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Results for “ENGINEERING,MATERIALS SCIENCE,SOLAR ENERGY”

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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5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

InP- and GaAs-Based 0.6 eV GaInAs Devices for Thermophotovoltaics and Laser Power Conversion

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.

ENGINEERING,MATERIALS SCIENCE,SOLAR ENERGY↗
DOE OSTI2021

Additive Manufacturing of Corrosion Resistant UHTC Materials for Chloride Salt-to-sCO2 Brayton Cycle Heat Exchangers

The objectives of the project include: (1) screen high-purity, high density ultra-high-temperature ceramics (UHTCs) for chloride salt corrosion resistance, (2) develop binder-jet additive manufacturing and sintering for ZrB 2 -MoSi 2 blends using literature and experiments to demonstrate feasibility, and (3) evaluate chloride salt corrosion resistance of additively manufactured ZrB 2 -MoSi 2 . These initial objectives support the development of a durable and reliable ultra-compact high-efficiency salt-to-sCO 2 heat exchanger for closed-loop Brayton power cycles based on corrosion resistant, high-strength, thermally conductive UHTC materials and novel designs enabled by additive manufacturing. The UHTC materials are also abrasion resistant so that they could be applicable to all three thermal carrier media pathways identified in the CSP Gen3 Roadmap.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exploring the Performance and Reliability of Screen-Printable Fire-Through Copper Paste on PERC Solar Cells

For 40 TW of PV required to transition our planet to 100% renewables, the silver (Ag) should disappear from PV production. Advantages of copper (Cu) over silver (Ag) include 1) bulk Cu has a similar conductivity to Ag (1.7 mu O-cm for Cu, 1.6 mu O-cm for Ag) and 2) Cu is approximately 100 times cheaper than Ag, making it an excellent potential replacement. Problems associated with copper (Cu) contacts include 1) easy oxidation and 2) diffusion into the Si cell and recombination activity.

copper paste↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments

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.

artificial intelligence↗