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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Vapor-Fed Cathode Microbial Electrolysis Cells with Closely Spaced Electrodes Enables Greatly Improved Performance

Hydrogen can be electrochemically produced in microbial electrolysis cells (MECs) by current generated from bacterial anodes with a small added voltage. MECs typically use a liquid catholyte containing a buffer or salts. However, anions in these catholytes result in charge being balanced predominantly by ions other than hydroxide or protons, leading to anode acidification. To enhance only hydroxide ion transport to the anode, we developed a novel vapor-fed MEC configuration lacking a catholyte with closely spaced electrodes and an anion exchange membrane to limit the acidification. This MEC design produced a record-high sustained current density of 43.1 ± 0.6 A/m 2 and a H 2 production rate of 72 ± 2 LH 2 /L-d (cell voltage of 0.79 ± 0.00 V). There was minimal impact on MEC performance of increased acetate concentrations, solution conductivity, or anolyte buffer capacity at applied voltages up to 1.1 V, as shown by a nearly constant internal resistance of only 6.8 ± 0.3 mΩ m 2 . At applied external voltages >1.1 V, the buffer capacity impacted performance, with current densities increasing from 28.5 ± 0.6 A/m 2 (20 mM phosphate buffer solution (PBS)) to 51 ± 1 A/m 2 (100 mM PBS). Furthermore, these results show that a vapor-fed MEC can produce higher and more stable performance than liquid-fed cathodes by enhancing transport of hydroxide ions to the anode.

anion exchange membrane↗
DOE OSTI2022

Probing charge density in materials with atomic resolution in real space

The charge distribution in materials at the nanoscale can often explain the origin of macroscopic properties such as localized conductivity or the plasmonic response and illuminate more fundamental changes in the microscopic structure such as changes in chemical bonding characteristics. Previously, direct visualization of the charge density with high spatial resolution was often a missing link in the formation of structure–property relationships, especially in heterogeneous materials systems. Furthermore, recent advancements in microscopy technology have enabled researchers to visualize the charge distribution in materials down to subatomic length scales. In this Technical Review, we discuss the developments in high-resolution real-space charge distribution imaging using diffraction techniques and electron microscopy, with a focus on the recent advancement of four-dimensional scanning transmission electron microscopy, electron holography, and applications to materials interfaces.

Electronic properties and materials↗
DOE OSTI2021

Guiding the design of oxidation-resistant Fe-based single atom alloy catalysts with insights from configurational space

The high activity and selectivity of Fe-based heterogeneous catalysts toward a variety of reactions that require the breaking of strong bonds are offset in large part by their considerable instability with respect to oxidative deactivation. While it has been shown that the stability of Fe catalysts is considerably enhanced by alloying them with precious metals (even at the single-atom limit), rational design criteria for choosing such secondary metals are still missing. Since oxidative deactivation occurs due to the strong binding of oxygen to Fe and reduction by adsorbed hydrogen mitigates the deactivation, we propose here to use the binding affinity of oxygen and hydrogen adatoms as the basis for rational design. As it would also be beneficial to use cheaper secondary metals, we have scanned over a large subset of 3d–5d mid-to late transition metal single atoms and computationally determined their effect on the oxygen and hydrogen adlayer binding as a function of chemical potential and adsorbate coverage. We further determine the underlying chemical origins that are responsible for these effects and connect them to experimentally tunable quantities. Our results reveal a reliable periodic trend wherein oxygen binding is weakened greatest as one moves right and down the periodic table. Hydrogen binding shows the same trend only at high (but relevant) coverages and otherwise tends to have its binding slightly increased in all systems. Trends with secondary metal coverage are also uncovered and connected to experimentally tunable parameters.

Hensley, Alyssa↗
DOE OSTI2022

Basin Management of Geologic CO2 Storage: Effect of Well Spacing on CO2 Plume and Pressure Interference

This is the conference paper accompanying an oral presentation made at the Society of Petroleum Engineers (SPE) Western Regional Meeting (WRM) held in Bakersfield, California, April 26–28, 2022. The paper provides the results of a basin-scale CO2 storage modeling project investigating subsurface pressure interference among CO2 injection wells located in proximity to each other.

Wijaya, Nur↗
DOE OSTI2023

Double Diffusion Maps and their Latent Harmonics for scientific computations in latent space

In this work, we introduce a data-driven approach to building reduced dynamical models through manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold learning technique) on time series data. A second round of Diffusion Maps on those latent coordinates allows the approximation of the reduced dynamical models. This second round enables mapping the latent space coordinates back to the full ambient space (what is called lifting); it also enables the approximation of full state functions of interest in terms of the reduced coordinates. In our work, we develop and test three different reduced numerical simulation methodologies, either through pre-tabulation in the latent space and integration on the fly or by going back and forth between the ambient space and the latent space. The data-driven latent space simulation results, based on the three different approaches, are validated through (a) the latent space observation of the full simulation through the Nyström Extension formula, or through (b) lifting the reduced trajectory back to the full ambient space, via Latent Harmonics. Latent space modeling often involves additional regularization to favor certain properties of the space over others, and the mapping back to the ambient space is then constructed mostly independently from these properties; here, we use the same data-driven approach to construct the latent space and then map back to the ambient space.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The Quantum Condition Space

Abstract The fundamental properties of quantum physics are exploited to evaluate event probabilities with projection measurements. Next, to study what events can be specified by quantum methods, the concept of the condition space is introduced, which is found to be the dual space of the classical outcome space of bit strings. Like the classical outcome space generates the quantum state space, the condition space generates the quantum condition space being the central idea of this work. The quantum condition space permits the existence of entangled conditions having no classical equivalent. In addition, the quantum condition space is related to the quantum state space by a Fourier transform guaranteed by the Pontryagin duality, and therefore an entropic uncertainty principle can be defined. The quantum condition space offers a novel perspective of understanding quantum states with the duality picture. Furthermore, the quantum conditions have physical meanings and realizations of their own and thus may be studied for purposes beyond the original motivation of characterizing events for probability evaluation. Finally, the relation between the condition space and quantum circuits provides insights into how quantum states are collectively modified by quantum gates, which may lead to deeper understanding of the complexity of quantum circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A new database of building-space-specific internal loads and load schedules for performance based code compliance modeling of commercial buildings

Building-level loads and load profiles prescribed by current modeling rules save modelers time and avoid gaming during whole building performance modeling. However, recent studies show that they sometimes insufficiently capture the entire building performance due to the varied loads and load profiles for different space types. As a solution to this issue, this paper develops a database of building-space-specific loads and load profiles used in code compliance modeling. The existing sets of loads and load profiles are reviewed and the challenges behind using them for specific research topics are discussed. Then, the proposed method to develop the building-space-specific loads and load profiles is introduced. After that, the database for these building-space-specific loads and load profiles is presented. In addition, one case is studied to demonstrate the applications of these loads and load profiles. In this case study, three methods are used to develop building energy models: space-specific (using knowledge of the distribution and location of space types and applying the space-specific data in the developed database), building-level (assuming a lack of knowledge of the space types and using the building-level data in the developed database), and calculated-ratio (assuming knowledge of the distribution of space types but not their locations and calculating weighted average values based on the space-specific data in the developed database). Finally, the energy results simulated by using these three methods are compared, which show building-level methods can produce energy results up to 20% different than the space-specific methods. Finally, this paper discusses the application scope and maintenance of this new database.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗