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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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Benchmarking Bicontinuous Nanospheres against Polymersomes for in Vivo Biodistribution and Dual Intracellular Delivery of Lipophilic and Water-Soluble Payloads

Bicontinuous nanospheres (BCNs) are polymeric analogs to lipid cubosomes, possessing cubic liquid crystalline phases with high internal surface area, aqueous channels for loading hydrophilic molecules, and high hydrophobic volume for lipophilic payloads. Primarily due to difficulties in scalable and consistent fabrication, neither controlled delivery of payloads via BCNs nor their organ or cellular biodistributions following in vivo administration have been demonstrated or characterized. We have recently validated flash nanoprecipitation as a rapid method of assembling uniform monodisperse 200–300 nm diameter BCNs from poly(ethylene glycol)-$b$-poly(propylene sulfide) (PEG-$b$-PPS) co-polymers. In this work, we compare these BCNs both in vitro and in vivo to 100 nm PEG-$b$-PPS polymersomes (PSs), which have been well characterized as nanocarriers for controlled delivery applications. Using a small molecule fluorophore and a fluorescently tagged protein as respective lipophilic and water-soluble model cargos, we demonstrate that BCNs can achieve significantly higher encapsulation efficiencies for both payloads on a per unit mass basis. At time points of 4 and 24 h after intravenous administration to mice, we found significant differences in organ-level uptake between BCNs and PSs, with BCNs showing reduced accumulation in the liver and increased uptake in the spleen. Despite these organ-level differences, BCNs and PSs displayed strikingly similar uptake profiles by immune cell populations in vitro and in the liver, spleen, and blood, as assayed by flow cytometry. In conclusion, we have found PEG-$b$-PPS BCNs to be well suited for dual loading and delivery of molecular payloads, with a favorable organ biodistribution and high cell uptake by therapeutically relevant immune cell populations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Benchmarking Guanidinium Organosulfonate Hydrogen-Bonded Frameworks for Structure Determination of Encapsulated Guests

Single crystal X-ray diffraction (SCXRD) is arguably the most definitive method for molecular structure determination, but it is often challenged by compounds that are liquids or oils at room temperature or do not form crystals adequate for analysis. Our laboratory previously reported a simple, cost-effective, single-step crystallization method based on guanidinium organosulfonate (GS) hydrogen bonded frameworks for structure determination of a wide range of encapsulated guest molecules, including assignment of the absolute configuration of chiral centers. Herein, we expand on those results and report a head-to-head comparison of the GS method with adamantoid “molecular chaperones”, which have been reported to be useful hosts for structure determination. Inclusion compounds limited to only two GS hosts are characterized by low R1 values and Flack parameters, infrequent disorder of the host and guest, and manageable disorder when it does exist. The structures of some target molecules that were not included or resolved using the adamantoid chaperones were successfully included and resolved by the GS hosts, and vice versa. Of the 32 guests attempted by the GS method, 31 inclusion compounds afforded successful guest structure solutions, a 97% success rate. The GS hosts and adamantoid chaperones are complementary with respect to guest inclusion, arguing that both should be employed in the arsenal of methods for structure determination. Furthermore, the low cost of organosulfonate host components promises an accessible route to molecular structure determination for a wide range of users.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Benchmark Theoretical Study of the π–π Binding Energy in the Benzene Dimer

We establish a new estimate for the binding energy between two benzene molecules in the parallel-displaced (PD) conformation by systematically converging (i) the intra- and intermolecular geometry at the minimum, (ii) the expansion of the orbital basis set, and (iii) the level of electron correlation. The calculations were performed at the second-order Møller–Plesset perturbation (MP2) and the coupled cluster including singles, doubles, and a perturbative estimate of triples replacement [CCSD(T)] levels of electronic structure theory. At both levels of theory, by including results corrected for basis set superposition error (BSSE), we have estimated the complete basis set (CBS) limit by employing the family of Dunning’s correlation-consistent polarized valence basis sets. The largest MP2 calculation was performed with the cc-pV6Z basis set (2772 basis functions), whereas the largest CCSD(T) calculation was with the cc-pV5Z basis set (1752 basis functions). The cluster geometries were optimized with basis sets up to quadruple-ζ quality, observing that both its intra- and intermolecular parts have practically converged with the triple-ζ quality sets. The use of converged geometries was found to play an important role for obtaining accurate estimates for the CBS limits. Our results demonstrate that the binding energies with the families of the plain (cc-pVnZ) and augmented (aug-cc-pVnZ) sets converge [within <0.01 kcal/mol for MP2 and <0.15 kcal/mol for CCSD(T)] to the same CBS limit. In addition, the average of the uncorrected and BSSE-corrected binding energies was found to converge to the same CBS limit much faster than either of the two constituents (uncorrected or BSSE-corrected binding energies). Due to the fact that the family of augmented basis sets (especially for the larger sets) causes serious linear dependency problems, the plain basis sets (for which no linear dependencies were found) are deemed as a more efficient and straightforward path for obtaining an accurate CBS limit. We considered extrapolations of the uncorrected (ΔE) and BSSE-corrected (ΔE cp ) binding energies, their average value (ΔE ave ), as well as the average of the latter over the plain and augmented sets (Δ~E ave ) with the cardinal number of the basis set n. Our best estimate of the CCSD(T)/CBS limit for the π–π binding energy in the PD benzene dimer is D e = -2.65 ± 0.02 kcal/mol. The best CCSD(T)/cc-pV5Z calculated value is -2.62 kcal/mol, just 0.03 kcal/mol away from the CBS limit. For comparison, the MP2/CBS limit estimate is -5.00 ± 0.01 kcal/mol, demonstrating a 90% overbinding with respect to CCSD(T). Finally, the spin-component-scaled (SCS) MP2 variant was found to closely reproduce the CCSD(T) results for each basis set, while scaled opposite spin (SOS) MP2 yielded results that are too low when compared to CCSD(T).

Environmental Molecular Sciences Laboratory↗
DOE OSTI2018

Benchmarking Geant4 for simulating galactic cosmic ray interactions within planetary bodies

Galactic cosmic rays undergo complex nuclear interactions with nuclei within planetary bodies that have little to no atmosphere. Radiation transport simulations are a key tool used in understanding the neutron and gamma-ray albedo coming from these interactions and tracing these signals back to geochemical composition of the target. In this paper, we study the validity of the code Geant4 for simulating such interactions by comparing simulation results to data from the Apollo 17 Lunar Neutron Probe Experiment. Different assumptions regarding the physics are explored to demonstrate how these impact the Geant4 simulation results. In general, all of the Geant4 results over-predict the data, however, certain physics lists perform better than others. Finally, in addition, we show that results from the radiation transport code MCNP6 are similar to those obtained using Geant4.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

The KPP Boundary Layer Scheme for the Ocean: Revisiting Its Formulation and Benchmarking One‐Dimensional Simulations Relative to LES

Abstract We evaluate the Community ocean Vertical Mixing project version of the K‐profile parameterization (KPP) for modeling upper ocean turbulent mixing. For this purpose, one‐dimensional KPP simulations are compared across a suite of oceanographically relevant regimes against horizontally averaged large eddy simulations (LESs). We find the standard configuration of KPP consistent with LES across many forcing regimes, supporting its physical basis. Our evaluation also motivates recommendations for KPP best practices within ocean circulation models and identifies areas where further research is warranted. The original treatment of KPP recommends the matching of interior diffusivities and their gradients to the KPP‐predicted values computed in the ocean surface boundary layer (OSBL). However, we find that difficulties in representing derivatives of rapidly changing diffusivities near the base of the OSBL can lead to loss of simulation fidelity. To mitigate this difficulty, we propose and evaluate two computationally simpler approaches: (1) match to the internal predicted diffusivity alone and (2) set the KPP diffusivity to 0 at the OSBL base. We find the KPP entrainment buoyancy flux to be sensitive to vertical grid resolution and details of how to diagnose the KPP boundary layer depth. We modify the KPP turbulent shear velocity parameterization to reduce resolution dependence. Additionally, an examination of LES vertical turbulent scalar flux budgets shows that the KPP‐parameterized nonlocal tracer flux is incomplete due to the assumption that it solely redistributes the surface tracer flux. This result motivates further studies of the nonlocal flux parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Benchmark to Test Generalization Capabilities of Deep Learning Methods to Classify Severe Convective Storms in a Changing Climate

Abstract This is a test case study assessing the ability of deep learning methods to generalize to a future climate (end of 21st century) when trained to classify thunderstorms in model output representative of the present‐day climate. A convolutional neural network (CNN) was trained to classify strongly rotating thunderstorms from a current climate created using the Weather Research and Forecasting model at high‐resolution, then evaluated against thunderstorms from a future climate and found to perform with skill and comparatively in both climates. Despite training with labels derived from a threshold value of a severe thunderstorm diagnostic (updraft helicity), which was not used as an input attribute, the CNN learned physical characteristics of organized convection and environments that are not captured by the diagnostic heuristic. Physical features were not prescribed but rather learned from the data, such as the importance of dry air at mid‐levels for intense thunderstorm development when low‐level moisture is present (i.e., convective available potential energy). Explanation techniques also revealed that thunderstorms classified as strongly rotating are associated with learned rotation signatures. Results show that the creation of synthetic data with ground truth is a viable alternative to human‐labeled data and that a CNN is able to generalize a target using learned features that would be difficult to encode due to spatial complexity. Most importantly, results from this study show that deep learning is capable of generalizing to future climate extremes and can exhibit out‐of‐sample robustness with hyperparameter tuning in certain applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A benchmark dataset for Hydrogen Combustion

The generation of reference data for deep learning models is challenging for reactive systems, and more so for combustion reactions due to the extreme conditions that create radical species and alternative spin states during the combustion process. Here, we extend intrinsic reaction coordinate (IRC) calculations with ab initio MD simulations and normal mode displacement calculations to more extensively cover the potential energy surface for 19 reaction channels for hydrogen combustion. A total of ~290,000 potential energies and ~1,270,000 nuclear force vectors are evaluated with a high quality range-separated hybrid density functional, ωB97X-V, to construct the reference data set, including transition state ensembles, for the deep learning models to study hydrogen combustion reaction.

08 HYDROGEN↗