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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 data set contains ice nucleating particle (INP) measurements, using Colorado State University’s (CSU) Ice Spectrometer (IS) of samples collected during the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign. Samples were collected from five different media: aerosol filters, meltwater, ice cores, snow pits, and bulk sea water (BSW).
Machine Learning (ML) has seen an exponential growth in its applications due to its advanced data driven prediction capabilities. The study presents a data-driven approach as a preliminary attempt to predict the power at which departure from nucleate boiling (DNB) occurs in pressurized water reactors (PWRs) by constructing an advanced ML algorithm that takes outlet pressure, inlet temperature and inlet mass flux as the input features. DNB is a critical heat flux (CHF) phenomenon seen in PWRs. The experimental data from the PWR subchannel and bundle tests (PSBT) benchmark is first used to train an artificial neural network (ANN) to predict the DNB power, which produces a root mean square error (RMSE) of 6.89 kW/m when tested on a blind subset of the PSBT data. Since the PSBT dataset is relatively small to train an accurate ANN, a data augmentation methodology based on generative adversarial networks (GANs) is used to expand the training dataset. By assuming that the real data follows a certain distribution, GANs try to learn that underlying distribution to generate similar synthetic data to augment the database and to improve the predictive capabilities of the ANN. The data generated from GANs are validated using 1-nearest neighbor and kernel maximum mean discrepancy. To further ensure data from GAN is similar to PSBT, the data is tested and filtered out using the sub-channel thermal-hydraulic code CTF. The results indicate that with the addition of 120 data points from GAN the RMSE reduces to 4.84 kW/m showing promising results for future developments.
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Nanoparticle-based superlattices have attracted extensive research efforts due to their versatile composition and packing-dependent optical, magnetic, electronic, catalytic, mechanical properties. Yet the experimental understanding of the thermodynamics and pathways of the crystallization processes for these systems, which govern their ultimate morphology, size, and packing structure, has for long remained underexplored. Here, this minireview highlights recent integration of liquid-phase transmission electron microscopy, which allows for real-space, real-time direct imaging of the crystallization processes at the single-particle level, with simulation and the conceptual framework established by pioneers such as Dr. A. A. Chernov. Different aspects of superlattice formation were revealed by direct imaging for the first time, such as the existence of prenucleation precursor in nonclassical crystallization, surface energy-dependent growth, and coalescence, allowing for the charting of phase coordinates and thermodynamic quantities at the nanoscale. We discuss the similarities and differences of crystal growth behaviors in atomic and nanoparticle systems, as well as engineering opportunities to fashion, shape, and achieve quality control in superlattice formation from nanoparticles, for next-generation optical and mechanical metamaterials.
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Abstract Terrace‐edges, which are step‐like height discontinuities formed during layer stacking or exfoliation, can locally modify magnetic characteristics. Magnetic domain behavior and its structural dependence in a 2D van der Waals (vdW) heterostructure composed of two distinct room‐temperature ferromagnets: Fe 3 GaTe 2 , an intrinsic vdW ferromagnet, and vanadium‐doped WSe 2 , a transition metal dichalcogenide exhibiting defect‐induced magnetism, is investigated. Using magnetic transmission X‐ray microscopy, the formation and annihilation of magnetic domains are directly observed and it is found that domains preferentially form at terrace‐edges of the heterostructure. Micromagnetic simulations reveal that in‐plane magnetization tilting near the terrace‐edge results in a localized maximum in total magnetic energy that promotes domain formation. The findings highlight the significant role of structural edge features and interfacial magnetic interactions in determining domain formation in hybrid 2D vdW magnetic systems, offering a new route to spatially controlled magnetization at room temperature.
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Metal halide perovskite solar cells (PSCs) attract an enormous attention because of their high power conversion efficiency (PCE) and low fabrication cost. However, their commercialization is limited by fabricating highly efficient large-area solar cells. Controlling the morphology and crystallization of perovskite for large-area fabrication is difficult but important. Here, in this work, a vacuum-assisted approach is developed to obtain mirror-like, pinhole-free, highly crystalline, and uniform blade-coated perovskite films, without the use of antisolvent and air knife. This method can be a universal approach for various perovskite compositions. Meanwhile, the phenethylammonium iodide passivation effect at the top and bottom interfaces of perovskite layer based on FTO/NiO x /perovskite/C 60 /BCP/Cu inverted p-i-n structure is systematically investigated. The optimized device fabricated by blade coating under an ambient environment exhibits the champion PCE of 20.7% and is among the top few records of blade coating inverted structure based on NiOx hole transport layer. The encapsulated device retains 97% of its maximum efficiency under open-circuit condition after 1000 h of photostability test in an ambient environment at room temperature with a relative humidity of 40–60%. Herein, low-cost, easy, and reproducible strategies to fabricate efficient and stable blade-coated PSCs are demonstrated.
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