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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Multi‐Omics Analyses Reveal Divergent Molecular Mechanisms Underlying Plant Biomass Conversion by Five Fungi

Fungal plant biomass conversion (FPBC) is of great importance to the global carbon cycle and has been increasingly applied for the production of biofuel and biochemicals from lignocellulose. However, the comprehensive understanding of relevant molecular mechanisms in different fungi remains challenging. Here, we comparatively analyzed the transcriptome, proteome and metabolome profile of four ascomycetes and one basidiomycete fungi during their growth on two common agricultural feedstocks (soybean hulls and corn stover). We revealed strong time‐, substrate‐ and species‐specific responses at multi‐omics levels for the tested fungi, highlighting species‐specific carbon utilization approaches and evolutionary adaptation to environmental niches. Notably, a remarkable expressional diversity of lignocellulose degrading enzymes, sugar transporter and metabolic genes, as well as industrially relevant metabolites were identified across different fungi and cultivation conditions. The findings improves our understanding of complex molecular networks underlying FPBC and fungal ecological roles, offering novel insights that can guide future genetic engineering of fungi for valorization of agriculture waste into value‐added bioproducts.

CAZy↗
DOE OSTI2024

Coupled Thermo-Hydro-Mechanical Processes in Fractured Rocks: Some Past Scientific Highlights and Future Research Directions

Abstract Coupled thermo-hydro-mechanical (THM) processes in fractured rocks have been a topic of intense scientific research for more than 30 years. The present paper takes a look into the past and highlights some scientific advances which are of an unusual “out-of-the-box” nature, and then looks forward and discusses possible directions of future research in this interesting field of study. Concerning future research directions, we see a trend from a focus on coupled THM processes in single fractures or a few interacting fractures, to the study of coupled THM behavior in complex fracture network systems where the fractures act collectively giving rise to local stress concentration points and points of large pressure gradients. Three examples of future research directions are presented. First is an effort towards identifying characterizing parameters of a fracture network that play a direct controlling role in major coupled THM phenomena (such as induced seismicity and flow channeling), rather than parameters of stochastic distributions of fractures in the network. The second example of research direction is accounting for the heterogeneity and hierarchy of fractures in a fault or fracture zone which has been associated with major THM events in a number of geo-energy projects. The third example is at the opposite end of the first; here it is recognized that in some cases, the coupled THM processes in fractured rocks may be controlled dominantly by only a few key bridges. Identification, characterization, and evaluation of these key bridges should be one of the important research directions in the coming days.

58 GEOSCIENCES↗
DOE OSTI2024

Clustering and Cliques in Preferential Attachment Random Graphs with Edge Insertion

In this paper, we investigate the global clustering coefficient (a.k.a transitivity) and clique number of graphs generated by a preferential attachment random graph model with an additional feature of allowing edge connections between existing vertices. Specifically, at each time step t, either a new vertex is added with probability f(t), or an edge is added between two existing vertices with probability 1 – f(t). We establish concentration inequalities for the global clustering and clique number of the resulting graphs under the assumption that f(t) is a regularly varying function at infinity with index of regular variation –$\gamma$, where $\gamma$ $\in$ [0, 1). Finally, we also demonstrate an inverse relation between these two statistics: the clique number is essentially the reciprocal of the global clustering coefficient.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

From electricity to fuels: Descriptors for C 1 selectivity in electrochemical CO 2 reduction

Electrochemical reduction of carbon dioxide (CO 2 RR) over transition metals follows a complex reaction network. Here, we combine observations from experimental literature with a theoretical analysis of energetics to rationalize that not all intermediates in the reduction of CO 2 are formed through direct protonation steps. We derive a selectivity map for two-electron products (carbon monoxide (CO) and formate) on pure metal surfaces using only the CO and OH binding energies as descriptors. For the pure metals that are selective towards CO formation, the variation of the CO binding energy is sufficient to further subdivide the map into domains that predominantly form H 2 , CO, and more reduced products. Our analysis rationalizes experimentally observed product distributions in CO 2 RR across pure metal systems. Overall, we highlight the need for additional material screening descriptors for CO 2 R and the importance of considering competition from the elementary steps of the hydrogen evolution reaction.

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

Accuracy of predictions made by machine learned models for biocrude yields obtained from hydrothermal liquefaction of organic wastes

Hydrothermal liquefaction (HTL) has potential for converting abundant wet organic wastes into renewable fuels. Because HTL consists of a complex reaction network, deterministic, physics-based prediction of its biocrude yield is prohibitively difficult. Data-driven methods provide an alternative to the physics-based approach; however, rigorous testing must be performed to ensure the accuracy of predictions made by data-driven methods. To this end, a data set was assembled consisting of 570 data points appearing in the open literature. The data set was divided into training, validation, and test sub-sets and used for evaluating different machine learning regression approaches to predict biocrude yield. Among the tested algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost) predicted biocrude yields in a test set that had not been used for training with the greatest accuracy, with root mean square errors (RMSE) of 8.34 and 8.57, respectively. Further refinement of the Random Forest model reduced its RMSE to 8.07. In comparison, predictions of a series of literature models resulted in RMSE ranging from 9.16 in the most accurate case to 27.6 in the least accurate; most literature models yielded RMSE values > 10. Using biocrude yield predictions from the most accurate Random Forest model and a probabilistic economic analysis found that the model accuracy is sufficient to prioritize allocation of resources based on projected minimum fuel selling price. In our report the models and analysis represent a major advance in the ability to use readily available data to predict biocrude yields on new feedstocks that have not previously been studied.

42 ENGINEERING↗
DOE OSTI2025

Degrees of rate control and AutoDiff-driven direct sensitivity analysis in heterogeneous catalysis

Despite the wide application and benefits of the degree of rate control (DRC) analysis, several details remain argued, particularly about the conservation of DRCs at transient (TR) and steady-state (SS) conditions, especially for complex reaction networks. This work argues that previous proofs about the conservation properties of DRCs have been incomplete, and we provide new mathematical proofs at TR and SS conditions. In addition, we use both analytical (automatic differentiation) and numerical (finite difference) approaches to compute DRCs for the case study of ethane hydrogenolysis (EH) over Pt(111). This work confirms that at both TR and SS conditions, the sum of all DRCs, i.e., sum of the degrees of kinetic (DKRC) and thermodynamic rate control (DTRC), is conserved at zero. At SS conditions, the sum of DKRC is conserved at 1 while the sum of DTRC is conserved at −1. In corroboration of previous works, we show that the DTRC for any adsorbate at SS is equal to the product of the species coverage and a constant. In contrast, at TR conditions, the individual sums of both DTRC and DKRC are not conserved and can be any real number, with potential implications for the novel field of dynamic catalysis. Finally, we show that the conventional finite difference (FD) approach, only useful at SS, is prone to inaccuracy and very sensitive to the value of the differential change applied. The optimal differential value also varies significantly with system and rate definition. Consequently, we describe and illustrate in this work the application of the automatic differentiation (AD) approach for the more accurate determination of DRCs at both TR and SS conditions.

Automatic differentiation↗
DOE OSTI2021

Topology optimization of additively manufactured fluidic components free of internal support structures

This report integrates projection-based approaches for implementing overhang constraints with fluid topology optimization to design additively manufactured fluidic components that do not require internal support structures. Internal support structures are challenging, potentially impossible to remove in complex fluid networks, yet would degrade fluidic performance if left within the part. This presents a major challenge in coupling topology optimization with additive manufacturing in the design of fluid components. The proposed approach leverages past work in overhang constraints for solid mechanics, including projection formulations and adjoint sensitivity analysis, and considers incompressible Navier–Stokes equations for laminar fluid flow. The approach is demonstrated on 2D and 3D fluid topology optimization problems to minimize pressure drop and constrain mass flow rates in pipes and manifolds. Resulting designs are crisp, logical, satisfy performance constraints, and satisfy overhang constraints, eliminating the need for internal support structures.

42 ENGINEERING↗
DOE OSTI2021

Preclinical Risk Evaluation of Normal Tissue Injury With Novel Radiosensitizers

Genotoxic damage induced by radiation triggers a highly coordinated DNA damage response, and molecular inhibitors of key nodes within this complex response network can profoundly enhance the antitumor efficacy of radiation. This is especially true for drugs targeting the catalytic subunit of DNA-dependent protein kinase, which is a core component of the nonhomologous end-joining DNA repair pathway, and ataxia telangiectasia mutated, which coordinates cell cycle arrest, apoptosis, and DNA repair functionalities after radiation exposure. Unlike the more modest in vitro radiosensitizing effects seen with classic sensitizing agents such as cisplatin, 5-fluorouracil, or taxanes, DNA-dependent protein kinase or ataxia telangiectasia mutated inhibitors provide much more robust sensitizing effects in vitro, as might be anticipated from targeting these key DNA repair modulators. However, patients with homozygous inactivating mutations of ataxia telangiectasia mutated or mice with homozygous defects in DNA-dependent protein kinase (severe combined immunodeficiency) have profoundly enhanced acute normal tissue radiation reactions. Therefore, there is significant potential that the combination of small molecule inhibitors of these kinases with radiation could cause similar dose-limiting acute normal tissue toxicities. Similarly, although less understood, inhibition of these DNA repair response pathways could markedly increase the risk of late radiation toxicities. Because these potent radiosensitizers could be highly useful to improve local control of otherwise radiation-resistant tumors, understanding the potential for elevated risks of radiation injury is essential for optimizing therapeutic ratio and developing safe and informative clinical trials. In this review, we will discuss 2 straightforward models to assess the potential for enhanced mucosal toxicity in the oral cavity and small intestine established in our laboratories. We also will discuss similar strategies for evaluating potential drug–radiation interactions with regard to increased risks of debilitating late effects.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Machine Learning Models for Binary Molecular Classification using VUV Absorption Spectra

Machine learning methods were combined with differential absorption spectroscopy measurements in the vacuum-ultraviolet region (5.167 – 9.920 eV) in order to develop predictive capabilities for inferring molecular structure from the spectra. Several types of species were analyzed and, for modeling purposes, were defined using a single classification: (1) alkane, (2) conjugation with oxygen (e.g. diacetyl, ethyl vinyl ether), (3) non-conjugated alkene (e.g. 1-butene, 1,4-cyclohexadiene), (4) oxygen-containing (e.g. 1-butanol, tetrahydrofuran), or (5) cyclic (e.g. cyclopentane, cyclohexanone). The latter molecular classification excluded cyclic ethers. Several modeling methods were employed in the analysis of 102 absorption spectra, 24 of which were measured for the first time. The primary objective was to identify suitable methods that enable accurate predictions of molecular structure classifications with minimized statistical uncertainties. Rather than identifying a single, unifying method to reliably predict molecular structure contributions to VUV absorption spectra, coordination is required among a particular method, the type of molecular structure detail (e.g. conjugation), and absorption region of interest. The latter is accomplished using a binning approach, wherein absorption regions of ~0.5 eV were utilized rather than the entire ~4.8 eV range. Photon energy binning enabled analysis of region-specific predictions of accuracy, precision, and recall. The outcome from the binning approach is that, rather than utilizing the entire spectrum, optimal determination of molecular structure using machine learning methods depends on the absorption region. Furthermore, the present work provides separate machine learning models for each molecular classification, which enables the identification of multi-functional species relevant to atmospheric chemistry and combustion chemistry, where isomer-resolved speciation is critical to understanding complex reaction networks.

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

Microbiome engineering for sustainable agriculture: using synthetic biology to enhance nitrogen metabolism in plant-associated microbes

Plants benefit from symbiotic relationships with their microbiomes. Modifying these microbiomes to further promote plant growth and improve stress tolerance in crops is a promising strategy. However, such efforts have had limited success, perhaps because the original microbiomes quickly re-establish. Since the complex biological networks involved are little understood, progress through conventional means is time-consuming. Synthetic biology, with its practical successes in multiple industries, could speed up this research considerably. Some fascinating candidates for production by synthetic microbiomes are organic nitrogen metabolites and related pyridoxal-5'-phosphate-dependent enzymes, which have pivotal roles in microbe-microbe and plant-microbe interactions. This review summarizes recent studies of these metabolites and enzymes and discusses prospective synthetic biology platforms for sustainable agriculture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Effects of co-adsorbed water on different bond cleavages involved in acetic acid decomposition on Pt (111)

Acetic acid decomposition on Pt (111) in the presence of co-adsorbed water is a good model system for oxygenate decomposition on Pt (111) in aqueous phase, with application in hydrogen production and biomass conversion. Here, in this study, we present a density functional theory (DFT) theory calculation of how co-adsorbed water affects different bond cleavages of acetic acid decomposition on Pt (111). The presence of co-adsorbed water generally enhances O$-$H bond cleavage while inhibiting OC$-$O and OC$-$OH bond cleavage. The influence of co-adsorbed water on C$-$H bond cleavage varies the most and depends on the nature of the transition state and how co-adsorbed water stabilizes the initial and final state. Although these trends are useful as general guidance, they are not sufficient to predict the effect on a complex reaction network such as acetic acid decomposition on Pt (111) which has several parallel reaction paths with similar energies. In the absence of co-adsorbed water, the two lowest energy pathways are decarboxylation (DCX) and decarbonylation (DCN) pathways through a common CH 2 COO intermediate, in which the DCX pathway (C$-$H bond cleavage of CH 2 COO) is more favorable than DCN pathway (OC$-$O bond cleavage of CH 2 COO). In the presence of co-adsorbed water, the energy difference between Csingle bondH bond cleavage and OC$-$O bond cleavage of CH 2 COO increases, suggesting an increase in the favorability of acetic acid decarboxylation (formation of carbon dioxide) over acetic acid decarbonylation (formation of carbon monoxide) on Pt (111).

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

Nitrate Controls on the Extent and Type of Metal Retention in Fine-Grained Sediments of a Simulated Aquifer

Aquifer groundwater quality is largely controlled by sediment composition and physical heterogeneity, which commonly sustain a unique redox gradient pattern. Attenuation of heavy metals within these heterogeneous aquifers is reliant on multiple factors, including redox conditions, and redox-active species that can further influence biogeochemical cycling. Here, we simulated an alluvial aquifer system using columns filled with natural coarse-grained sediments and two domains of fine-grained sediment lenses. Our goal was to examine heavy metal (Ni and Zn) attenuation within a complex aquifer network and further explore nitrate-rich groundwater conditions. The fine-grained sediment lenses sustained reducing conditions and served as a sink for Ni sequestration – in the form of Ni-silicates, Ni-organic matter, and a dominant Ni-sulfide phase. Further, the silicate clay and sulfide pools were also important retention mechanisms for Zn; however, Ni was associated more extensively with organic matter compared to Zn that formed layered double hydroxides. Nitrate-rich conditions promoted denitrification within the lenses that was coupled to the oxidation of Fe(II) and the concomitant precipitation of an Fe(III) phase with higher structural distortion. A decreased metal sulfide pool also resulted, where nitrate-rich conditions generated in an average 20% decrease in solid phase Ni, Zn, and Fe. Ultimately, nitrate plays a significant role in the aquifer’s biogeochemical cycling and capacity to retain heavy metals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Modified Energy Span Analysis of Catalytic Parallel Pathways and Selectivity

Mechanistic modeling provides vital insights into catalytic reactions. To analyze complex reaction networks with parallel pathways, we leverage the graph theory approach of the Energy Span Model (ESM) to develop a modified energy span analysis (MESA). A new method of cycle plots is proposed to perform reaction pathways analysis visually. We demonstrate this method on two published models: one describing carbon monoxide oxidation and the other simulating ethylene conversion to propanal via hydroformylation or ethane via hydrogenation. Fundamental insights explain kinetic observables, such as a reactant’s negative reaction order. General principles are revealed, such as rate-determining surface species being outside the primary reaction flux cycle and pathway selectivity being a purely kinetic property when reaction conditions are not near equilibrium. Lastly, we demonstrate MESA’s consistency with published microkinetic modeling results, highlighting this technique’s extension of the ESM to heterogeneous catalysts using collision theory to describe adsorption steps and concentration effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parametrized from MD simulations of complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four order of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example in the case of large molecules or solids. In this work, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parametrize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation timescales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parametrized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Single-Molecule Kinetics of Styrene Hydrogenation on Silica-Supported Vanadium: The Role of Disorder for Single-Atom Catalysts

We report a theoretical approach for the study of supported atom catalysis is developed based on recent advances in the study of single-molecule kinetics. This view is particularly useful in exhibiting the role of disorder in single-atom and single-site catalysts on amorphous supports. The distribution of passage times (or waiting times) through a complex catalytic network originating from a set of coupled active sites is described by a probability distribution function (PDF), f(t), that reflects the local environment of the reaction center. An efficient algorithm is developed based on the linear algebra of the Markov transition matrix that produces f(t) or its moments. The kinetics of the hydrogenation reaction of styrene on an organovanadium(III) catalyst supported on amorphous silica is studied. A kinetic model consisting of three intertwined catalytic cycles emanating from three chemically distinct active sites is proposed to describe the chemistry. Density functional theory (DFT) calculations are employed to determine the free energy barriers of the reactions, which are used to construct the rate coefficient matrix. The disorder induced by the amorphous support material is divided into a low-dimensional short-range component reflecting the covalent structures near the reaction center and a weaker long-range component modeling the bulk randomness. The results are computed and analyzed for a wide range of concentration values and disorder scenarios. The unusual structure in the f(t) PDF is found to occur for certain cases that reveal the contribution of multiple catalytic pathways acting in concert.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Hydrogen Evolution on Restructured B-Rich WB: Metastable Surface States and Isolated Active Sites

Borides have been recently identified to be a class of earth-abundant and low-cost materials that are surprisingly active toward oxidative dehydrogenation and hydrogen evolution reaction (HER) catalysis. Here, we explain from first-principles calculations the HER activity of WB, an industrial material known for its superior physical properties and chemical inertness. We find that, compared to bulk termination, a major surface reconstruction takes place, leading to the off-stoichiometric B-rich surface termination that contains the active sites. The hydrogen adsorbate configurations are further investigated under various adsorbate coverages. Many competing configurations appear to be accessible in reaction conditions, and thus, a grand canonical ensemble is established to describe the catalytic system. A phase diagram of adsorbate coverages is constructed as a function of pH and the applied potential. A complex reaction network is presented based on the ensemble-averaged reaction rates, and the electrocatalytic activities are evaluated under different overpotentials. Here, the major contributors to the activity are found to be a few metastable surface states with a distinct electronic structure that are only accessible at high adsorbate coverages in reaction conditions. In addition, while the activity of the dominant active site is nearly the same as that on the unreconstructed WB, the B-rich formations play an important role of isolating the active sites and preventing the passivation of the surface with H 2 bubble formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗