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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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Design, Synthesis and X-Ray Structural Studies of Potent HIV-1 Protease Inhibitors Containing C-4 Su
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In;#8197;Situ Studies of Solvothermal Synthesis of Energy Materials
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Simulation and 4D seismic studies of pressure management and CO 2 plume control by means of brine extraction and monitoring at the Devine Test Site, South Texas, USA: Original Research Article: Simulation and 4D seismic studies of pressure management and CO 2 plume control
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Structural studies of antitumor compounds that target the RING domain of MDM2
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High pressure infrared and X-ray Raman studies of aluminum nitride
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Point defects in Cu 2 ZnSnSe 4 (CZTSe): Resonant X-ray diffraction study of the low-temperature order/disorder transition: Point defects in Cu 2 ZnSnSe 4 (CZTSe)
The interest in Cu2ZnSn(S,Se)4 (CZTS) for photovoltaic applications is motivated by similarities to Cu(In,Ga)Se2 while being comprised of non-toxic and earth abundant elements. However, CZTS suffers from a Voc deficit, where the Voc is much lower than expected based on the band gap, which may be the result of a high concentration of point-defects in the CZTS lattice. Recently, reports have observed a low-temperature order/disorder transition by Raman and optical spectroscopies in CZTS films and is reported to describe the ordering of Cu and Zn atoms in the CZTS crystal structure. To directly determine the level of Cu/Zn ordering, we have used resonant-XRD, a site, and element specific probe of long range order. We used CZTSe films annealed just below and quenched from just above the transition temperature; based on previous work, the Cu and Zn should be ordered and highly disordered, respectively. Our data show that there is some Cu/Zn ordering near the low temperature transition but significantly less than high chemical order expected from Raman. To understand both our resonant-XRD results and the Raman results, we present a structural model that involves antiphase domain boundaries and accommodates the excess Zn within the CZTS lattice.
Plant growth chamber design for subambient p CO 2 and δ 13 C studies
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Predicting wind farm operations with machine learning and the P2D‐RANS model: A case study for an AWAKEN site
Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.
Building a Wide-Area File Transfer Performance Predictor: An Empirical Study
Wide-area data transfer is central to geographically distributed scientific workflows. Faster delivery of data is important for these workflows. Predictability is equally (or even more) important. With the goal of providing a reasonably accurate estimate of data transfer time to improve resource allocation & scheduling for workflows and enable end-to-end data transfer optimization, we apply machine learning methods to develop predictive models for data transfer times over a variety of wide area networks. To build and evaluate these models, we use 201,388 transfers, involving 759 million files totaling 9 PB transferred, over 115 heavily used source-destination pairs (“edges”) between 135 unique endpoints. We evaluate the models for different retraining frequencies and different window size of history data. In the best case, the resulting models have a median prediction error of ≤21% for 50% of the edges, and ≤32% for 75% of the edges. We present a detailed analysis of these results that provides insights into the cause of some of the high errors. We envision that the performance predictor will be informative for scheduling geo-distributed workflows. The insights also suggest obvious directions for both further analysis and transfer service optimization.
A Speckle Patterning Study For Laboratory-Scale DIC Experiments.
Abstract not provided.
Structural behavior of a stuffed derivative of α-quartz, Mg0.5AlSiO4, at high temperature: an in situ synchrotron XRD study
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An X-ray diffraction and Raman spectroscopic study of the high-pressure behavior of gaspeite (Ni[sub
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Impact on Patient Safety and Satisfaction of Implementation of an Outpatient Clinic in Interventional Radiology (IPSIPOLI-Study): A Quasi-Experimental Prospective Study
PurposeInterventional radiology (IR) procedures are associated with high rates of preparation and planning errors. In many centers, pre-procedural consultation and screening of patients is performed by referring physicians. Interventional radiologists have better knowledge about procedure details and risks, but often only get acquainted with the patient in the procedure room. We hypothesized that patient safety (PS) and patient satisfaction (PSAT) in elective IR procedures would improve by implementation of a pre-procedural visit to an outpatient IR clinic.Material and MethodsIRB approval was obtained and informed consent was waived. PS and PSAT were measured in patients undergoing elective IR procedures before (control group; n = 110) and after (experimental group; n = 110) implementation of an outpatient IR clinic. PS was measured as the number of process deviations. PSAT was assessed using a questionnaire measuring Likert scores of three dimensions: interpersonal care aspects, information/communication, and patient participation. Differences in PS and PSAT between the two groups were compared using an independent t test.ResultsThe average number of process deviations per patient was 0.39 in the control group compared to 0.06 in the experimental group (p < 0.001). In 9.1 % patients in the control group, no legal informed consent was obtained compared to 0 % in the experimental group. The mean overall Likert score was significantly higher in the experimental group compared to the control group: 2.68 (SD 0.314) versus 2.48 (SD 0.381) (p < 0.001).ConclusionPS and PSAT improve significantly if patients receive consultation and screening in an IR outpatient clinic prior to elective IR procedures.