, a capability that makes D-HVPE more attractive for applications beyond photovoltaics.
2D materials, particularly transition metal dichalcogenides (TMDCs), have shown great potential for microelectronics and optoelectronics. However, a major challenge in commercializing these materials is the inability to control their doping at a wafer scale with high spatial fidelity. Interface chemistry is used with the underlying substrate oxide and concomitant exposure to visible light in ambient conditions for photo-dedoping wafer scale MoS 2 . It is hypothesized that the oxide layer traps photoexcited holes, leaving behind long-lived electrons that become available for surface reactions with ambient air at sulfur vacancies (defect sites) resulting in dedoping. Additionally, high fidelity spatial control is showcased over the dedoping process, by laser writing, and fine control achieved over the degree of doping by modulating the illumination time and power density. This localized change in MoS 2 doping density is very stable (at least 7 days) and robust to processing conditions like high temperature and vacuum. The scalability and ease of implementation of this approach can address one of the major issues preventing the “Lab to Fab” transition of 2D materials and facilitate its seamless integration for commercial applications in multi-logic devices, inverters, and other optoelectronic devices.
Transparent conducting materials (TCMs) are essential components for a variety of optoelectronic devices, such as photovoltaics, displays and touch screens. In recent years, extensive efforts have been made to develop TCMs with both high electrical conductivity and optical transmittance. Based on material types, they can be mainly categorized into the following classes: metal oxides, metal nanowire networks, carbon-material-based TCMs (graphene and carbon nanotube networks) and conjugated conductive polymers (PEDOT:PSS). This review will discuss the fundamental electrical and optical properties, typical fabrication methods and the applications in solar cells for each class of TCMs and highlight the current challenges and potential future research directions.
With the rise of global warming and the growing energy crisis, scientists have pivoted from typical resources to look for new materials and technologies. Perovskite materials hold the potential for making high-efficiency, low-cost solar cells through solution processing of Earth-abundant materials; however, scalability, stability, and durability remain key challenges. In order to transition from small-scale processing in inert environments to higher throughput processing in ambient conditions, the fundamentals of perovskite crystallization must be understood. Classical nucleation theory, the LaMer relation, and nonclassical crystallization considerations are discussed to provide a mechanism by which a gellan gum (GG) additive—a nontoxic polymeric saccharide—has enabled researchers to produce quality halide perovskite thin-film blade coated in ambient conditions without a quench step. Furthermore, we report on the improved stability and durability properties inherent to these films, which feature improved morphologies and optoelectronic properties compared to films spin-coated in a glovebox with antisolvent. We tune the amount of GG in the perovskite precursor and study the interplay between GG concentration and processability, morphological control, and increased stability under humidity, heat, and mechanical testing. The simplicity of this approach and insensitivity to environmental conditions enable a wide process window for the production of low-defect, mechanically robust, and operationally stable perovskites with fracture energies among the highest obtained for perovskites.