SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Preprocessing and identification algorithms can boost the quality and reproducibility of results in untargeted ion mobility (IM)-MS workflows. For this CRADA we developed two methods for pre-processing to improve the quality of Agilent metabolomics data: i) a new saturation repair preprocessing method and ii) multi-dimensional smoothing.
Understanding and controlling the optoelectronic properties of organic semiconductors at the molecular level remains a challenge due to the complexity of chemical structures and intermolecular interactions. A common strategy to address this challenge is to utilize both experimental and computational approaches. In this contribution, we show that density functional theory (DFT) calculation is a useful tool to provide insights into the bonding, electron population distribution, and optical transitions of adducts between conjugated molecules and Lewis acids (CM-LA). Adduct formation leads to relevant modifications of key properties, including a red shift in optical transitions and an increase in charge carrier density and charge mobility, compared to the parent conjugated molecules. We show that electron density transfer from the CM to the LA, which was hypothesized to cause the experimental red shift in absorption spectra upon LA binding, can be quantified and interpreted by population analysis. Experimental red shifts in optical transitions for all molecular families can also be predicted by time dependent DFT calculations with different density functionals. Finally, these detailed insights help to optimize a priori design guidelines for future applications.
Solid-state ion shuttles are of broad interest in electrochemical devices, nonvolatile memory, neuromorphic computing, and biomimicry utilizing synthetic membranes. Traditional design approaches are primarily based on substitutional doping of dissimilar valent cations in a solid lattice, which has inherent limits on dopant concentration and thereby ionic conductivity. Here, we demonstrate perovskite nickelates as Li-ion shuttles with simultaneous suppression of electronic transport via Mott transition. Electrochemically lithiated SmNiO 3 (Li-SNO) contains a large amount of mobile Li + located in interstitial sites of the perovskite approaching one dopant ion per unit cell. A significant lattice expansion associated with interstitial doping allows for fast Li + conduction with reduced activation energy. We further present a generalization of this approach with results on other rare-earth perovskite nickelates as well as dopants such as Na + . The results highlight the potential of quantum materials and emergent physics in design of ion conductors.
Bacterial genes for molybdenum-containing and tungsten-containing enzymes are often differentially regulated depending on the metal availability in the environment. Here in this paper, we describe a new family of transcription factors with an unusual DNA-binding domain related to excisionases of bacteriophages. These transcription factors are associated with genes for various molybdate and tungstate-specific transporting systems as well as molybdo/tungsto-enzymes in a wide range of bacterial genomes. We used a combination of computational and experimental techniques to study a member of the TF family, named TaoR (for tungsten-containing aldehyde oxidoreductase regulator). In Desulfovibrio vulgaris Hildenborough, a model bacterium for sulfate reduction studies, TaoR activates expression of aldehyde oxidoreductase aor and represses tungsten-specific ABC-type transporter tupABC genes under tungsten-replete conditions. TaoR binding sites at aor promoter were identified by electrophoretic mobility shift assay and DNase I footprinting. We also reconstructed TaoR regulons in forty five Deltaproteobacteria by comparative genomics approach and predicted target genes for TaoR family members in other Proteobacteria and Firmicutes.
A combination of machine learned interatomic potentials (MLIPs) and enhanced sampling simulations is used to investigate the activation of methane on a Ni(111) surface. The work entails the development and iterative refinement of MLIPs, initially trained on a dataset constructed via ab initio molecular dynamics (AIMD) simulations, supplemented by adaptive biasing forces, to enrich the sampling of catalytically relevant configurations. Our results reveal that by incorporating collective variables that capture the behavior of the reactant molecule, as well as additional frames that describe the dynamic response of the catalytic surface, it is possible to enhance considerably the accuracy of predicted energies and forces. By employing enhanced sampling schemes in the refinement of the MLIP, we systematically explore the potential energy surface, leading to a refined MLIP capable of predicting DFT-level energies and forces and replicating key geometric characteristics of the catalytic system. The resulting free energy landscapes at several temperatures provide a detailed view of the thermodynamics and dynamics of methane activation. Specifically, as methane approaches and dissociates on the catalytic surface, the process involves the dynamic interplay of CH 4 and the Ni catalyst that includes both enthalpic and entropic contributions. The progression towards the transition state involves an CH 4 moiety that is increasingly restrained in its ability to rotate or translate, while the stage following the transition state is characterized by a notable rise of the Ni atom that interacts with the cleaved C–H bond. Furthermore, this leads to an increase in the mobility of the adsorbed species, a feature that becomes more pronounced at higher temperatures.
Electron-phonon interactions (EPIs) are presumably detrimental for thermoelectric performance in semiconductors because they limit carrier mobility. Here we show that enhanced EPIs with strong energy dependence offer an intrinsic pathway to a significant increase in the Seebeck coefficient and the thermoelectric power factor, particularly in the context of two-dimensional (2D) graphene-like Dirac bands. The increase is realized by enabling electron energy filtering through preferential scattering of electron/hole carriers. We prove this concept by implementing first-principles computational methods with explicit treatment of EPIs for a 2D gapless MoS 2 allotrope, which has both massless Dirac bands and a heavy-fermion state that acts as the filter. We determine that the optimal location of the heavy state and hence the onset of the filtering process is at the Dirac point. Our study opens an avenue for attaining ultrahigh power factors via engineering the EPIs in graphene-like semimetals or identifying new compounds that intrinsically possess the featured electronic structure.
FIGS is a neural network software that ingests real time synchronized field data on environmental flow fields and turbulence and gas concentration variations at high frequency and uses an error minimization algorithm to locate the gas source and quantify its strength. The software can be interfaced with atmospheric, oceanic and subsurface instruments in a variety of platforms stationary or mobile (e.g. cars, UAVs, submersible vehicles or boreholes) and used to find gas sources by smart use of data and phenomenology. FIGS can be trained by phenomenological model of the flow fields in the environment of interest and/or be calibrated by controlled release. After initial deployment the FIGS learning will grow with time as it accumulates data on source quantification. FIGS can be installed on any computer from small beagle-bones for field deployment/end-use to PC/MACs/main-frame for training/analysis. FIGS has been trained (using LANL's high resolution atmospheric simulations) and calibrated, tested and evaluated in the field and shown to perform well in finding and quantifying methane leaks at 10-100m scales at well pads by ingesting atmospheric measurements. The code is applicable to gas and particle source location at large scales.
A combined experimental and computational evaluation of damage accumulation in ion-irradiated Ni, NiFe, and NiFeCoCr is presented. Furthermore, a suppressed damage accumulation, at early stages (low-fluence irradiation), is revealed in NiFeCoCr, with a linear dependence as a function of ion fluence, in sharp contrast with Ni and NiFe. This effect, observed at 16 K, is attributed to the complex energy landscape in these alloys that limits defect mobility and therefore enhances defect interaction and recombination. Our results, together with previous room-temperature and high-temperature investigations, suggest "self-healing" as an intrinsic property of complex alloys that is not a thermally activated process.
We examined the electrosorption and ion dynamics of imidazolium-based room temperature ionic liquids (RTILs) having short (3-carbon, C3mim + ) and long (12-carbon, C12mim + ) cations, that is, 1-propyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (C3mimTFSI) and 1-dodecyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (C12mimTFSI), confined in ordered mesoporous carbon (OMC) and analyzed the influence of the cation alkyl chain length on the ion dynamics and the capacitive behavior using electrochemical measurements together with quasi-elastic neutron scattering (QENS) observations and classical density functional theory (cDFT) computations. Electrochemical tests highlighted the significant influence of specific applied potentials on accumulated charge storage densities and on the limits of saturation of larger electrolytes in the pores. Computational analyses corroborated these findings and predicted a 16% increase in the capacitance of the smaller-cation electrolyte under high applied potentials. However, QENS experiments revealed a behavior of decoupling of alkyl chain dynamics from the ring in electrolytes with larger ions. cDFT calculations identified density spikes for C12mim + away from the pore walls to further corroborate this unique behavior. Here, our insights into chain length-dependent dynamics and electrosorption in complex electrolyte-electrode systems deepen fundamental understanding of confined RTIL electrolyte behavior in the porous carbon electrodes.
Due to the compatibility with the well-developed Si-based semiconductor industry, there is considerable interest in developing silicon structures with direct energy band gaps for effective sunlight harvesting. In this paper, using silicon triangles as the building block, we propose a new silicon allotrope with a direct band gap of 0.61 eV, which is dynamically, thermally and mechanically stable. Symmetry group analysis further suggests that dipole transition at the direct band gap is allowed. Additionally, this new allotrope displays large carrier mobility (~10 4 cm/V · s) at room temperature and a low mass density (1.71 g/cm 3 ), making it a promising material for optoelectronic applications.
The mobility of dislocations is an important factor in understanding material strength. Dislocations experience a drag due to their interaction with the crystal structure, the dominating contribution at high stress and temperature being the scattering off phonons due to phonon wind. Yet, the velocity dependence of this effect has eluded a good theoretical understanding. In a previous work, dislocation drag from phonon wind as a function of velocity was computed from first principles in the isotropic limit, in part for simplicity, but also arguing that macroscopically, a polycrystalline metal looks isotropic. However, since the single crystal grains are typically a few microns up to a millimeter in size, dislocations travel in single crystals and cross boundaries, but never actually see an isotropic material. In this work we therefore highlight the effect of crystal anisotropy on dislocation drag by accounting for the crystal and slip plane geometries. In particular, we keep the phonon spectrum isotropic for simplicity, but dislocations are modeled according to the crystal symmetry (bcc, fcc, hcp, etc.). We then compare to the earlier purely isotropic results, as well as to experimental data and MD simulations where they are available.
The recently synthesized monolayer fullerene network in a quasi-hexagonal phase (qHP-C 60 ) exhibits superior electron mobility and optoelectronic properties compared to molecular fullerene (C 60 ), making it highly promising for a variety of applications. However, the microscopic carrier dynamics of qHP-C 60 remain unclear, particularly in realistic environments, which are of significant importance for applications in optoelectronic devices. Unfortunately, traditional ab initio methods are prohibitive for capturing the real-time carrier dynamics of such large systems due to their high computational cost. In this work, we present the first real-time electron–nuclear dynamics study of qHP-C 60 using velocity-gauge density functional tight binding, which enables us to perform several picoseconds of excited-state electron–nuclear dynamics simulations for nanoscale systems with periodic boundary conditions. When applied to C 60 , qHP-C 60 , and their solvated counterparts, we demonstrate that water/moisture significantly increases the electron–hole recombination time in C 60 but has little impact on qHP-C 60 . Our excited-state electron–nuclear dynamics calculations show that qHP-C 60 is extremely unique and enable exploration of time-resolved dynamics for understanding excited-state processes of large systems in complex, solvated environments.
Vacancy-mediated diffusion barriers in 316 stainless steel have been systematically calculated using density functional theory to provide essential parameters for mesoscale microstructure evolution models. A statistical sampling approach employing 210 nudged elastic band calculations across multiple special quasi-random structures captures the effects of local chemical environments in this concentrated alloy. The computational methodology addresses challenges specific to chemically disordered systems, including proper magnetic treatment throughout multi-step calculations and validation against experimental structural properties. The calculated activation barriers reveal clear species-dependent diffusion behavior with the hierarchy Ni >> Fe ˜ Cr >> Mo. Nickel exhibits the highest barriers (0.74–1.31 eV, mean 1.045 eV), confirming its role as the slowest-diffusing major component. Iron and chromium show similar moderate barriers averaging 0.587 eV and 0.522 eV, respectively. Remarkably, molybdenum demonstrates exceptionally low barriers (0.12–0.28 eV, mean 0.194 eV), suggesting much higher mobility than previously recognized and potentially significant implications for precipitation kinetics and microstructure evolution. The barrier ranges remain consistent across different 316 SS compositions, supporting parameter transferability for modeling applications. The overall mean barrier of 0.64 eV provides a practical approximation for phase field simulations, while species-specific values enable detailed treatments of diffusion-controlled processes. This systematic approach establishes a validated framework for generating diffusion parameters in other concentrated alloys where experimental data are limited, while providing the first systematic set of species-specific barriers for predictive modeling of 316 stainless steel microstructure evolution.
Electron-phonon (e–ph) interactions are usually treated in the lowest order of perturbation theory. Here we derive next-to-leading order e–ph interactions, and compute from first principles the associated electron-two-phonon (2ph) scattering rates. The derivations involve Matsubara sums of two-loop Feynman diagrams, and the numerical calculations are challenging as they involve Brillouin zone integrals over two crystal momenta and depend critically on the intermediate state lifetimes. Using Monte Carlo integration together with a self-consistent update of the intermediate state lifetimes, we compute and converge the 2ph scattering rates, and analyze their energy and temperature dependence. We apply our method to GaAs, a weakly polar semiconductor with dominant optical-mode long-range e–ph interactions. We find that the 2ph scattering rates are as large as nearly half the value of the one-phonon rates, and that including the 2ph processes is necessary to accurately predict the electron mobility in GaAs from first principles.
This study focuses on investigation of the distribution of Fe plaque in the root epidermis of the selected wetland plant species (Phragmites australis, Typha latifolia and Spartina alterniflora) using synchrotron X-ray microfluoresces, X-ray absorption near edge structure and transmission X-ray microscope techniques with (sub)micro-scale resolution. The wetland plants were collected in Liberty State Park, New Jersey, USA, and Yangtze River intertidal zone, Shanghai, China, respectively, during the different time period. Although a number of early studies have reported that Fe-oxides can precipitate on the surface of aquatic plants in the rhizosphere to form iron plaque, the role of Fe plaque in regulating metal biogeochemical cycle has been in discussion for decades. The results from this study show that Fe is mainly distributed in the epidermis non-uniformly, and the major Fe species is ferric Fe (Fe3+). This information is needed to make broad inferences about the relevant plant metal uptake mechanisms because Fe accumulation and distribution in the root system is important to understanding the metal transport processes that control the mobility of metals in plants. This study improves our understanding of Fe plaque distributions and speciation in the wetland plant root system, and helps us to understand the function of Fe plaque in metal transport and accumulation through the root system.
A new type of safe, small, transportable nuclear reactor would address the intense and ever-growing global demand for energy produced via a resilient, carbon-free energy source. In this regard, transportable small modular reactors (SMRs) are being designed and developed for electricity generation within small/micro-grid/off-grid isolated systems, as well as for heat generation in industrial/residential applications. These reactors feature the capability to be fully factory fabricated and then directly transported to utilities’ sites as “plug-and-play” systems. Research and development (R&D) programs are underway at Idaho National Laboratory (INL) to successfully design, develop, and demonstrate such safe-by-design mobile reactor technologies, in collaboration with partner organizations. Multiscale experimental facilities and multiphysics simulation tools are required for reactor design verification and validation (V&V), and licensing. These advanced reactors are intended to feature passive safety systems such as passive containment cooling systems (PCCS), which consist of multiphase flows and multispecies distributions. This seminar talk will focus on designing and analyzing transportable SMR PCCS by using multiscale experiments and multiphysics computational fluid dynamics (CFD) simulations to support reactor licensing and safety. The corresponding research challenges are addressed via supportive verification and validation results generated by the models and simulation tools in combination with selective parametric and uncertainty analysis. This solution approach could blaze the trail for commercial adoption of such technologies. The facilities, simulation capabilities, and research opportunities available at INL in regard to such reactors and the integrated energy systems with which they go hand in hand are also discussed briefly, and may spark interest in deeper research as well as new collaborative projects.
Long-term monitoring of contaminant transport in groundwater is expected to account for a large fraction of future life-cycle cleanup costs at the DOE sites. The Attenuation-Based Remedies in the Subsurface Applied Field Research Initiative (ABRS AFRI) is developing an innovative approach for cost-effective in situ long-term monitoring. The approach is based on strategically adding the measurements of controlling variables (such as pH, redox potential, electrical conductivity, and groundwater level), which control the plume mobility and its spatial and temporal distribution. In situ measurements of these variables - supplemented with a reduced number of standard periodic groundwater samples - are expected to lead to more cost-effective monitoring, and can also serve as an early warning system for detecting unexpected plume migration. The objective of this study is to support the development of such a long-term monitoring approach through the application of advanced computational methods, including (1) statistical data mining and analysis, and (2) three-dimensional flow and reactive chemical transport modeling. We have performed the statistical data analysis of historical and current monitoring data at the Savannah River Site (SRS) F-Area to quantify the correlations between the controlling variables and radioactive contaminant concentrations. In parallel, we have used 3D modeling of flow and contaminant transport to provide the prediction of the contaminant plume evolution. The results of both data analysis and modeling confirmed that the correlations between controlling variables and contaminant concentrations are significant. Modeling results also suggest that the correlation parameters will change in the future, which is important to assess the long-term efficacy of the proposed monitoring approach. (authors)
Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. Further, in this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.