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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

A First-Passage Kinetic Monte Carlo method for reaction–drift–diffusion processes

Stochastic reaction–diffusion models are now a popular tool for studying physical systems in which both the explicit diffusion of molecules and noise in the chemical reaction process play important roles. The Smoluchowski diffusion-limited reaction model (SDLR) is one of several that have been used to study biological systems. Exact realizations of the underlying stochastic processes described by the SDLR model can be generated by the recently proposed First-Passage Kinetic Monte Carlo (FPKMC) method. This exactness relies on sampling analytical solutions to one and two-body diffusion equations in simplified protective domains. In this work we extend the FPKMC to allow for drift arising from fixed, background potentials. As the corresponding Fokker–Planck equations that describe the motion of each molecule can no longer be solved analytically, we develop a hybrid method that discretizes the protective domains. The discretization is chosen so that the drift–diffusion of each molecule within its protective domain is approximated by a continuous-time random walk on a lattice. New lattices are defined dynamically as the protective domains are updated, hence we will refer to our method as Dynamic Lattice FPKMC or DL-FPKMC. We focus primarily on the one-dimensional case in this manuscript, and demonstrate the numerical convergence and accuracy of our method in this case for both smooth and discontinuous potentials. We also present applications of our method, which illustrate the impact of drift on reaction kinetics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Kinetic modeling of evolution of 3 + 1:Resonance enhanced multiphoton ionization plasma in argon at low pressures

We present numerical kinetic modeling of generation and evolution of the plasma produced as a result of resonance enhanced multiphoton ionization (REMPI) in Argon gas. The particle-in-cell/Monte Carlo collision (PIC/MCC) simulations capture non-equilibrium effects in REMPI plasma expansion by considering the major collisional processes at the microscopic level: elastic scattering, electron impact ionization, ion charge exchange, and recombination and quenching for metastable excited atoms. The conditions in one-dimensional (1D) and two-dimensional (2D) formulations correspond to known experiments in Argon at a pressure of 5 Torr. The 1D PIC/MCC calculations are compared with the published results of local drift-diffusion model, obtained for the same conditions. It is shown that the PIC/MCC and diffusion-drift models are in qualitative and in reasonable quantitative agreement during the ambipolar expansion stage, whereas significant non-equilibrium exists during the first few 10 s of nanoseconds. 2D effects are important in the REMPI plasma expansion. The 2D PIC/MCC calculations produce significantly lower peak electron densities as compared to 1D and show a better agreement with experimentally measured microwave radiation scattering.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Measurement of atomic oxygen densities using TALIF on a dielectric barrier discharge: insights into the volume above a micro cavity plasma array

Dielectric barrier discharges, particularly micro cavity plasma arrays, offer significant potential for plasma-catalytic research due to their ability to ignite plasma in direct contact with a catalytic surface, enabling the observation of plasma-surface interactions. A key factor in their application is the generation of reactive species, such as atomic oxygen, within the cavities. These species can interact with both the surface (e.g. for activation or cleaning) and the gas being treated (e.g. for oxidation). Given the central role of oxygen atoms in plasma catalysis and their use as a model for more complex species, this work investigates the transport of these atoms out of the cavities. Two-photon absorption laser-induced fluorescence spectroscopy with picosecond laser excitation is performed in the volume above the cavities. The results are compared with a basic diffusion model. The reactor operates with a He/O 2 mixture at a flow rate of 1 slm and atmospheric pressure. Densities of up to 10 16 cm -3 are measured near the surface. Time-dependent measurements show that, at a distance of 350 µm from the surface, a density equilibrium is reached within less than 3 ms of reactor operation. Decay times due to ozone formation after the reactor is turned off are on a similar scale. Spatially resolved measurements show that the oxygen density decreases exponentially from the surface but remains detectable up to approximately 1 mm above the surface, indicating significant application potential. Variations in the O 2 admixture show a density maximum at 0.4%, confirming previous helium state enhanced actinometry measurements within the cavities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Chitosan Functionalization with Amino Acids Yields to Higher Copper Ions Adsorption Capacity

Chitosan (Chi) beads were conjugated with three different amino acids [namely, glutamic acid (GLU), methionine (MET), and taurine (TAU)] aiming to increase the divalent copper ions uptake in aqueous media. Scanning Electron Microscopy evidenced the development of a large porous structure after amino acid functionalization, associated with the increase in a number of amino groups in the polymer backbone. X-Ray Photoelectron Spectroscopy and Fourier-Transform Infrared Spectra analyses were also employed to assess the conjugation of these three different amino acids in chitosan backbone. Adsorption experiments were conducted in a batch process, at 298 K, and kinetic data indicated a slightly better fitting for the pseudo-first-order model when compared to pseudo-second order. Intraparticle diffusion model suggested a three-step mechanism for Cu(II) adsorption kinetics, limited by the third step, the intraparticle diffusion. The isotherm data fitting to the traditional Langmuir and Freundlich models indicated a better fit for the former case. The amino acid conjugation resulted in the increase of the maximum adsorption capacity for Cu(II) from 1.30 mmol g{sup −1} prior to amino acid conjugation to values as high as 2.31 mmol g{sup −1}, 2.40 mmol g{sup −1} and 2.68 mmol g{sup −1} for Chi–TAU, Chi–GLU, and Chi–MET, respectively. These results are attributed to the introduction of additional amino groups and new carboxylate and amino acid residues into the chitosan backbone, which might also be explored for amino acid demanding applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Diffusion coefficients in the envelopes of white dwarfs

The diffusion of elements is a key process in understanding the unusual surface composition of white dwarfs and their spectral evolution. The diffusion coefficients of Paquette et al. have been widely used to model diffusion in white dwarfs. In this work, we perform new calculations of the coefficients of interdiffusion and ionic thermal diffusion with (1) a more advanced model that uses a recent modification of the calculation of the collision integrals that is more suitable for the partially ionized, partially degenerate, and moderately coupled plasma and (2) classical molecular dynamics. The coefficients are evaluated for silicon and calcium in white dwarf envelopes of hydrogen and helium. A comparison of our results with Paquette et al. shows that the latter systematically underestimates the coefficient of interdiffusion yet provides reliable estimates for the relatively weakly coupled plasmas found in nearly all types of stars, as well as in white dwarfs with hydrogen envelopes. In white dwarfs with cool helium envelopes (${T}_{\mathrm{eff}}$ < 15,000 K), the difference grows to more than a factor of two. We also explored the effect of the ionization model used to determine the charges of the ions and found that it can be a substantial source of discrepancy between different calculations. Finally, we consider the relative diffusion timescales of Si and Ca in the context of the pollution of white dwarf photospheres by accreted planetesimals and find factor of ≳3 differences between calculations based on Paquette et al. and our model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Interpretable Deep Learning for Advancing Field-Enhanced Catalysis

This DOE Early Career project developed a physics-informed, interpretable AI-and-modeling framework to understand and exploit electric-field effects in heterogeneous catalysis, with ammonia cracking and synthesis as a representative pathway. The team built and validated methods to map local electric fields on metal surfaces and nanoparticles, showing that low-coordination features (tips/edges/corners) can concentrate fields by several-fold relative to flat facets. Using DFT-generated datasets, the project created physics-guided machine learning models that rapidly predict local electric fields and field-dependent adsorption energetics with near-DFT accuracy while reducing computational cost by orders of magnitude. These predictions were integrated with microkinetic modeling to quantify how field-dipole interactions reshape reaction energetics and mechanisms, enabling large increases in predicted catalytic rates and substantial reductions in operating temperature under favorable field conditions. To accelerate discovery of earth-abundant catalysts, the project combined interpretable ML screening (with electronic-structure descriptors identified as key drivers) with a generative inverse-design workflow based on diffusion models and physics constraints. The resulting closed-loop approach, linking simulation, mechanistic modeling, and AI, provides reusable tools and datasets for designing catalysts and operating conditions in field-enhanced catalysis, with broad relevance to electrostatic catalysis, plasma catalysis, electrocatalysis, and other energy-related chemical transformations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Investigation of Molecular Diffusion at Block Copolymer Thin Films Using Maximum Entropy Method-Based Fluorescence Correlation Spectroscopy and Single Molecule Tracking

Fluorescence correlation spectroscopy (FCS) has been widely used to investigate molecular diffusion behavior in various samples. The use of the maximum entropy method (MEM) for FCS data analysis provides a unique means to determine multiple distinct diffusion coefficients without a priori assumption of their number. Comparison of the MEM-based FCS method (MEM-FCS) with another method will reveal its utility and advantage as an analytical tool to investigate diffusion dynamics. Herein, we measured diffusion of fluorescent probes doped into nanostructured thin films using MEM-FCS, and validated the results with single molecule tracking (SMT) data. The efficacy of the MEM code employed was first demonstrated by analyzing simulated FCS data for systems incorporating one and two diffusion modes with broadly distributed diffusion coefficients. The MEM analysis accurately afforded the number of distinct diffusion modes and their mean diffusion coefficients. These results contrasted with those obtained by fitting the simulated data to conventional two-component and anomalous diffusion models, which yielded inaccurate estimates of the diffusion coefficients. Subsequently, the MEM analysis was applied to FCS data acquired from hydrophilic dye molecules incorporated into microphase-separated polystyrene-block-poly(ethylene oxide) (PS-b-PEO) thin films characterized under a water-saturated nitrogen atmosphere. The MEM analysis revealed distinct fast and slow diffusion components attributable to molecules diffusing on the film surface and inside the film, respectively. SMT studies of the same materials yielded trajectories for mobile molecules that appear to follow the curved PEO microdomains. Diffusion coefficients obtained from the SMT data were consistent with those obtained for the slow diffusion component detected by MEM-FCS. Furthermore, these results highlight the utility of MEM-FCS and SMT for gaining complementary information on molecular diffusion processes in heterogeneous material systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Modeling, control, and dynamic performance analysis of a reverse osmosis desalination plant integrated within hybrid energy systems

An RO (reverse osmosis) desalination plant is proposed as an effective, FLR (flexible load resource) to be integrated into HES (hybrid energy systems) to support various types of ancillary services to the electric grid, under variable operating conditions. To study the dynamic (transient) analysis of such system, among the various unit operations within HES, special attention is given here to the detailed dynamic modeling and control design of RO desalination process with a spiral-wound membrane module. The model incorporates key physical phenomena that have been investigated individually into a dynamic integrated model framework. In particular, the solution-diffusion model modified with the concentration polarization theory is applied to predict RO performance over a large range of operating conditions. Simulation results involving several case studies suggest that an RO desalination plant, acting as a FLR, can provide operational flexibility to participate in energy management at the utility scale by dynamically optimizing the use of excess electrical energy. Here, the incorporation of additional commodity (fresh water) produced from a FLR allows a broader range of HES operations for maximizing overall system performance and profitability. For the purpose of assessing the incorporation of health assessment into process operations, an online condition monitoring approach for RO membrane fouling supervision is addressed in the case study presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Band tail recombination in polymer:fullerene organic solar cells

Recombination through band tail localized states is studied analytically and by measurement of the forward-bias dark current as a function of temperature in three different organic bulk-heterojunction solar cells. The Shockley-Read-Hall mechanism is analyzed for the specific case of recombination between mobile carriers and an exponential distribution of localized band tail states. The analysis gives a simple relation between the dark current ideality factor and the band tail slope. Assumptions of the model are verified by numerical drift-diffusion modeling. Diode current-voltage measurements give good agreement with the analytical model, confirming that the band tail recombination mechanism applies to at least some organic solar cells. Deep traps provide a secondary recombination channel in some devices.

14 SOLAR ENERGY↗
DOE OSTI2023

Photoinduced structural recovery dynamics of rare-earth nickelate thin films

Photoinduced structural dynamics of rare-earth nickelate thin films, NdNiO 3 and SmNiO 3 , grown on (001) oriented SrTiO 3 were studied using time-resolved x-ray diffraction. The evolution of the (002) Bragg peak was tracked following laser excitation. The recovery pathways were found to be strongly dependent on laser fluence for NdNiO 3 and distinct for the two rare-earth nickelates. The recovery of the (002) peak shifts was modeled using a one-dimensional thermal diffusion model which showed that the recovery processes are non-thermal at high fluences. For NdNiO 3 , the timescales for the recovery of the (002) peak shift were found to be closely related to Ni magnetism recovery, potentially indicating magneto-structural coupling. Moreover, the evolution of integrated intensity and full-width-half-maximum points towards the presence of a structural phase separation during recovery. In conclusion, our studies highlight structural recovery pathways in nickelates by comparing the distinct responses of the photoinduced metal-insulator transition in NdNiO 3 and SmNiO 3 .

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quartz-hosted inclusions and embayments reveal storage, fluxing, and ascent of the Mesa Falls Tuff, Yellowstone

Quartz-hosted glasses from the Mesa Falls Tuff provide a geochemical window into the pre-eruptive magmatic system from one of Yellowstone's largest-volume caldera-forming eruptions. H 2 O and CO 2 concentrations, along with major and trace elements, were measured in both fully enclosed glass inclusions and partially enclosed embayments in the same quartz crystals. Major elements are largely consistent between the inclusions and embayments, except for K 2 O and Na 2 O. Of note, K 2 O is enriched by ~1 wt.% in embayment interiors relative to inclusions. Most trace elements are also enriched in the embayment interiors compared to inclusions from the same crystals. Fractionation trends of trace elements are consistent with ~30-60% crystallization. Quartz-hosted glass inclusions preserve 3.1±0.9 wt.% H 2 O and 493±227 ppm CO 2 whereas embayment interiors have 0.9±0.1 wt.% H 2 O and 399±229 ppm CO 2 . The CO 2 is roughly similar, but the distinct ~2 wt.% discrepancy between inclusion and embayment interior H 2 O contents may have been produced by CO 2 fluxing sourced from underplated Yellowstone basalts. H 2 O gradients within embayments are flat in their interiors and modified by sharp positive gradients near embayment exteriors which were produced by post-eruptive rehydration. CO 2 gradients occur as gently sloping concentration gradients that extend inward 150 to 250 μm from the embayment exterior. Finally, finite-difference 1D and 2D diffusion modeling indicates the distribution of H 2 O and CO 2 in embayments was produced by slow, fluid-saturated decompression that preceded rapid ascent during the caldera-forming eruption.

2D diffusion modeling↗
DOE OSTI2017

Turbulent mixing and removal of ozone within an Amazon rainforest canopy

Simultaneous profiles of turbulence statistics and mean ozone mixing ratio are used to establish a relation between eddy diffusivity and ozone mixing within the Amazon forest. A one–dimensional diffusion model is proposed and used to infer mixing time scales from the eddy diffusivity profiles. Data and model results indicate that during daytime conditions, the upper (lower) half of the canopy is well (partially) mixed most of the time and that most of the vertical extent of the forest can be mixed in less than an hour. During nighttime, most of the canopy is predominantly poorly mixed, except for periods with bursts of intermittent turbulence. Even though turbulence is faster than chemistry during daytime, both processes have comparable time scales in the lower canopy layers during nighttime conditions. Nonchemical loss time scales (associated with stomatal uptake and dry deposition) for the entire forest are comparable to turbulent mixing time scale in the lower canopy during the day and in the entire canopy during the night, indicating a tight coupling between turbulent transport and dry deposition and stomatal uptake processes. Because of the significant time of day and height variability of the turbulent mixing time scale inside the canopy, it is important to take it into account when studying chemical and biophysical processes happening in the forest environment. In conclusion, the method proposed here to estimate turbulent mixing time scales is a reliable alternative to currently used models, especially for situations in which the vertical distribution of the time scale is relevant.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Thermal diffusion, exhaust gas recirculation and blending effects on lean premixed hydrogen flames

Thermodiffusively-unstable lean premixed hydrogen flames are investigated using two-dimensional direct numerical simulation employing finite-rate chemical kinetics. Three databases are generated focussing on the inclusion of the Soret effect, the recirculation of exhaust gas, and blending with methane. A simple rescaling of a classic thermal diffusion model is presented and shown to mimic multicomponent diffusion with very low computational cost and little-to-no loss in accuracy. It is also shown that a previously developed model for mean local flame speeds in lean premixed hydrogen flames can still be used provided Soret effects are taken into account in one-dimensional calculations. The addition of exhaust gas to the unburned mixture is found to enhance thermodiffusive instability; the primary mechanism for this was shown to be the highly-efficient third-body nature of water, with the reduction of adiabatic flame temperature a second-order effect. Again, the existing mean local flame speed model proved sufficient. Finally, blending with methane was found to reduce the thermodiffusive response of the flame, more so than the existing model suggests, despite adjustment of the fuel Lewis number; an adapted model is presented to account for this.

08 HYDROGEN↗
DOE OSTI2020

Insights into the Water Transport Mechanism in Polymeric Membranes from Neutron Scattering

Polymeric membranes are ubiquitous in transport applications including gas separation, water desalination, solid-state batteries, and fuel cells. The transport mechanism in certain classes of membranes is well understood. It is generally believed that polymeric membranes used in gas separation follow the solution-diffusion model. However, the transport mechanism in other membranes such as the ones used in water desalination is less clear as both the solution-diffusion and hydrodynamic models have been proposed. In this contribution, we study the structure as well as the water and polymer dynamics of several polymeric membranes using small-angle and quasi-elastic neutron scattering. We demonstrate that on the scale of a few nanometers, water appears to diffuse through a desalination membrane at a rate comparable to bulk water, while water diffuses at a rate that is a factor of 2 slower in an anion-exchange membrane. These results illustrate the importance of studying both the structure and dynamics of membrane materials and have important implications in understanding the transport mechanisms that differentiate the different types of polymeric membranes.

36 MATERIALS SCIENCE↗
DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗
DOE OSTI2015

Numerical simulation of cathode plasma dynamics in magnetically insulated vacuum transmission lines

A novel algorithm for the simulation of cathode plasmas in particle-in-cell codes is described and applied to investigate cathode plasma evolution in magnetically insulated transmission lines (MITLs). The MITL electron sheath is modeled by a fully kinetic electron species. Electron and ion macroparticles, both modeled as fluid species, form a dense plasma which is initially localized at the cathode surface. Energetic plasma electron particles can be converted to kinetic electrons to resupply the electron flux at the plasma edge (the “effective” cathode). Using this model, we compare results for the time evolution of the cathode plasma and MITL electron flow with a simplified (isothermal) diffusion model. Simulations in 1D show a slow diffusive expansion of the plasma from the cathode surface. But in multiple dimensions, the plasma can expand much more rapidly due to anomalous diffusion caused by an instability due to the strong coupling of a transverse magnetic mode in the electron sheath with the expanding resistive plasma layer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗