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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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Reproducing curvature effects due to differential diffusion in tabulated chemistry for premixed flames
Tabulated chemistry reduces the cost of modeling reacting flows by transporting fewer scalars. It also has the benefit of introducing fewer terms that must be closed for Reynolds-averaged Navier-Stokes and large eddy simulation modeling. In this study of premixed lean hydrogen–air flames, an extension to an existing tabulated chemistry model was developed by including differential diffusion and thermal diffusion of multiple species, using a mixture-averaged diffusion model. The transport equations for the tabulation variables (a progress variable and a mixture fraction-like variable) have been formulated, and curvature effects are intrinsic to the transport equations derived from first principles. The mathematical derivation is general, such that other fuel/air mixtures could be examined. The new tabulated chemistry model was then validated using a range of configurations, including flames with curvature, extinction, and hot spots. The model validation indicated excellent agreement with detailed chemistry simulations for lean premixed hydrogen–air mixtures. Conditional means of the progress variable source term were shown to agree well with detailed chemistry simulations of two-dimensional freely propagating flames and three-dimensional turbulent flames. Finally, progress variable source terms three times higher than one-dimensional flat flame results were identified and validated with detailed chemistry, and super-adiabatic hot spots were also predicted in the turbulent flame configuration.
Reproducible responses of geochemical and microbial successional patterns in the subsurface to carbon source amendment
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Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy
Atom probe tomography, and related methods, probe the three-dimensional architecture of a material. The software tools that microscopists use, and how these tools are connected into workflows, makes a substantial contribution to the accuracy and precision of such a material characterization experiment. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced that collaborating is useful. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-to-day data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results.
How Reproducible Are Isotherm Measurements in Metal–Organic Frameworks?
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Building a More Predictive Protein Force Field: A Systematic and Reproducible Route to AMBER-FB15
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Examination of Replicate Syntheses of Metal Organic Frameworks as a Window into Reproducibility in Materials Chemistry
Replicate experiments are a useful tool in understanding the repeatability of scientific measurements. In 2019, a systematic search for replicate syntheses of a collection of 130 metal–organic frameworks (MOFs) found that 89% of these materials had no reported replicate syntheses apart from the original publications identifying the material (Agrawal, M. Proc. Natl. Acad. Sci. U.S.A. 2020, 117, 877−88210.1073/pnas.1918484117). A potential weakness of that search was that only 5–11 years had elapsed since the original publication of each material. Here, this analysis is extended to all publications 11–17 years after the original publication. Although this extended time period identifies more repeat syntheses, 83% of the materials still have no reported replicate syntheses. We also consider how appropriately selected Density Functional Theory (DFT) calculations can provide corroboration for the experimentally reported crystal structures. By using data from previous high-throughput DFT studies, corroborating evidence from DFT was available for 17% of the 130 structures for which no replicate syntheses are available. In total, approximately 1/3 of the 130 MOFs have data associated with replicate synthesis experiments and/or directly corroborating DFT calculations.
Reproducing Quantum Probability Distributions at the Speed of Classical Dynamics: A New Approach for Developing Force-Field Functors
Modeling nuclear quantum effects is required for accurate molecular dynamics (MD) simulations of molecules. The community has paid special attention to water and other biomolecules that show hydrogen bonding. Standard methods of modeling nuclear quantum effects like Ring Polymer Molecular Dynamics (RPMD) are computationally costlier than running classical trajectories. A force-field functor (FFF) is an alternative method that computes an effective force field that replicates quantum properties of the original force field. In this work, we propose an efficient method of computing FFF using the Wigner–Kirkwood expansion. As a test case, we calculate a range of thermodynamic properties of Neon, obtaining the same level of accuracy as RPMD, but with the shorter runtime of classical simulations. Lastly, by modifying existing MD programs, the proposed method could be used in the future to increase the efficiency and accuracy of MD simulations involving water and proteins.
Predicting Device Parameters for Dye-Sensitized Solar Cells from Electronic Structure Calculations to Reproduce Experiment.
Given that improvements to the power-conversion efficiency (PCE) of dye-sensitized solar cells (DSSCs) have slowed in recent years, a means to accurately predict the device parameters yielded by trial dyes in silico, without having to synthesize them, would be extremely valuable to speed up the design process. Currently, the best-performing methods of calculating device parameters rely on a set of experimentally determined kinetic coefficients. In practice, it is very difficult to measure these kinetic parameters accurately, limiting the overall accuracy of such predictive methods. This work proposes a model to obtain key parameters such as J(SC), V-OC, and PCE using only the results from density functional theory (DFT) and time-dependent DFT calculations, noting that rates of electron-transfer steps are ultimately linked to the electronic structure of the dye center dot center dot center dot TiO2 working electrode. Six organic DSSC dyes from dissimilar chemical classes (L0, L1, L2, WS-2, WS-92, and C281) were chosen to demonstrate the power of this approach. Their a priori known experimentally determined device performance metrics served to validate our predictions. The greatest absolute error in our predicted PCE values was 0.36% relative to the experiment, while the greatest fractional error was 0.042. This indicates that the proposed model offers a dramatic improvement on previous predictive methods for DSSC device parameters, both in accuracy and in consistency. Moreover, such a predictive model has great potential to be applied to other photovoltaic applications, further enabling the design of novel, highly efficient photoactive materials.
Building a Consistent and Reproducible Database for Adsorption Evaluation in Covalent–Organic Frameworks
We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent-organic frameworks (COFs) reported in the literature, to characterize their CO 2 adsorption properties using the following steps: (1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory, (2) fitting atomic point charges based on the electron density, (3) characterizing the pore geometry of the structures before and after optimization, (4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally, (5) assessing the CO 2 parasitic energy via process modeling. The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.
Wettability Improvement of a Carbazole-Based Hole-Selective Monolayer for Reproducible Perovskite Solar Cells
Not Available
Improved Atoms-in-Molecule Charge Partitioning Functional for Simultaneously Reproducing the Electrostatic Potential and Chemical States in Periodic and Nonperiodic Materials
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Development of Isopentanol Reaction Mechanism Reproducing Autoignition Character at High and Low Temperatures
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Storage Conditions of Human Kidney Tissue Sections Affect Spatial Lipidomics Analysis Reproducibility
Lipids often are labile, unstable, and tend to degrade overtime, so it is of the upmost importance to study these molecules in their most native state. In this study, we sought to understand the optimal storage conditions for spatial lipidomic analysis of human kidney tissue sections. Specifically, we evaluated human kidney tissue sections on several different days throughout the span of a week using our established protocol for elucidating lipids using high mass resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). We studied kidney tissue sections stored under five different conditions: open stored at –80 °C, vacuumed sealed and stored at –80 °C, with matrix preapplied before storage at –80 °C, under a nitrogen atmosphere and stored at –80 °C, and at room temperature in a desiccator. Results were compared to data obtained from kidney tissue sections that were prepared and analyzed immediately after cryosectioning. Data was processed using METASPACE. After a week of storage, the sections stored at room temperature showed the largest amount of lipid degradation, while sections stored under nitrogen and at –80 °C retained the greatest number of overlapping annotations in relation to freshly cut tissue. Overall, we found that molecular degradation of the tissue sections was unavoidable over time, regardless of storage conditions, but storing tissue sections in an inert gas at low temperatures can curtail molecular degradation within tissue sections.
Collaborative cloud-enabled tools allow rapid, reproducible biological insights
Microbial ecologists today face critical computational barriers. The rapid increase in the quantity of data acquired by modern sequencing instruments makes analysis by hand infeasible, and even software developed just a few years ago cannot scale to modern data sets. As a result, making advanced, scalable algorithms and large-scale computational resources available to end-users is necessary to advancing our understanding of microbial ecology.