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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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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
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.
Conserved and reproducible bacterial communities associate with extraradical hyphae of arbuscular mycorrhizal fungi
Abstract Extraradical hyphae (ERH) of arbuscular mycorrhizal fungi (AMF) extend from plant roots into the soil environment and interact with soil microbial communities. Evidence of positive and negative interactions between AMF and soil bacteria point to functionally important ERH-associated communities. To characterize communities associated with ERH and test controls on their establishment and composition, we utilized an in-growth core system containing a live soil–sand mixture that allowed manual extraction of ERH for 16S rRNA gene amplicon profiling. Across experiments and soils, consistent enrichment of members of the Betaproteobacteriales, Myxococcales, Fibrobacterales, Cytophagales, Chloroflexales, and Cellvibrionales was observed on ERH samples, while variation among samples from different soils was observed primarily at lower taxonomic ranks. The ERH-associated community was conserved between two fungal species assayed, Glomus versiforme and Rhizophagus irregularis, though R. irregularis exerted a stronger selection and showed greater enrichment for taxa in the Alphaproteobacteria and Gammaproteobacteria. A distinct community established within 14 days of hyphal access to the soil, while temporal patterns of establishment and turnover varied between taxonomic groups. Identification of a conserved ERH-associated community is consistent with the concept of an AMF microbiome and can aid the characterization of facilitative and antagonistic interactions influencing the plant-fungal symbiosis.
On the issue of transparency and reproducibility in nanomedicine
Below we report short extracts highlighting the main messages of the correspondences we received.
Reproducibility in the fabrication and physics of moiré materials
Not provided.
Reproducible graphene synthesis by oxygen-free chemical vapour deposition
Not provided.
The need for reproducible research in soft robotics
In recent years we have witnessed the rise of commercialization efforts for soft robotic technology, including soft grippers (Soft Robotics, Inc.), stretchable sensors (StretchSense, Inc. and Lightlace, Inc.), and platforms for human-robot interaction (Festo Inc., Meta Reality Labs, Toyota Research Institute, and Disney Research). However, commercialization as a whole lags the trends enjoyed by other robotic technology at equivalent points in their respective lifecycles.
A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows
The capability to replicate the predictions by machine learning (ML) or artificial intelligence (AI) models and the results in scientific workflows that incorporate such ML/AI predictions is driven by a variety of factors.
Perspective: Reproducible Coherence Characterization of Superconducting Quantum Devices
As the field of superconducting quantum computing approaches maturity, optimization of single-device performance is proving to be a promising avenue towards large-scale quantum computers. However, this optimization is possible only if performance metrics can be accurately compared among measurements, devices, and laboratories. Currently such comparisons are inaccurate or impossible due to understudied errors from a plethora of sources. In this Perspective, we outline the current state of error analysis for qubits and resonators in superconducting quantum circuits, and discuss what future investigations are required before superconducting quantum device optimization can be realized.
Reproducibility of real-time time-dependent density functional theory calculations of electronic stopping power in warm dense matter
Real-time time-dependent density functional theory (TDDFT) is widely considered to be the most accurate available method for calculating electronic stopping powers from first principles, but there have been relatively few assessments of the consistency of its predictions across different implementations. This problem is particularly acute in the warm dense regime, where computational costs are high and experimental validation is rare and resource intensive. We report a comprehensive cross-verification of stopping power calculations in conditions relevant to inertial confinement fusion conducted using four different TDDFT implementations. We find excellent agreement among both the post-processed stopping powers and relevant time-resolved quantities for alpha particles in warm dense hydrogen. We also analyze sensitivities to a wide range of methodological details, including the exchange-correlation model, pseudopotentials, initial conditions, observable from which the stopping power is extracted, averaging procedures, projectile trajectory, and finite-size effects. We show that among these details, pseudopotentials, trajectory-dependence, and finite-size effects have the strongest influence, and we discuss different strategies for controlling the latter two considerations.
Consistent and reproducible computation of the glass transition temperature from molecular dynamics simulations
In many fields, from semiconductors for opto-electronic applications to ionic liquids (ILs) for separations, the glass transition temperature (Tg) of a material is a useful gauge for its potential use in practical settings. As a result, there is a great deal of interest in predicting Tg using molecular simulations. However, the uncertainty and variation in the trend shift method, a common approach in simulations to predict Tg, can be high. This is due to the need for human intervention in defining a fitting range for linear fits of density with temperature assumed for the liquid and glass phases across the simulated cooling. The definition of such fitting ranges then defines the estimate for the Tg as the intersection of linear fits. We eliminate this need for human intervention by leveraging the Shapiro–Wilk normality test and proposing an algorithm to define the fitting ranges and, consequently, Tg. Through this integration, we incorporate into our automated methodology that residuals must be normally distributed around zero for any fit, a requirement that must be met for any regression problem. Consequently, fitting ranges for realizing linear fits for each phase are statistically defined rather than visually inferred, obtaining an estimate for Tg without any human intervention. The method is also capable of finding multiple linear regimes across density vs temperature curves. We compare the predictions of our proposed method across multiple IL and semiconductor molecular dynamics simulation results from the literature and compare other proposed methods for automatically detecting Tg from density–temperature data. We believe that our proposed method would allow for more consistent predictions of Tg. We make this methodology available and open source through GitHub.
Towards molecular simulations that are transparent, reproducible, usable by others, and extensible (TRUE)
Not Available
Data Reproducibility of Spin-Echo Small-Angle Neutron Scattering Instruments
Spin-echo small-angle neutron scattering (SESANS) is a unique method to measure structures of materials in real space with length scales from ∼ 30 μm to ∼20 µm. As shown in Figure 1, the accessible length scale of SESANS is given by its ability to encode the momentum transfer into the Larmor phase, namely Φ = $\vec{𝛿}$ ⋅ $\vec{𝑄}$, where $\vec{𝑄}$ is the momentum transfer and $\vec{𝛿}$ is the encoding vector of the setup and its projection along Q (δQ) is called spin-echo length (SEL). The spin echo length, which is synonymous with the spatial correlation distance probed, is defined as the following 𝛿𝑄 ∝ 𝜆 2 𝐵𝐿cot𝜃 where 𝜆 is neutron wavelength, B is magnetic field, L is length of the parallelogram magnetic field region, and 𝜃 is the angle between the inclined magnetic field boundary and the beam direction, as shown in Figure 1. The result of the SESANS experiment is a Hankel transformation of the SANS scattering function I(Q), which yields the correlation function of the sample in real space.
Structuring superconductor data with ontology: reproducing historical datasets as knowledge bases
Not Available