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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Electrically driven proton transfer promotes Brønsted acid catalysis by orders of magnitude

Electric fields play a key role in enzymatic catalysis and can enhance reaction rates by 100,000-fold, but the same rate enhancements have yet to be achieved in thermochemical heterogeneous catalysis. In this work, we probe the influence of catalyst potential and interfacial electric fields on heterogeneous Brønsted acid catalysis. We observed that variations in applied potential of ~380 mV led to a 100,000-fold rate enhancement for 1-methylcyclopentanol dehydration, which was catalyzed by carbon-supported phosphotungstic acid. Mechanistic studies support a model in which the interfacial electrostatic potential drop drives quasi-equilibrated proton transfer to the adsorbed substrate prior to rate-limiting C–O bond cleavage. Large increases in rate with potential were also observed for the same reaction catalyzed by Ti/TiO y H x and for the Friedel Crafts acylation of anisole with acetic anhydride by carbon-supported phosphotungstic acid.

Science & Technology - Other Topics↗
DOE OSTI2022

Mechanistic Study of 1,2-Dichloroethane Hydrodechlorination on Cu-Rich Pt–Cu Alloys: Combining Reaction Kinetics Experiments with DFT Calculations and Microkinetic Modeling

Cu-rich Pt–Cu bimetallic catalysts are among the most promising candidates for actively catalyzing the hydrodechlorination of 1,2-dichloroethane (1,2-DCA) toward ethylene production. Combining reaction kinetics experiments with density functional theory (DFT) calculations and mean-field microkinetic modeling, we present a systematic mechanistic study for 1,2-DCA hydrodechlorination on Cu-rich Pt–Cu alloy catalysts. Our DFT (PBE+(TS+SCS)) results suggest that increasing Cu content in the Pt–Cu alloy destabilizes C 2 -species adsorption while stabilizing the binding of atomic chlorine. The reaction energetics of all the elementary steps in the 1,2-DCA reaction network were calculated on a Pt 1 Cu 3 (111) model surface. The DFT results were then used to construct a microkinetic model, and the model-predicted reaction rates were compared with our reaction kinetics experimental results on a Cu-rich SiO 2 -supported Pt–Cu alloy catalyst through a parameter estimation procedure. Both the reaction kinetics experiments and the microkinetic model after parameter adjustments yielded 100% selectivity to ethylene. The microkinetic model pointed to a reaction pathway involving two sequential chlorine-removal steps on the Pt–Cu alloy catalyst, a mechanism distinct from the one previously identified on pure Pt/SiO 2 catalysts, which involved an initial hydrogen-removal step. Adjustments to the DFT-derived parameters indicate the possible formation of chlorine-induced Cu-enriched surface sites during 1,2-DCA hydrodechlorination conditions, sites that are more active than those encountered in the bulk Pt 1 Cu 3 (111) alloy surface. Furthermore, our study offers valuable initial insights on the 1,2-DCA hydrodechlorination reaction mechanism and the nature of the active sites on PtCu bimetallic catalysts.

1,2-Dichloroethane↗
DOE OSTI2024

"Source Term Modeling for Advanced Gas Micro-Reactors"

Maintaining the safety of the public, environment, and operating personnel is the most important factor in designing, operating, maintaining, and decommissioning nuclear reactors. In recent years, there has been a growing interest in the development of micro-reactors employing TRi-structural ISOtropic (TRISO)-coated particle fuel. In gas reactors, TRISO fuel plays an important role in the safety case for high temperature reactors because of the fission product retention properties of the fuel. This ability enables the use of a functional containment strategy for the reactor where multiple barriers are used to prevent fission product release to the environment. Part of the safety analysis of these advanced reactors is the assessment of radionuclide releases under normal and accident conditions through the multiple credited safety barriers. Using conservative assumptions, a mechanistic analysis can be performed to quantify these releases that combines the probabilistic assessment of failure with analytic solutions to radionuclide transport equations. Source term modeling for TRISO fuel has been performed for previous reactor designs; however, these models are outdated, in many cases proprietary, and need updates to be applied to the current state of TRISO fuel technology and alternative gas reactor core configurations [1]. Currently, the only publicly available source term assessment for gas reactors is an expert-based Monte Carlo simulation based on the effectiveness of the fuel kernel, coating layers, and graphite block in a modular high temperature gas reactor [2]. Thus, there is a need to develop a simple, versatile, and mechanistic model of fission product release and transport in gas reactor cores that could be applied to a variety of reactors through user inputs and reactor-specific radionuclide inventories. The release is calculated by the diffusion of the key safety important fission products through the kernel, silicon carbide (SiC), graphite for both intact and defective TRISO particles based on fuel and graphite temperatures in the reactor under normal operation. These releases from the fuel enter the coolant where they can plate-out on cooler surfaces. A clean-up model is included for designs with a coolant purification system to remove fission gases. This initial distribution of fission products in the reactor serves as an initial condition for potential releases under postulated accident conditions. The model then can calculate the fission product release for any transient temperature profile and fission product releases can then be used to assess radiological dose to the workers and the public using conventional dose tools. Data on the diffusion of fission products is based on historic German TRISO experiments and the more current Department of Energy (DOE) Advanced Gas Reactor (AGR) TRISO fuel development program. The model is coded in python with inputs and outputs in excel spreadsheets, as well as python plotting utilities to aid in the interpretation of the results. References: [1] INL, NGNP Mechanistic Source Term White Paper, INL-10-17997, July 2010. [2] David A. Petti, Richard R. Hobbins, Peter Lowry, Hans Gougar, “Representative Source Terms and The Influence of Reactor Attributes on Functional Containment in Modular High Temperature Gas-cooled Reactors,” Nuclear Technology, Vol. 184, p. 181-197, Nov. 2013.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Functional Requirements for the Modeling and Simulation of Advanced (Non-LWR) Reactor Mechanistic Source Term

A number of industry vendors are leading a resurgence in advanced reactor development. As the assurance of public safety from the accidental release of radionuclides to the environment is central to regulatory licensing and the reactor design process, the development of a mechanistic source term (MST) assessment has been identified by the advanced reactor industry as a foremost priority. In contrast to historic light-water reactor (LWR) source term analysis efforts, an MST attempts to realistically model the release and transport of radionuclides from the source to the environment for specific scenarios, while accounting for retention or transmutation phenomena and associated uncertainties. This work seeks to aid in the development of MST modeling and simulation tools by performing a preliminary identification of functional requirements for the major advanced reactor types.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Mechanistic Insights into Molecular Copper Hydride Catalysis: the Kinetic Stability of CuH Monomers toward Aggregation is a Critical Parameter for Catalyst Performance

The activity of molecular copper hydride (CuH) complexes towards the selective insertion of unsaturated hydrocarbons under mild conditions has contributed significantly to versatile methodologies for upgrading these feedstocks. However, these catalysts are particularly susceptible to deleterious aggregation, leading to the depletion of active CuH species. Little is known about the mechanisms of CuH aggregation, how it influences overall catalyst performance, and how it can be controlled. We address these challenges with mechanistic studies on a model reaction of unactivated alkene hydroboration catalyzed by (IPr*CPh 3 )CuH (LCuH). Here, we report a comprehensive mechanistic investigation of this system, identifying an aggregation pathway that continuously depletes catalytically active LCuH to form inactive CuH clusters during turnover. Deactivation of LCuH is controlled primarily by the competition between the kinetics of the initial LCuH dimerization step and that of alkene insertion. We therefore propose that a more comprehensive understanding of CuH catalyst performance must account for the kinetics of the initial LCuH dimerization step, revising a previously explored thermodynamic understanding of CuH aggregation, where the concentration of active species is controlled by equilibria established between CuH dimers and monomers. With a series of (NHC)CuH congeners (NHC = N-heterocyclic carbene), we demonstrate that ostensibly minor structural modifications to the ligand peripheries can drastically affect the LCuH dimerization kinetics, while maintaining reactivity towards on–cycle alkene insertion. We employed a computational approach based on molecular dynamics simulations to provide an in-depth understanding of how specific structural ligand modifications can substantially increase the kinetic stability of monomeric CuH catalysts. Our combined experimental and computational studies suggest strategies for rational ligand design that can be broadly applied to molecular catalyst systems that are susceptible to deactivation via aggregation pathways.

Ryan, David E. [Pacific Northwest National Laborat↗
DOE OSTI2026

A Statistician’s Overview of Physics-Informed Neural Networks for Spatio-Temporal Data

The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of mechanistic information in predictive models. Statisticians and others have long been interested in this problem, which has led to several practical and innovative solutions dating back decades. In this overview, we focus on the problem of data-driven prediction and inference of dynamic spatio-temporal processes that include mechanistic information, such as would be available from partial differential equations, with a strong focus on the quantification of uncertainty associated with data, process, and parameters. Here, we give a brief review of several paradigms and focus our attention on Bayesian implementations given they naturally accommodate uncertainty quantification. We then show that it is straight-forward to include the Bayesian PINN (B-PINN) within the Bayesian hierarchical model (BHM) framework that has long been considered for modeling dynamic spatio-temporal processes. Such a BHM-PINN is illustrated via a simulation study in which a latent nonlinear Burgers’ equation PDE governs the dynamics of Poisson distributed spatio-temporal data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Bayesian↗
DOE OSTI2021

Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

Abstract Wetland methane (CH 4 ) emissions ( $${F}_{{{CH}}_{4}}$$ F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature using observations from the FLUXNET-CH 4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH 4 production are thus needed to improve global CH 4 budget assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Catalytic Conversion of Model Compounds of Plastic Pyrolysis Oil over ZSM-5

Mechanistic investigation of the catalytic conversion of model compounds for plastic pyrolysis oil (1-octene, octadiene, octane, and toluene) over ZSM-5 in a fixed-bed reactor was studied. 1-Octene breaks down into smaller olefins, which undergo further cracking, oligomerization, cyclization, and hydrogen transfer to eventually produce benzene, toluene, xylene (BTX), coke, and hydrogen. The effect of contact time on 1-octene conversion was further investigated and compared with thermodynamics analyses to elucidate the reaction network. Under the reaction conditions (500 oC, 1 atm), octadiene undergoes thermal coking, significantly contributing to reactor fouling. The products from octane cracking are similar to the products from 1-octene conversion whereas toluene undergoes disproportionation, dealkylation and coking. The analysis of spent catalyst showed long-chain hydrocarbons created by oligomerization reactions filled the pores and covered the surface of the catalyst. As a result, when mesoporous ZSM-5 is used instead of conventional, product selectivity is maintained for 70 hours in time-on-stream experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Development of a creep model informed by lower-length scale simulations to simulate creep in doped UO 2

Using molecular dynamics, we predict information at the atomistic scale used to develop a mechanistic UO 2 creep model for use in higher length-scale fuel performance codes. The ultimate objective of the model is to better describe the grain size dependence and therefore impact of doping on creep rates in UO 2 . In a previous NEAMS milestone, we found that Nabarro-Herring (bulk diffusional) creep was too low to capture the experimentally observed creep rates in standard UO 2 . Moreover, in that milestone, other mechanisms were explored, such as Coble (grain boundary) creep and dislocation climb, with each mechanism exhibiting different grain size dependencies. Again, these were orders of magnitude too low to describe the experimental creep rates. In this work, we address the previous assumptions made for the Coble creep mechanism by investigating the diffusivity of various defects at grain boundaries in UO 2 and, critically, to determine if enhanced grain boundary diffusivity allows the model to better reproduce experimental results. The diffusivity as a function of temperature for different concentrations of uranium vacancies and interstitials for bulk UO 2 have also been examined using cluster dynamics. Furthermore, using a concentration dependent segregation model, the concentration of defects at the grain boundary were predicted. This atomistic data was then input into the various creep mechanisms and the creep rates compared to the empirical MATPRO correlation (used in BISON) and experiment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Data-Driven Constitutive Model for the Inelastic Response of Metals: Application to 316H Steel

Here, predictions of the mechanical response of structural elements are conditioned by the accuracy of constitutive models used at the engineering length-scale. In this regard, a prospect of mechanistic crystal-plasticity-based constitutive models is that they could be used for extrapolation beyond regimes in which they are calibrated. However, their use for assessing the performance of a component is computationally onerous. To address this limitation, a new approach is proposed whereby a surrogate constitutive model (SM) of the inelastic response of 316H steel is derived from a mechanistic crystal plasticity-based polycrystal model tracking the evolution of dislocation densities on all slip systems. The latter is used to generate a database of the expected plastic response and dislocation content evolution associated with several instances of creep loading. From the database, a SM is developed. It relies on the use of orthogonal polynomial regression to describe the evolution of the dislocation content. The SM is then validated against predictions of the dead load creep response given by the polycrystal model across a range of temperatures and stresses. When the SM is used to predict the response of 316H during complex non monotonic loading, extrapolating to new loading conditions, it is found that predictions compare particularly well against those from the physics-based polycrystal model.

36 MATERIALS SCIENCE↗
DOE OSTI2025

From tides to seasons: How cyclic tidal drivers and plant physiology interact to affect carbon cycling at the terrestrial-estuarine boundary (Final technical report)

Coastal ecosystems are among the most biologically and biogeochemically active and diverse systems on Earth. Because they act as important linkages between terrestrial ecosystems and the open ocean, their incorporation in Earth system models (ESMs) is critical to predict coastal and global responses to environmental changes. However, they vary greatly in the magnitude of tides and the volume and timing of freshwater input from land, making it challenging to model the major biogeochemical reactions that control productivity and greenhouse gas emissions across coastal terrestrial aquatic interfaces (TAIs). Our overall objective was to improve mechanistic process understanding and modeling of tidal wetland hydro-biogeochemistry in coastal TAIs. We established a new flux tower site (Ameriflux US-PLo) in the oligohaline part of the Parker River to continuously monitor ecosystem-scale carbon fluxes under temporally varying salinity conditions. The site is co-located with long-term monitoring plots of the Plum Island Ecosystems LTER project. We installed wells and redox sensors in the marsh interior and creek bank, established biomass monitoring plots and deployed novel optode sensors in both locations. We used this data to parameterize plant-mediated transport in PFLOTRAN and tested the impact of soil heterogeneity on porewater constituents and gas fluxes. We collected observations of root oxygen release with a novel planar optode system in the field. Flux data collected during the measurement period encompasses a large variation in salinity ranging from drought to record precipitation years. We developed a method to extract functional relationships from the flux data using artificial neural networks, identifying salinity thresholds for CH 4 fluxes. Finally, we are using the coupled ELM-PFLOTRAN model to test the impact of antecedent hydrological conditions on the salinity-CH 4 flux relationship. This grant contributed to the professional development of one postdoc, three research assistants and one graduate student. The sensor data has been shared with external collaborators.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Smart culture medium optimization for recombinant protein production: Experimental, modeling, and AI/ML-driven strategies

Recombinant protein production (RPP) is central to biotechnology, where recombinant proteins are used as either end products or catalysts in the synthesis of chemicals, fuels, and materials. Among the major cost drivers, culture medium plays a pivotal role in determining protein yield and quality. This review presents a comprehensive perspective on the critical stages of “smart” culture medium optimization: planning, screening, modeling, optimization, and validation. In the planning stage, we examine the nutritional and energetic roles of medium components, including carbon, nitrogen, amino acids, salts, and trace metals, and their impacts on culture parameters such as pH, oxidative state, and osmolality. We highlight the variability in trace metal content due to water sources, culture vessels, and raw materials, which can substantially influence RPP. The screening stage covers Design of Experiments (DoE) approaches, assessing their theoretical basis, implementation, and limitations. For modeling, we describe methods that integrate experimental data to develop predictive models for smart medium formulation. Model-based optimization strategies can then be employed to select optimal media compositions for a given application. The validation stage aims to evaluate model predictions and provide feedback for model training and refinement. Finally, we survey mechanistic and artificial intelligence/machine learning (AI/ML)-driven models as integrated, transformational tools for predictive modeling of bioprocess conditions, nutrient availability, cellular metabolism, and protein quality, with the goal of optimizing culture media to enhance protein yields while reducing costs and environmental impact. We conclude by addressing the challenges of translating laboratory-scale medium optimization to industrial-scale settings and exploring future AI/ML-driven approaches that may overcome current bottlenecks and accelerate medium design for RPP. Overall, this review provides a unified framework for advancing smart medium design in RPP.

Artificial Intelligence/Machine Learning (AI/ML)↗
DOE OSTI2024

Metabolic model-based ecological modeling for probiotic design

The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter ‘probiotic’ treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual’s microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Sensitivity Study of Multiscale and Phenomenological Elasto-Viscoplastic Grade 91 Material Models for Component-Scale Response

Many advanced nuclear reactor concepts currently being developed are targeting higher operating temperatures relative to the current fleet of light water nuclear reactors, for efficiency gains and other operational considerations. The design of high temperature structural components with reliable long-term operational performance will depend on material models that accurately capture the inelastic deformation mechanisms active in these environments. In this work, we perform a detailed parameter sensitivity analysis of two unified elasto-viscoplastic Grade 91 material models capable of capturing long term high temperature creep deformation. The first model is a phenomelogical material model from the Nuclear Engineering Material Library (NEML) developed at Argonne National Lab. The NEML model parameters and their uncertainty were fit to a range of Grade 91 experimental data using Bayesian Markov Chain Monte Carlo analysis. The second model is a LAROMance data-driven surrogate material model developed at Los Alamos National Lab. The LAROMance model is fit to a large database of responses produced by a mechanistic crystal plasticity based polycrystal model. Parameters for the LAROMance surrogate material model reflect the pedigree of the Grade 91 microstructure. Both material models have been integrated into the Grizzly code, based on the open-source MOOSE multiphysics simulation framework, to simulate both the progression of aging mechanisms and the effects of that aging on nuclear power plant structures. Grizzly is used analyze a three-dimensional Grade 91 piping system to compare the long-term inelastic response predicted by these two fundamentally different models and assess the sensitivity of the material model input parameters on this quantity of interest.

42 ENGINEERING↗
DOE OSTI2021

Modeling Growth Kinetics, Interspecies Cell Fusion, and Metabolism of a Clostridium acetobutylicum / Clostridium ljungdahlii Syntrophic Coculture

ABSTRACT Clostridium acetobutylicum and Clostridium ljungdahlii grown in a syntrophic culture were recently shown to fuse membranes and exchange cytosolic contents, yielding hybrid cells with significant shifts in gene expression and growth phenotypes. Here, we introduce a dynamic genome-scale metabolic modeling framework to explore how cell fusion alters the growth phenotype and panel of metabolites produced by this binary community. Computational results indicate C. ljungdahlii persists in the coculture through proteome exchange during fusing events, which endow C. ljungdahlii cells with expanded substrate utilization, and access to additional reducing equivalents from C. acetobutylicum -evolved H 2 and through acquisition of C. acetobutylicum -native cofactor-reducing enzymes. Simulations predict maximum theoretical ethanol and isopropanol yields that are increased by 0.64 mmol and 0.39 mmol per mmol hexose sugar consumed, respectively, during exponential growth when cell fusion is active. This modeling effort provides a mechanistic explanation for the metabolic outcome of cellular fusion and altered homeostasis achieved in this syntrophic clostridial community. IMPORTANCE Widespread cell fusion and protein exchange between microbial organisms as observed in synthetic C. acetobutylicum / C. ljungdahlii culture is a novel observation that has not been explored in silico . The mechanisms responsible for the observed cell fusion events in this culture are still unknown. In this work, we develop a modeling framework that captures the observed culture composition and metabolic phenotype, use it to offer a mechanistic explanation for how the culture achieves homeostasis, and identify C. ljungdahlii as primary beneficiary of fusion events. The implications for the events described in this study are far reaching, with potential to reshape our understanding of microbial community behavior synthetically and in nature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Quantifying Drivers of Methane Hydrobiogeochemistry in a Tidal River Floodplain System

The influence of coastal ecosystems on global greenhouse gas (GHG) budgets and their response to increasing inundation and salinization remains poorly constrained. In this study, we have integrated an uncertainty quantification (UQ) and ensemble machine learning (ML) framework to identify and rank the most influential processes, properties, and conditions controlling methane behavior in a freshwater floodplain responding to recently restored seawater inundation. Our unique multivariate, multiyear, and multi-site dataset comprises tidal creek and floodplain porewater observations encompassing water level, salinity, pH, temperature, dissolved oxygen (DO), dissolved organic carbon (DOC), total dissolved nitrogen (TDN), partial pressure of carbon dioxide (pCO 2 ), nitrous oxide (pN 2 O), methane (pCH 4 ), and the stable isotopic composition of methane (δ 13 CH 4 ). Additionally, we incorporated topographical data, soil porosity, hydraulic conductivity, and water retention parameters for UQ analysis using a previously developed 3D variably saturated flow and transport floodplain model for a physical mechanistic understanding of factors influencing groundwater levels and salinity and, therefore, CH 4 . Principal component analysis revealed that groundwater level and salinity are the most significant predictors of overall biogeochemical variability. The ensemble ML models and UQ analyses identified DO, water level, salinity, and temperature as the most influential factors for porewater methane levels and indicated that approximately 80% of the total variability in hourly water levels and around 60% of the total variability in hourly salinity can be explained by permeability, creek water level, and two van Genuchten water retention function parameters: the air-entry suction parameter α and the pore size distribution parameter m. These findings provide insights on the physicochemical factors in methane behavior in coastal ecosystems and their representation in local- to global-scale Earth system models.

54 ENVIRONMENTAL SCIENCES↗