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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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Quantum Well Infrared Photodetector Research and Development at Jet Propulsion Laboratory
In this paper we discuss the optimization of the detector design, material growth and processing that has culminated in realization of 15 micron cutoff 128 x 128 QWIP focal plane array camera, hand-held and palmize 256 x 256 long-wavelength QWIP cameras and 648 x 480 long-wavelength cameras, holding forth great promise for myriad applications in 6-25 micron wavelength range in science, medicine, defense and industry.
Fabrication and Performance of Diffractive Optics for Quantum Well Infrared Photodetectors
Diffractive optical elements (microlenses) for quantum will infrared photodetectors (QWIPs) were fabricated by two techniques: 1) standard lithography of a binary optical structure and 2) PMMA pattern transfer for an analog diffractive optic structure.
Recent developments and applications of Quantum Well Infrared Photodetector focal plane arrays
In this paper we discuss the optimization of the detector design, material growth and processing that has culminated in realization of 15 micron cutoff 128x128 QWIP focal plane array camera, hand-held and palmsize 256x256 long wavelength QWIP cameras and 648x480 long-wavelength camera, holding forth great promise for myriad applications in 6-25 micron wavelength range in science, medicine, defense and industry.
Commercialization of Quantum Well Infrared Photodetector QWIP Focal Plane Arrays
Many commercial and government applications need high performance, large format, long-wavelength infrared (LWIR) detector arrays in the range of 6-20 mu.
Enhanced Gas Recovery and Stimulation by Thermal Methods in Depleted Hydraulically Fractured Gas Wells in Shales
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Metamaterials for Geothermal Well Integrity Monitoring DOE Phase I Final Report
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Optimized Deuterium Ionizer for Pulsed and Continuous Neutron Sources for Well Logging Applications
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Development of Passive Cooling Mechanisms and Material Selection for Acoustic Well Logging Tool Survival in High-Temperature High-Pressure Geothermal Environments
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Why it is Unfortunate that Linear Machine Learning “Works” so well in Electromechanical Switching of Ferroelectric Thin Films
Machine learning (ML) is relied on for materials spectroscopy. It is challenging to make ML models fail because statistical correlations can mimic the physics without causality. Here, using a benchmark band-excitation piezoresponse force microscopy polarization spectroscopy (BEPS) dataset the pitfalls of the so-called “better”, “faster”, and “less-biased” ML of electromechanical switching are demonstrated and overcome. Using a toy and real experimental dataset, it is demonstrated how linear nontemporal ML methods result in physically reasonable embedding (eigenvalues) while producing nonsensical eigenvectors and generated spectra, promoting misleading interpretations. A new method of unsupervised multimodal hyperspectral analysis of BEPS is demonstrated using long-short-term memory (LSTM) β-variational autoencoders (β-VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. Further, to improve the interpretability of the latent space, a variational Kullback–Leibler-divergency regularization is imposed . Finally, regularization scheduling of β as a disentanglement metric is leveraged to reduce user bias. Combining these experiment-inspired modifications enables the automated detection of ferroelectric switching mechanisms, including a complex two-step, three-state one. Ultimately, this work provides a robust ML method for the rapid discovery of electromechanical switching mechanisms in ferroelectrics and is applicable to other multimodal hyperspectral materials spectroscopies.
Effects of injection well operation conditions on CO 2 storage capacity in deep saline aquifers
Not provided.
Butterfly, Vinylidene-Like, Monobridged and Trans Structures of Si 2 H 2 + : Comparison to the Well-Characterized Neutral Si 2 H 2
This paper investigates four of the constitutional isomers of Si 2 H 2 + , namely the butterfly, vinylidene-like, monobridged, and trans structures. These isomer geometries were all studied using the CCSD(T) method with basis sets as large as cc-pV5Z. Higher level methods CCSDT and CCSDT(Q) were used for final energetics. It is found that the butterfly isomer has the lowest energy, followed by vinylidene-like and monobridged structures; the trans structure has the highest energy of the four. All structures were compared with their neutral counterparts. Partial charges are computed and it is found that all isomers share the positive charge between the silicon atoms. NBO analysis shows that the cation Si-Si bond order is reduced by 0.5 relative to the neutral versions of the same isomer in the case of the butterfly, vinylidene-like, and monobridged; and reduced by 0.6 for the trans isomer. Vibrational frequencies, infrared intensities, and dipole moments are predicted to encourage the spectroscopic identification of Si 2 H 2 + .
Open‐source photovoltaic model pipeline validation against well‐characterized system data
Abstract All freely available plane‐of‐array (POA) transposition models and photovoltaic (PV) temperature and performance models in pvlib‐python and pvpltools‐python were examined against multiyear field data from Albuquerque, New Mexico. The data include different PV systems composed of crystalline silicon modules that vary in cell type, module construction, and materials. These systems have been characterized via IEC 61853‐1 and 61853‐2 testing, and the input data for each model were sourced from these system‐specific test results, rather than considering any generic input data (e.g., manufacturer's specification [spec] sheets or generic Panneau Solaire [PAN] files). Six POA transposition models, 7 temperature models, and 12 performance models are included in this comparative analysis. These freely available models were proven effective across many different types of technologies. The POA transposition models exhibited average normalized mean bias errors (NMBEs) within ±3%. Most PV temperature models underestimated temperature exhibiting mean and median residuals ranging from −6.5°C to 2.7°C; all temperature models saw a reduction in root mean square error when using transient assumptions over steady state. The performance models demonstrated similar behavior with a first and third interquartile NMBEs within ±4.2% and an overall average NMBE within ±2.3%. Although differences among models were observed at different times of the day/year, this study shows that the availability of system‐specific input data is more important than model selection. For example, using spec sheet or generic PAN file data with a complex PV performance model does not guarantee a better accuracy than a simpler PV performance model that uses system‐specific data.
Chain extension epoxide polymerization to well‐defined block polymers using a N‐Al Lewis pair catalyst
Block polyethers comprised of poly(propylene oxide) (PPO) and poly(ethylene oxide) (PEG or PEO) segments form the basis of ABA-type PEO-b-PPO-b-PEO poloxamer materials. The inverse architecture with an internal hydrophilic PEO segment flanked by hydrophobic blocks can be difficult to prepare with control of architecture by use of traditional anionic polymerization. These oxyanionic polymerizations are plagued by chain-transfer-to-monomer side reactions that occur with substituted epoxides such as propylene oxide (PO). Herein, we report a new method for the preparation of block polymers through a controlled polymerization involving a N-Al Lewis adduct catalyst and an aluminum alkoxide macroinitiator. The Lewis pair catalyst was able to chain-extend commercial PEO macroinitiators to prepare di-, tri-, and pentablock polyethers with low dispersity and reasonable monomer tolerance. Chain extension was confirmed using size exclusion chromatography and diffusion ordered nuclear magnetic resonance spectroscopy. In conclusion, the resulting block polymers were additionally analyzed with small-angle X-ray scattering to correlate the morphology to molecular architecture.