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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to enable and understanding of their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transfected with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produce. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of novel DB positions and enzymatic specificity.

mass spectrometry, deep learning, Lipidomics, doub↗
DOE OSTI2015

Experimental study of thermal conductivity at high pressures: Implications for the deep Earth’s interior

Lattice thermal conductivity of ferropericlase and radiative thermal conductivity of iron bearing magnesium silicate perovskite (bridgmanite) – the major mineral of Earth’s lower mantle– has been measured at room temperature up to 30 and 46 GPa, respectively, using time domain thermoreflectance and optical spectroscopy techniques in diamond anvil cells. The results provide new constraints for the pressure dependencies of the thermal conductivities of Fe bearing minerals. The lattice thermal conductivity of ferropericlase (Mg 0.9 Fe 0.1 )O is 5.7(6) W/(m*K) at ambient conditions, which is almost 10 times smaller than that of pure MgO; however, it increases with pressure much faster (6.1(7)%/GPa vs 3.6%/GPa). The radiative conductivity of Mg 0.94 Fe 0.06 SiO 3 bridgmanite single crystal agrees with previously determined values at ambient pressure; it is almost pressure-independent in the investigated pressure range. Furthermore, our results confirm the reduced radiative conductivity scenario for the Earth’s lower mantle, while the assessment of the heat flow through the core-mantle boundary still requires in situ measurements at the relevant pressure-temperature conditions.

58 GEOSCIENCES↗
DOE OSTI2016

Deep data mining in a real space: Separation of intertwined electronic responses in a lightly doped BaFe 2 As 2

Electronic interactions present in material compositions close to the superconducting dome play a key role in the manifestation of high-T c superconductivity. In many correlated electron systems, however, the parent or underdoped states exhibit strongly inhomogeneous electronic landscape at the nanoscale that may be associated with competing, coexisting, or intertwined chemical disorder, strain, magnetic, and structural order parameters. Here we demonstrate an approach based on a combination of scanning tunneling microscopy/spectroscopy and advanced statistical learning for an automatic separation and extraction of statistically significant electronic behaviors in the spin density wave regime of a lightly (~1%) gold-doped BaFe 2 As 2 . Lastly, we show that the decomposed STS spectral features have a direct relevance to fundamental physical properties of the system, such as SDW-induced gap, pseudogap-like state, and impurity resonance states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Deep Learning-Based Dynamic Modeling of Three-Phase Voltage Source Inverters

Inverter-based resource (IBR) models are necessary to analyze modern power system stability and create effective control strategies. Modeling IBRs in converter-rich power systems is crucial, yet challenging due to the lack of commercial information on converter topologies and control parameters. This paper proposes novel convolutional neural network (CNN)–based data-driven techniques for modeling IBRs, addressing adaptability and proprietary concerns without requiring internal system physics knowledge. The proposed method is tested using real grid-tied commercial IBR transient data and demonstrates effectiveness and accuracy. Furthermore, the developed modeling approach is integrated and implemented in the open-source power distribution simulation and analysis tool, GridLAB-D, to illustrate the potentiality of dynamic analysis of large-scale power systems with high IBRs.

deep learning, artificial intelligence↗
DOE OSTI2017

Reduced lattice thermal conductivity of Fe-bearing bridgmanite in Earth's deep mantle: Reduced Conductivity of Fe-Bridgmanite

Complex seismic, thermal, and chemical features have been reported in Earth's lowermost mantle. In particular, possible iron enrichments in the large low shear-wave velocity provinces (LLSVPs) could influence thermal transport properties of the constituting minerals in this region, altering the lower mantle dynamics and heat flux across core-mantle boundary (CMB). Thermal conductivity of bridgmanite is expected to partially control the thermal evolution and dynamics of Earth's lower mantle. Importantly, the pressure-induced lattice distortion and iron spin and valence states in bridgmanite could affect its lattice thermal conductivity, but these effects remain largely unknown. Here we precisely measured the lattice thermal conductivity of Fe-bearing bridgmanite to 120 GPa using optical pump-probe spectroscopy. The conductivity of Fe-bearing bridgmanite increases monotonically with pressure but drops significantly around 45 GPa due to pressure-induced lattice distortion on iron sites. Our findings indicate that lattice thermal conductivity at lowermost mantle conditions is twice smaller than previously thought. The decrease in the thermal conductivity of bridgmanite in mid-lower mantle and below would promote mantle flow against a potential viscosity barrier, facilitating slabs crossing over the 1000 km depth. Modeling of our results applied to LLSVPs shows that variations in iron and bridgmanite fractions induce a significant thermal conductivity decrease, which would enhance internal convective flow. Our CMB heat flux modeling indicates that while heat flux variations are dominated by thermal effects, variations in thermal conductivity also play a significant role. The CMB heat flux map we obtained is substantially different from those assumed so far, which may influence our understanding of the geodynamo.

58 GEOSCIENCES↗
DOE OSTI2021

Deep Optical Switching on Subpicosecond Timescales in an Amorphous Ge Metamaterial

Active nanostructured optical components show promise as potential building blocks for novel light-based computing and data processing architectures. However, nanoscale all-optical switches that have low activation powers and high-contrast ultrafast switching have been elusive so far. Here, pump–probe measurements performed on amorphous-Ge-based micro-resonator metasurfaces that exhibit strong resonant modes in the mid-infrared are reported. Relative change is observed in transmittance of ΔT/T ≈ 1 with picosecond (down to τ ≈ 0.5 ps) free carrier relaxation rates, obtained with very low pump fluences of 50 μJ cm –2 . In this work, these observations are attributed to efficient free carrier promotion, affecting light transmittance via high quality-factor optical resonances, followed by an increased electron–phonon scattering of free carriers due to the amorphous crystal structure of Ge. Full-wave simulations based on a permittivity model that describes free-carrier damping through crystal structure disorder find excellent agreement with the experimental data. These findings offer an efficient and robust platform for all-optical switching at the nanoscale.

36 MATERIALS SCIENCE↗