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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Proton transfer reactions in donor site mutants of ESR, a retinal protein from Exiguobacterium sibiricum

Light-driven proton transport by microbial retinal proteins such as archaeal bacteriorhodopsin involves carboxylic residues as internal proton donors to the catalytic center which is a retinal Schiff base (SB). The proton donor, Asp96 in bacteriorhodopsin, supplies a proton to the transiently deprotonated Schiff base during the photochemical cycle. Subsequent proton uptake resets the protonated state of the donor. Here this two step process became a distinctive signature of retinal based proton pumps. Similar steps are observed also in many natural variants of bacterial proteorhodopsins and xanthorhodopsins where glutamic acid residues serve as a proton donor. Recently, however, an exception to this rule was found. A retinal protein from Exiguobacterium sibiricum, ESR, contains a Lys residue in place of Asp or Glu, which facilitates proton transfer from the bulk to the SB. Lys96 can be functionally replaced with the more common donor residues, Asp or Glu. Proton transfer to the SB in the mutants containing these replacements (K96E and K96D/A47T) is much faster than in the proteins lacking the proton donor (K96A and similar mutants), and in the case of K96D/A47T, comparable with that in the wild type, indicating that carboxylic residues can replace Lys96 as proton donors in ESR. We show here that there are important differences in the functioning of these residues in ESR from the way Asp96 functions in bacteriorhodopsin. Reprotonation of the SB and proton uptake from the bulk occur almost simultaneously during the M to N transition (as in the wild type ESR at neutral pH), whereas in bacteriorhodopsin these two steps are well separated in time and occur during the M to N and N to O transitions, respectively.

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

ARM Aerial Instrument Workshop Report

The mission of the U.S. Department of Energy’s (DOE) Biological and Environmental Research (BER) program is to “support transformative science and scientific user facilities to achieve a predictive understanding of complex biological, earth, and environmental systems for energy and infrastructure security, independence, and prosperity.” (https://science.osti.gov/ber) Aligned with the BER central mission, the Earth and Environmental Systems Sciences Division (EESSD) plays a vital role in supporting the fundamental research to understand and predict Earth’s climate and environmental systems, and is also in a unique position to inform the development of sustainable solutions to the nation’s energy and environmental challenges. Specifically, EESSD manages two scientific user facilities: the Atmospheric Radiation Measurement (ARM) user facility and the Environmental Molecular Sciences Laboratory (EMSL). These facilities provide the broader scientific community with scientific expertise, technical capabilities, and unique data sets to facilitate science in areas of importance to DOE. As a multi-platform scientific user facility, ARM aims to fulfill the needs predominantly within the EESSD Atmospheric System Research (ASR) and the Earth and Environmental System Modeling (EESM) mission areas, and provide the critical measurements required to improve understanding of aerosol and cloud life cycles and their interactions, and their coupling with the Earth’s surface. Over the years, ARM has carried out piloted and unmanned aircraft campaigns under different organizational and operational paradigms (Schmid et al. 2014, 2016). Building on its success, the ARM Aerial Facility (AAF) continues to complement the ground-based observations with airborne in situ cloud, aerosol, and trace gas observations as well as measurements of atmospheric state and atmospheric radiation. During the past three years, ARM has managed field campaigns using unmanned aerial systems (UAS) and tethered balloon systems (TBS) at Oliktok Point in Alaska to improve understanding of atmospheric processes in the Arctic. In 2019, following a careful evaluation of scientific community needs, ARM acquired a Bombardier Challenger 850 regional jet to replace the vintage Grumman Gulfstream-159 turboprop aircraft previously used by AAF. With this new “laboratory in the sky”, AAF is evaluating its current and future aerial observation capabilities to continue satisfying the needs of the research community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Crystal structure of MutYX: a novel clusterless adenine DNA glycosylase with a distinct C-terminal domain and 8-oxoguanine recognition sphere

Abstract The [4Fe–4S] cluster is an important cofactor of the base excision repair (BER) adenine DNA glycosylase MutY to prevent mutations associated with 8-oxoguanine (OG). Several MutYs lacking the [4Fe–4S] cofactor have been identified. Phylogenetic analysis shows that clusterless MutYs are distributed in two clades suggesting cofactor loss has occurred in multiple independent evolutionary events. Herein, we determined the first crystal structure of a clusterless MutY complexed with DNA. On the basis of the dramatic structural divergence from canonical MutYs, we refer to this as representative of a clusterless MutY subgroup “MutYX.” Interestingly, MutYX compensates for the missing [4Fe–4S] cofactor to maintain positioning of catalytic residues by expanding a pre-existing α-helix and acquisition of a new α-helix. Surprisingly, MutYX also acquired a new C-terminal domain that uniquely recognizes OG using residues Gln201 and Arg209. Adenine glycosylase assays and binding affinity measurements indicate that Arg209 is the primary residue responsible for OG:A lesion specificity, while Gln201 assists by bridging OG and Arg209. Surprisingly, replacement of Arg209 and Gln201 with Ala increased activity toward G:A mismatches. The MutYX structure serves as an example of devolution, capturing structural features required to retain function in the absence of a metal cofactor considered indispensable.

Trasviña-Arenas, Carlos H [University of Californi↗
DOE OSTI2021

Data-Driven Strategies for Accelerated Materials Design

The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks

The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these empirical parameters with machine-learned dynamic values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability, while the deep neural network parameterization is smooth and accurate and reproduces insightful features of the original empirical parameterization. Altogether, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models.

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

Interfacially Enhanced Superconductivity in Fe(Te,Se)/Bi 4 Te 3 Heterostructures

Realizing topological superconductivity by integrating high-transition-temperature (T C ) superconductors with topological insulators can open new paths for quantum computing applications. Here, a new approach is reported for increasing the superconducting transition temperature $T^{onset}_C$ by interfacing the unconventional superconductor Fe(Te,Se) with the topological insulator Bi–Te system in the low-Se doping regime, near where superconductivity vanishes in the bulk. The critical finding is that the $T^{onset}_C$ of Fe(Te,Se) increases from nominally non-superconducting to as high as 12.5 K when Bi 2 Te3 is replaced with the topological phase Bi 4 Te 3 . Interfacing Fe(Te,Se) with Bi4Te3 is also found to be critical for stabilizing superconductivity in monolayer films where $T^{onset}_C$ can be as high as 6 K. Measurements of the electronic and crystalline structure of the Bi 4 Te 3 layer reveal that a large electron transfer, epitaxial strain, and novel chemical reduction processes are critical factors for the enhancement of superconductivity. This novel route for enhancing T C in an important epitaxial system provides new insight on the nature of interfacial superconductivity and a platform to identify and utilize new electronic phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe₂. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress–strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation—phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. In conclusion, this work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

2D materials↗
DOE OSTI2026

Mechanistic Insights into Dinitrogen Reduction to Ammonia in Light-Controlled Nanocrystal:Nitrogenase Complexes

Developing systems that can efficiently capture photon energy and convert this energy into fuels and chemicals requires understanding how to assemble molecular components with diverse functions into complete systems possessing selectivity and efficiency in directing charge carriers to catalytic reactions. There are many challenges to achieving this goal. One promising approach is the development of hybrid systems that combine semiconductor nanocrystals (NCs) for light capture and enzymes as efficient catalysts. Such biohybrid systems capitalize on the tunable electronic and optical properties of NCs while leveraging the unmatched specificity and efficiency of enzymes in catalyzing chemical reactions, thereby offering opportunities to surpass the limitations of each component alone. Here, we focus on recent progress in developing a biohybrid system that combines CdS NCs for photon capture with the enzyme nitrogenase to accomplish light-driven dinitrogen (N 2 ) reduction to ammonia (NH 3 ). Integrating light-harvesting materials with biological catalysts requires a deep understanding of NC properties, protein stability, and electron transfer (ET), making it an inherently multidisciplinary problem. The reduction of N 2 to NH 3 is a challenging reaction, with a high demand in both agriculture and industrial chemical production. This reaction is intrinsically energy intensive, due to the need to activate the N≡N triple bond. The current standard industrial approach to N 2 reduction, the Haber−Bosch reaction, obtains the necessary energy input from fossil fuels, whereas biological systems capable of N 2 reduction utilize the hydrolysis of ATP as their energy source. Replacing these costly, energy-intensive inputs with renewable light energy represents a critical step toward sustainable NH 3 production. Recent progress has demonstrated that semiconductor CdS NCs can be coupled to the catalytic component of nitrogenase, the MoFe protein, to form a biohybrid CdS NC:MoFe protein complex, enabling light-driven N 2 reduction rather than energy input from fossil fuels or ATP. This illustrates how inorganic NCs can functionally replace the natural Fe protein partner, yielding a biohybrid catalyst that enables controlled electron delivery and provides not only light-driven NH 3 production but also new approaches for probing enzyme catalytic function. The CdS NC:MoFe protein biohybrid system enables light-initiated electron delivery at ambient temperature, as well as temperatures below freezing, allowing for stabilization and spectroscopic characterization of key reaction intermediates. These findings highlight how photochemical biohybrids can serve as both functional catalysts and mechanistic probes. Beyond studies of the nitrogenase mechanism, studies of the CdS NC:MoFe system reveal how variables such as NC size, electrostatic binding interactions, and sacrificial electron donors (SEDs) govern complex stability, charge transfer efficiency, and catalytic performance. In addition, studies of nitrogenase and the high activation barrier for N 2 reduction are enabling investigations of new and interesting questions regarding the properties and limitations of NC biocatalysis. In this Account, we describe the key features of CdS NC:MoFe protein biohybrids and the parameters for optimal light-driven N 2 reduction, and how controlling ET with light illuminates the path to new insights into the nitrogenase mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A polyketide-based biosynthetic platform for diols, amino alcohols and hydroxy acids

Medium- and branched-chain diols and amino alcohols are important industrial solvents, polymer building blocks, cosmetics and pharmaceutical ingredients, yet biosynthetically challenging to produce. Here we present an approach that uses a modular polyketide synthase (PKS) platform for the efficient production of these compounds. This platform takes advantage of a versatile loading module from the rimocidin PKS and nicotinamide adenine dinucleotide phosphate-dependent terminal thioreductases. Reduction of the terminal aldehyde with alcohol dehydrogenases enables the production of diols, oxidation enables the production of hydroxy acids and specific transaminases allow the production of various amino alcohols. Furthermore, replacement of the malonyl-coenzyme A-specific acyltransferase in the extension module with methyl- or ethylmalonyl-coenzyme A-specific acyltransferase enables the production of branched-chain diols, amino alcohols and carboxylic acids in high titres. Use of our PKS platform in Streptomyces albus demonstrated the high tunability and efficiency of the platform.

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

Investigation of Defect Physics for Efficient, Durable and Ubiquitous Perovskite Solar Modules (Final Technical Report)

Organometal halide perovskite solar cells have experienced eye-catching improvements in its recent few years. It serves as one of the most promising candidates to replace the currently widely used silicon-based solar modules. To implement its final step to the real application, functional longevity becomes the dernier continent to conquer. As a polycrystalline material, defect plays critical role in the efficiency and stability of the perovskite solar cells. Thus, the investigation of the defect physics of the perovskite layer is indispensable in this research field. However, hard evidence and a consensus are still lacking in terms of the specific nature of the defects and their effects on performance and hysteresis, and perhaps even more importantly, there is absence of fundamental understanding of the correlations between the defects and long-term operational stability of the device. A more fundamental understanding of the nature of defects in perovskite materials is of paramount importance to progress their efficiency and durability. In this work we propose in-depth studies of correlations of defects with performance and stability of perovskite solar cells. Our project aimed 1) to investigate the defects physics in perovskite solar cells, and 2) to develop a comprehensive understanding and physical model of defects and its influence on performance and stability of perovskite solar cells. With the support from program manager, Peter Lobaccaro, and the Solar Energy Technologies Office of U.S. Department of Energy, the project ends with impact achievements. Our research results have systematically provided strategies to analyze the influences of constructive molecular configurations to the charged defects in the perovskite lattices and developed in-depth understanding of chemical additive approach to improve the perovskite solar cell performance and stability. As history has shown us, control over defect properties of semiconductor materials is the key to achieving high performance and low cost devices. Therefore, the potential impact of unlocking the understanding and manipulation of defects in perovskites is great, enabling this technology to realize SETO goals. The research project is highly productive with 18 published papers in three years in top-level journals such as Science, Nature, Nature Materials, Nature Communications, Journal of American Chemistry Society, Joule, Advanced Materials, and Nano Letters. These works have drawn great attention nationwide with notable total citations over 700 times from 2020 to 2022.

14 SOLAR ENERGY↗
DOE OSTI2021

SISGR: Chemomechanics of Far-From-Equilibrium Interfaces (COFFEI)

Portable, reliable, and deployable devices for energy storage and conversion require fundamental changes in design of solid-state composites comprising ceramics and metals. These materials comprise the electrodes and electrolytes of next-generation solid-oxide fuel cells and all solid-state batteries, forming solid-state functional composites. The advent of solid-state batteries – which replace liquid electrolytes with solid electrolytes capable of lithium ion transport for reliable energy storage in portable batteries – and the increased demand space for all solid-state fuel cells capable of oxygen reduction at intermediate temperatures remain important challenges for improved material stability and decreased system cost. However, little is understood about three fundamental facets of materials that enable such solid-state energy applications. First, how do such materials deform, fracture, or delaminate under operando conditions? Second, how does such mechanical deformation limit or facilitate electronic and ionic transport within and across such material interfaces? Third, how we can predictably design interface-rich composites to engineer both structural and electrochemical stability? This COFFEI Group comprised expertise from Materials Science & Engineering and Nuclear Science & Engineering to integrate unique in situ experiments, simulations, and fabricated interfaces that address these fundamental questions in solid-state interfaces of nanoscale composites that will guide solid-state electrochemistry, transport kinetics, and mechanical deformation for nonstoichiometric materials that enable such applications. In particular, we built on COFFEI’s understanding of chemomechanical coupling among defect concentrations, ionic transport, electron transport, and stored elastic energy that is particularly acute in the far-from-equilibrium conditions typical of energy device applications. By tailoring our focus to solid-state interfaces, we addressed these important issues by (a) developing and applying advanced in situ and ex situ characterization tools to characterize model materials and interfaces synthesized with molecular-level control, under both laboratory-controlled and extreme environments representative of energy device operation; and (b) employing computational modeling and simulation frameworks to predict transport mechanisms, reactivity and stability of these model materials and interfaces under significant chemical strains typical of energy device operation. Recent progress provided insights to additional materials systems and electrochemomechanical fatigue and fracture that were not fully envisioned when the program was initiated. Specifically, in the final three years of COFFEI we pursued two integrated thrusts, with complementary focus. Thrust I focused on failure-resistant electrochemomechanical composites, while Thrust II focused on strain-modulated conductivity and reactivity across interfaces. In contrast to our initial COFFEI focus, these thrusts concentrated wholly on solid-state material interfacial interactions and included greater integration of multiscale visualization including in situ electron microscopy of strained structures/interactions and mesoscale simulations. Successful development of functionally superior and long-lived battery and fuel cell systems and stress adaptable oxides requires a deeper, fundamental understanding of the coupling among the historically important subfields of solid-state electrochemistry, transport kinetics, and mechanical deformation for nonstoichiometric metal oxide electrodes. In this program, the understanding and the application of chemomechanical coupling of defect concentrations, ionic transport, electro-catalytic activity and stored elastic energy, particularly acute in the far-from-equilibrium conditions typical of energy device applications, are being refined and implications for device operation clarified, including for miniaturized solid-state batteries and fuel cells.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Filon-like integration strategy for calculating exact exchange in periodic boundary conditions: a plane-wave DFT implementation

An efficient and accurate approach for calculating exact exchange and other two-electron integrals has been developed for periodic electronic structure methods. Traditional approaches used for integrating over the Brillouin zone in band structure calculations, e.g. trapezoidal or Monkhorst-Pack, are not accurate enough for two-electron integrals. This is because their integrands contain multiple singularities over the double integration of the Brillouin zone, which with simple integration methods lead to very inaccurate results. A common approach to this problem has been to replace the Coulomb interaction with a screened Coulomb interaction that removes singularities from the integrands in the two-electron integrals, albeit at the inelegance of having to introduce a screening factor which must precomputed or guessed. Instead of introducing screened Coulomb interactions in an ad hoc way, the method developed in this work derives an effective screened potential using a Filon-like integration approach that is based only on the lattice parameters. This approach overcomes the limitations of traditionally defined screened Coulomb interactions for calculating two-electron integrals, and makes chemistry many-body calculations tractable in periodic boundary conditions. This method has been applied to several systems for which conventional DFT methods do not work well, including the reaction pathways for the addition of H 2 to phenol and Au$^{–}_{20}$ nanoparticle, and the electron transfer of a charge trapped state in the Fe(II) containing mica, annite.

36 MATERIALS SCIENCE↗
DOE OSTI2024

E(n)-Equivariant cartesian tensor message passing interatomic potential

Machine learning potential (MLP) has been a popular topic in recent years for its capability to replace expensive first-principles calculations in some large systems. Meanwhile, message passing networks have gained significant attention due to their remarkable accuracy, and a wave of message passing networks based on Cartesian coordinates has emerged. However, the information of the node in these models is usually limited to scalars, and vectors. In this work, we propose High-order Tensor message Passing interatomic Potential (HotPP), an E(n) equivariant message passing neural network that extends the node embedding and message to an arbitrary order tensor. By performing some basic equivariant operations, high order tensors can be coupled very simply and thus the model can make direct predictions of high-order tensors such as dipole moments and polarizabilities without any modifications. The tests in several datasets show that HotPP not only achieves high accuracy in predicting target properties, but also successfully performs tasks such as calculating phonon spectra, infrared spectra, and Raman spectra, demonstrating its potential as a tool for future research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Measurements of Intermediate Species in Fuel-Rich Oxidation of Ethylene, Toluene, and n -Decane

Polycyclic aromatic hydrocarbons (PAHs) are important precursors to the formation of soot and are also pollutant emissions from combustion devices, including internal combustion engines. To understand the formation of PAHs, the low-molecular-weight hydrocarbon chemistry leading to their formation needs to be understood. Toward this goal, fuel-rich oxidation of three different hydrocarbons, i.e., ethylene, toluene, and n-decane, has been investigated in an atmospheric pressure flow reactor at varying temperatures (1000–1350 K), equivalence ratios (φ = 3.0–12.0), and residence times of 0.25–1.5 s. The major C 1 to C 7 intermediates, such as methane, acetylene, ethylene, allene, propyne, propylene, vinylacetylene, 1,3-butadiene, cyclopentadiene, benzene, and toluene, were quantified using a gas chromatograph equipped with a flame ionization detector. The experimental tendency of intermediate species formation, e.g., the dependence of temperature, equivalence ratio, and residence time, was similar in ethylene oxidation and n-decane oxidation. The concentrations of intermediate species up to C 4 were higher in ethylene/n-decane oxidation than toluene oxidation, while a nearly equal or larger amount of C5–C7 species was produced in toluene oxidation. The experimental data were compared with modeling results using a detailed chemical kinetic mechanism. The calculated data using the kinetic model were in agreement with the experimental results. Additionally, a comprehensive kinetic analysis on the reaction pathways of each species was conducted to assess the differences in the oxidation chemistry with the change in the structure of hydrocarbons. In particular, in-depth analysis of benzene formation was performed, elucidating that two benzene formation pathways were important in ethylene and n-decane oxidation: (1) H elimination of the 2,4-cyclohexadienyl radical produced from the isomerization of the 2,4-cyclopentadienylmethyl radical, and (2) dehydrogenation of cyclohexadiene produced from reactions of vinyl radical with 1,3-butadiene. In toluene oxidation, it was found that benzene was primarily produced from toluene through the replacement of the methyl group with hydrogen.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Structural basis for sensitivity and acquired resistance of fungal cap guanine-N7 methyltransferases to the antifungal antibiotic sinefungin

Abstract The essential enzyme messenger RNA (mRNA) (guanine-N7) methyltransferase catalyzes S-adenosylmethionine (SAM)-dependent conversion of GpppRNA ends to the m7GpppRNA cap structure characteristic of eukaryal mRNAs. The antibiotic sinefungin (SFG) is a SAM analog in which the S-CH3 sulfonium moiety of SAM is replaced by a C-NH2 amine. Available evidence indicates that the antifungal activity of SFG is exerted via inhibition of fungal cap methyltransferase Abd1. Here we report that recombinant Kluyveromyces lactis and Saccharomyces cerevisiae Abd1 are 240-fold and 485-fold more sensitive to inhibition by SFG than by the reaction product S-adenosylhomocysteine (SAH). Crystal structures of K. lactis and S. cerevisiae Abd1 as binary complexes with SAH or SFG and ternary complexes with GTP•SFG highlight how SFG makes two hydrogen bonds from its C-NH2 amine to the guanine-O6 and -N7 atoms of GTP that account for its higher affinity vis-à-vis SAH and SAM. Through a genetic screen to isolate SFG-resistant S. cerevisiae strains, a conserved tyrosine (Tyr416) that interacts with the cap guanine in Abd1 was identified as a key determinant of SFG potency. Tyr416 Abd1 variants confer SFG resistance in vitro by weakening cap-assisted SFG interactions with Abd1. Our study illuminates the basis for the exquisite SFG sensitivity of fungal cap methyltransferases.

Biochemistry & Molecular Biology↗
DOE OSTI2024

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Selected humanization of yeast U1 snRNP leads to global suppression of pre-mRNA splicing and mitochondrial dysfunction in the budding yeast

The recognition of the 5′ splice site (5′ ss) is one of the earliest steps of pre-mRNA splicing. To better understand, the mechanism and regulation of 5′ ss recognition, we selectively humanized components of the yeast U1 (yU1) snRNP to reveal the function of these components in 5′ ss recognition and splicing. We targeted U1C and Luc7, two proteins that interact with and stabilize the yU1 snRNA and the 5′ ss RNA duplex. We replaced the zinc-finger (ZnF) domain of yeast U1C (yU1C) with its human counterpart, which resulted in a cold-sensitive growth phenotype and moderate splicing defects. We next added an auxin-inducible degron to yeast Luc7 (yLuc7) protein (to mimic the lack of Luc7Ls in human U1 snRNP). We found that Luc7-depleted yU1 snRNP resulted in the concomitant loss of Prp40 and Snu71 (two other essential yU1 snRNP proteins), and further biochemical analyses suggest a model of how these three proteins interact with each other in the U1 snRNP. The loss of these proteins resulted in a significant growth retardation accompanied by a global suppression of pre-mRNA splicing. The splicing suppression led to mitochondrial dysfunction as revealed by a release of Fe 2+ into the growth medium and an induction of mitochondrial reactive oxygen species. Together, these observations indicate that the human U1C ZnF can substitute that of yeast, Luc7 is essential for the incorporation of the Luc7–Prp40–Snu71 trimer into yU1 snRNP, and splicing plays a major role in the regulation of mitochondrial function in yeast.

Biochemistry & Molecular Biology↗