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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Generating Co-expression Networks for Three Cyanobacteria: Synechococcus sp. PCC 7942, Synechococcus sp. PCC 7002, Synechocystis sp. PCC 6803

Cyanobacteria are photosynthetic organisms capable of high growth rate and represent a promising bioplatform for harnessing the sun’s energy to make biofuel. Additionally, the process of photosynthesis absorbs CO2 from the environment. Understanding the metabolic processes involved in photosynthesis could lead to solutions to the recent rise of CO2 concentration in Earth’s atmosphere and the associated climate change. More research on the transcriptional regulation of these cells is needed to learn how to harness the untapped potential of cyanobacteria for these applications. Transcriptional analysis via RNA-seq provides an understanding of how gene expression changes at the mRNA level under diverse growing conditions. I systematically collected and analyzed RNA-Seq data obtained under a variety of conditions and available on the NCBI database for three cyanobacteria model organisms: Synechococcus elongatus sp. PCC 7942, Synechococcus sp. PCC 7002, and Synechocystis sp. PCC 6803. For each organism, the data was mapped to a reference genome to characterize the RNA expression profile. Samples were checked for quality based on the number of reads and the correlation of the expression profile between labeled replicates. All samples were transformed into transcripts per million reads, followed by a log transformation to account for the wide range of sample sizes. Gene co-expression networks were generated and analyzed for each species using Cytoscape. These networks provide a base level of gene expression for each species. The network topology and high-betweeness nodes of these networks need to be analyzed further to provide insight on potential ways to harness cyanobacteria genetics. Additionally, these datasets can be used together to form a core genome network analysis- one that includes only the genes that are homologous between the three species. This project has prepared the way for a more in-depth study on photosynthetic microbes on a genetic level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

UNH TDP - Load Cell Raw Data and Processing Scripts - Fall 2021

This submission contains raw Load Cell data and processing scripts associated with MHKDR submission 394 (UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary, DOI: 10.15473/1973860) from the University of New Hampshire and Atlantic Marine Energy Center (AMEC) turbine deployment platform. The user is directed to the MHKDR submission 394 for relevant context and detail of this deployment; see link below. The 394_READ_ME file here provides the description from that submission for quick reference. The READ_ME file for this specific instrument from the 394 submission is also available here. This submission contains a zipped folder structure containing raw data in its original format and MATLAB (2019a) processing scripts used to process and manipulate the data into its final form. The final data products are submitted in the 394 submission.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Protocol for single-cell isolation and genome amplification of environmental microbial eukaryotes for genomic analysis

We describe environmental microbial eukaryotes (EMEs) sample collection, single-cell isolation, lysis, and genome amplification, followed by the rDNA amplification and OTU screening for recovery of high-quality species-specific genomes for de novo assembly. These protocols are part of our pipeline that also includes bioinformatic methods. The pipeline and its application on a wide range of phyla of different sample complexity are described in our complementary paper. In addition, this protocol describes optimized lysis, genome amplification, and OTU screening steps of the pipeline. For complete details on the use and execution of this protocol, please refer to Ciobanu et al. (2021).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Exotic edge states of C 3 high-fold fermions in honeycomb lattices

A generalization of the graphene honeycomb model to the case where each site in the honeycomb lattice contains a n -fold degenerate set of eigenstates of the C 3 symmetry has been recently proposed to describe several systems, including triangulene crystals and photonic lattices. These generalized honeycomb models are defined by ( n a , n b ) , the number of C 3 eigenstates in the a and b sites of the unit cell, resulting in n a + n b bands. Thus, the (1,1) case gives the coventional honeycomb model that describes the two low-energy bands in graphene. Generalizations, such as (2,1), (2,2), and (3,3) display several nontrivial features, such as coexisting graphenelike Dirac cones with flat bands, both at zero and finite energy, as well as robust degeneracy points where a flat band and a parabolic band meet at the Γ point. Here we explore the edge states of this class of crystals, using as reference triangulene crystals, and we find several types of edge states absent in the conventional (1,1) honeycomb case, associated to the nontrivial features of the two-dimensional bands of the high-fold case. First, we find dispersive edge states associated to the finite-energy flat bands, that occur both at the armchair and zigzag termination. Second, in the case of noncentrosymmetric triangulene crystals that lead to a S = 1 Dirac band, we have a bonding-antibonding pair of dispersive edge states, localized in the same edge so that their energy splitting is reduced as their localization increases, opposite to the conventional behavior of pairs of states localized in opposite edges. Third, for the (3,3) case, that hosts a gap separating a pair of flat conduction and valence bands, we find nondispersive edge states with E = 0 in all edge terminations. Published by the American Physical Society 2024

Madail, L. (ORCID:0000000208457748)↗
DOE OSTI2020

Non-destructive simulation of node defects in additively manufactured lattice structures

Additive Manufacturing (AM), commonly referred to as 3D printing, offers the ability to not only fabricate geometrically complex lattice structures but parts in which lattice topologies in-fill volumes bounded by complex surface geometries. However, current AM processes produce defects on the strut and node elements which make up the lattice structure. This creates an inherent difference between the as-designed and as-fabricated geometries, which negatively affects predictions (via numerical simulation) of the lattice’s mechanical performance. Although experimental and numerical analysis of an AM lattice’s bulk structure, unit cell and struts have been performed, there exists almost no research data on the mechanical response of the individual as-manufactured lattice node elements. Here we propose a methodology that, for the first time, allows non-destructive quantification of the mechanical response of node elements within an as-manufactured lattice structure. A custom-developed tool is used to extract and classify each individual node geometry from micro-computed tomography scans of an AM fabricated lattice. Voxel-based finite element meshes are generated for numerical simulation and the mechanical response distribution is compared to that of the idealised computer-aided design model. The method demonstrates compatibility with Uncertainty Quantification methods that provide opportunities for efficient prediction of a population of nodal responses from sampled data. Overall, the non-destructive and automated nature of the node extraction and response evaluation is promising for its application in qualification and certification of additively manufactured lattice structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Evaluation of a 5kW Solid Oxide Electrolysis Cell Stack

Solid oxide electrolysis cells (SOECs) are a developing technology for hydrogen production. They are promising due to their utilization of thermal energy to reduce their electricity consumption. The Department of Energy (DOE) has set goals to reduce the price per kilogram of hydrogen, and Idaho National Laboratory (INL) is conducting research to develop and demonstrate advanced methods and technologies to achieve making hydrogen a more affordable, efficient, and sustainable energy source. In this project, INL is collaborating with a vendor to evaluate and validate the design of a 5kW SOEC stack. The test aims to demonstrate a safe startup, operation, and shutdown on INL’s 5kW test stand, providing third-party validation in a different environment. A successful 50-hour test with stable hydrogen production will pave the way for a subsequent 1000-hour test. This project emphasized learning about electrolysis and the various support systems, referred to as balance of plant (BOP) equipment. These systems required modification while swapping to the vendor 5kW SOEC stack, most prominently the furnace door design and the controls system. It also included gaining skills in creating computer-aided design (CAD) drawings for the new door while using available materials and ensuring it met high-temperature requirements. Upon completion of modifications to the test stand, the SOEC will be test-fitted with respective wiring and piping. Instrumentation and piping will be checked for leaks and quality. This must be completed before the vendor’s engineers arrive to observe the test plan, startup, and a collection of 50 hours of data. The vendor’s engineers will ensure their stack performed sufficiently in the 50-hour test to enable the progression to a 1000-hour test.

08 - HYDROGEN↗
DOE OSTI2021

Research progress on the mechanism of ferroptosis and its clinical application

Ferroptosis is a mode of cell death dependent on iron ions, which is mainly induced by the decrease of the biological activity of glutathione peroxidase or the accumulation of lipid peroxidation and reactive oxygen species (ROS). It is significantly different from autophagy and other forms of cell death in terms of cell morphology and biochemistry. The exact mechanisms of ferroptosis are not clear. More and more studies have shown that various tumor diseases and nervous system diseases are closely related to ferroptosis. The occurrence and development of related diseases can be tolerated by stimulating or inhibiting the occurrence of ferroptosis. Therefore, ferroptosis has occupied a very important position in recent years. This article reviews the discovery process, characteristics, mechanisms, inducers, inhibitors of ferroptosis and its related clinical applications to lay a foundation for follow-up researchers to study ferroptosis and provide some reference value.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A mass–momentum consistent coupling for mesh-adaptive two-phase flow simulations

Here, we present a novel mass-momentum consistent coupling between a geometric volume-of-fluid scheme and an incompressible flow solver with differing directional-splitting approaches. The advection of the volume fraction is performed using a direction-split algorithm, whereas the momentum advection algorithm uses a traditional unsplit, fractional-step approach. Both algorithms employ finite-volume discretizations based on Cartesian meshes. In solving the mass-momentum consistency problem, momentum fluxes at the cell faces are weighted by the density fluxes based on the already advected volume fraction. The success of our approach lies on introducing a Favre-averaged velocity interpolation at the liquid/gas interface along with a minmod slope limiter. Mesh-convergence studies show that when the minmod slope limiter is used, the two-phase solver retains an accuracy between first and second order, but when a purely upwind scheme is considered, its accuracy drops to first order. Finally, after considering several validation problems, the solver is shown to agree well with reference numerical and experimental data while retaining its robustness and efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Computational analysis of metal–metal bonded dimetal tetrabenzoate redox potentials in the context of ammonia oxidation electrocatalysis

Metal–metal bonded complexes are promising candidates for catalyzing redox transformations. Of particular interest is the oxidation of ammonia to dinitrogen, an important half reaction for the potential utilization of ammonia as a fuel or hydrogen carrier. This work computationally explores 30 different metal–metal bonded dimers (5 different metal centers and 6 different benzoate ligand derivatives) to explore the tunability of the redox potential when ammonia is bound to the complexes as an axial ligand, modeling the first step in ammonia oxidation electrocatalysis. We calculate the redox potentials of these compounds, making reference to experimental data when appropriate, identifying two degrees of tunability: a coarse adjustment, changing the metal center, allows for a wide range of redox potentials to be accessed (from +1.0 to –2.0 V vs. ferrocene/ferrocenium in acetonitrile solution) and a fine adjustment, the para-substituent of the benzoate derivative, which affects the redox potential in a smaller range based on the electron donating/withdrawing effects of the substituent. Ruthenium and osmium tetrabenzoate catalysts are prime candidates for next generation ammonia oxidation catalysts because their redox potentials fall within the direct ammonia fuel cell “viability zone” bracketed by the thermodynamic potentials of oxygen reduction (ORR) and nitrogen reduction (NRR). Rhodium tetrabenzoate species fall above the ORR potential, suggesting ammonia oxidation promoted by Rh 2 catalysts could instead be used to facilitate hydrogen production through coupling to hydrogen evolution at a cathode. The redox potentials of rhenium and iridium tetrabenzoate catalysts fall below the NRR potential suggesting that these compounds could be further investigated in the context of electrochemical ammonia synthesis. Each redox event studied involves electron transfer from the M–M δ* orbital regardless of choice of metal or benzoate ligand derivative; this leads us to believe that the chemical reactivity of the various studied compounds will be similar in the context of ammonia oxidation.

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

Wavelength- and irradiance-dependent changes in intracellular nitric oxide level

Photobiomodulation (PBM) refers to beneficial effects of low energy light absorption. Although there is a large and growing body of literature describing down-stream physiological benefits of PBM, there is limited understanding of the molecular mechanisms that initiate and mediate these effects at the level of photon absorption. At present, the leading hypothesis is that light absorption induces release of nitric oxide (NO) from the active site of cytochrome c oxidase (COX), allowing it to bind O 2 instead. This is believed to increase mitochondrial respiration, and result in greater overall health of the cell due to increased ATP production. Aim: Although NO itself is a powerful signaling molecule involved in a host of biological responses, less attention has been devoted to the mechanisms of NO release and subsequent effects on cell signaling. The purpose of this work is to investigate wavelength-specific effects on intracellular NO release in living cells. Approach: We have conducted in depth dosimetry analyses of NO production and function in an in vitro retinal model in response to low energy exposure to one or more wavelengths of laser light. Results: We found statistically significant wavelength-dependent elevations (10-30%) in intracellular NO levels following laser exposures at 447, 532, 635 or 808 nm. Sequential or simultaneous exposures to light at two different wavelengths enhanced the NO modulation up to 50% of unexposed controls. Additionally, the immediate increases in cellular NO levels were independent of the function of nitric oxide synthase, depended greatly on the substrate source of electrons entering the electron transport chain, and did not result in increased levels of cGMP. Conclusions: This study concludes the simple model of light-mediated release of NO from COX is unlikely to explain the wide variety of PBM effects reported in the literature. Our multi-wavelength method provides a novel tool for studying immediate and early mechanisms of PBM, as well as exploring intracellular NO signaling networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Spindown of Pulsars Interacting with Companion Winds: The Impact of Magnetospheric Compression

Pulsars in binary systems with strong companion winds can have the magnetopause separating their magnetosphere from the wind located well within their light cylinder. This bow-like enclosure effectively creates a waveguide that confines the pulsar's electromagnetic fields and can significantly alter its spindown. In this paper, we study the spindown of compressed pulsar magnetospheres in such systems. We parameterize the confinement as the ratio between the equatorial position of the magnetopause (or standoff distance) R m and the pulsar's light cylinder R LC . Using particle-in-cell simulations, we quantify the pulsar spindown for a range of compressions, R m /R LC = 1/3–1, and inclination angles, χ = 0°...90°, between magnetic and rotation axes. Our strongly confined models (R m /R LC = 1/3) show two distinct limits. For χ = 0°, the spindown of a compressed pulsar magnetosphere is enhanced by approximately a factor of three compared to an isolated pulsar due to the increased number of open magnetic field lines. Conversely, for χ = 90°, the compressed pulsar spins down at less than 40% of the rate of an isolated reference pulsar due to the mismatch between the pulsar wind stripe wavelength and the waveguide size. We apply our analysis to the 2.77 s oblique rotator (χ = 60°) in the double-pulsar system PSR J0737-3039. With the numerically derived spindown estimate, we constrain its surface magnetic field to B* ≈ (7.3 ± 0.2) × 10 11 G. We discuss the time modulation of its period derivative, the effects of compression on its braking index, and implications for the radio eclipse in PSR J0737-3039.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection Description This dataset contains input and output data for the manuscript Mongird, K. et al. (under review) titled "Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection". Input data corresponds to gridded spatial siting attributes that are necessary to conduct a random forest machine learning analysis of siting feature importance. Output data includes SHAP feature analysis outputs, and classification report values. For data on power plant siting results referred to in the manuscript, please refer to the CERF: IM3 Projected Western US Power Plant Locations data download page. The downloadable data includes values for eight different future scenarios for the Western US. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 Technical Information The dataset includes two sets of data files: (1) CERF gridded siting parameters and (2) Feature analysis outputs and classification reports. All downloadable data is in csv file format. Files with x/y coordinate information use the Albers Equal Area Conic projection (ESRI:102003). 1. CERF Gridded Siting Parameters This directory provides a balanced sample of gridded CERF siting parameters data for eight different scenarios for the Western US through 2055, seven different technologies, and eight timesteps. This data serves as input to the feature analysis. It contains the following parameters. region_name - name of region (i.e., state) sited - binary value representing whether the grid cell received a siting of that technology type (1=True) rcp - binary value representing scenario resource concentration pathway (0 = RCP4.5, 1 = RCP8.5) ssp - binary value representing scenario shared socioeconomic pathway (0 = SSP3, 1 = SSP5) climate - binary value representing cooler (0) or hotter (1) GCM forcing tech_name - generation technology name sited_year - year that values correspond to transmission_cost - cost of transmission interconnection pipeline_cost - cost of natural gas pipeline interconnection interconnection_cost - total interconnection cost (sum of transmission cost and gas pipeline cost) lmp - associated locational marginal value ($/MWh) associated with the grid cell, timestep, scenario, and technology xcoord - x-coordinate of location ycoord - y-coordinate of location 2a. Feature Analysis Output The dataset includes the feature analysis shap output for locational marginal price and interconnection cost. It contains the following parameters. technology - generator technology name scenario - name of scenario feature - name of feature, either locational_marginal_price or interconnection_cost value - the mean of absolute value of SHAP values for given feature 2b. Feature Analysis Classification Report This download includes the classification report associated with each random forest model. The dataset contains the following parameters. technology - generation technology name scenario - name of scenario test - one of precision (the proportion of predicted positives that are actually correct), recall (the proportion of actual positives that were correctly identified), f1-score (the harmonic mean of precision and recall) 0.0 - value of test for classification of 0 (grid cell not chosen for siting) 1.0 - value of test for classification of 1 (grid cell chosen for siting) accuracy - accuracy of model (i.e., fraction of all predictions that were right) macro avg - Simple average of test values for all classes weighted avg - Weighted average of test values for all classes, weighted based on Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Guanidinium-Assisted Surface Matrix Engineering for Highly Efficient Perovskite Quantum Dot Photovoltaics

Metal halide perovskite quantum dots (Pe-QDs) are of great interest in new-generation photovoltaics (PVs). However, it remains challenging in the construction of conductive and intact Pe-QD films to maximize their functionality. Herein, a ligand-assisted surface matrix strategy to engineer the surface and packing states of Pe-QD solids is demonstrated by a mild thermal annealing treatment after ligand exchange processing (referred to as “LE-TA”) triggered by guanidinium thiocyanate. The “LE-TA” method induces the formation of surface matrix on CsPbI 3 QDs, which is dominated by the cationic guanidinium (GA+) rather than the SCN-, maintaining the intact cubic structure and facilitating interparticle electrical interaction of QD solids. Consequently, the GA-matrix-confined CsPbI 3 QDs exhibit remarkably enhanced charge mobility and carrier diffusion length compared to control ones, leading to a champion power conversion efficiency of 15.21% when assembled in PVs, which is one of the highest among all Pe-QD solar cells. Additionally, the “LE-TA” method shows similar effects when applied to other Pe-QD PV systems like CsPbBr 3 and FAPbI 3 (FA = formamidinium), indicating its versatility in regulating the surfaces of various Pe-QDs. This work may afford new guidelines to construct electrically conductive and structurally intact Pe-QD solids for efficient optoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Design and application of a kinetic model of lipid metabolism in Saccharomyces cerevisiae

Lipid biosynthesis plays a vital role in living cells and has been increasingly engineered to overproduce various lipid-based chemicals. However, owing to the tightly constrained and interconnected nature of lipid biosynthesis, both understanding and engineering of lipid metabolism remain challenging, even with the help of mathematical models. Here we report the development of a kinetic metabolic model of lipid metabolism in Saccharomyces cerevisiae that integrates fatty acid biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism, storage lipids, lumped sterol synthesis, and the synthesis and transport of relevant target-chemicals, such as fatty acids and fatty alcohols. The model was trained on lipidomic data of a reference S. cerevisiae strain, single knockout mutants, and lipid overproduction strains reported in literature. The model was used to design mutants for fatty alcohol overproduction and the lipidomic analysis of the resultant mutant strains coupled with model-guided hypothesis led to discovery of a futile cycle in the triacylglycerol biosynthesis pathway. In addition, the model was used to explain successful and unsuccessful mutant designs in metabolic engineering literature. Thus, this kinetic model of lipid metabolism can not only enable the discovery of new phenomenon in lipid metabolism but also the engineering of mutant strains for overproduction of lipids.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

MOCVD Surface Preparation of V-Groove Si for III-V Growth

V-groove nanopatterning of Si substrates has recently demonstrated promise for achieving high-quality III-V-on-Si epitaxy while providing a lower-cost processing route than chemo-mechanical polishing to produce epi-ready planar wafers. A key factor in determining the crystalline quality of III-V buffer layers is the Si surface structure and its chemical composition. Unlike planar Si surfaces, the surfaces of V-grooves prior to growth have not been studied in detail. Here, we study the surface of V-groove Si prepared for GaP nucleation via X-ray photoelectron spectroscopy and low-energy electron diffraction. We identify several pretreatments, using both 830 and 1000 annealing under an As background pressure, as being suitable for deoxidizing and cleaning the V-groove Si surface. The V-groove Si was found to behave similarly to reference Si(0 0 1) and Si(1 1 1) planar samples, demonstrating that in situ techniques such as reflection anisotropy spectroscopy can be used on reference samples to infer the state of the V-groove surface, and indicating that the extensive research on planar Si surfaces can be directly applied to V-grooves.

III-V epitaxy↗
DOE OSTI2025

Dielectric constant engineering of nonfullerene acceptors enables a record fill factor of 83.58% and a high efficiency of 20.80% in organic solar cells

Organic solar cells (OSCs) have achieved power conversion efficiencies (PCEs) surpassing 20%, but their development remains hindered by the inherently low dielectric constant (εr) of organic semiconductors, which limits charge transport and contributes to serious recombination losses. Herein, we present a comprehensive strategy to overcome the challenge by engineering the dielectric properties of nonfullerene acceptors (NFAs). Two Y-series NFAs of BTP-N3F and BTP-C3F have been synthesized featuring trifluoromethyl (CF 3 ) end-capped alkyl side chains. This molecular design significantly enhances the dipole moment and ε r (up to ∼5.9) when compared to the reference acceptor Y6 (∼3), reducing the exciton binding energy (E b ) and improving charge transport. Furthermore, the incorporation of a high-εr polymer additive, poly(pentafluorostyrene) (PPFS), synergistically improves the active layer morphology and dielectric properties, enabling efficient charge extraction and reduced recombination losses. Devices based on the optimized D18-Cl/BTP-C3F system have achieved a record-high fill factor (FF) of 83.58% and an impressive PCE of 20.80%, setting a new benchmark for OSCs. Finally, our results underscore the pivotal role of ε r in enhancing device performance and establish a versatile pathway for advancing OSC efficiency and stability through molecular and morphological optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mechanical properties of zeolite-templated carbons from approximate density functional theory calculations

Zeolite-templated carbon (ZTC) is a unique porous carbonaceous material whose structure is ordered at the nanometre scale, enabling a representative periodic description at the atomistic level. Utilizing an existing, well-defined reference model for ZTCs, a structural library of varying compositions was developed by refinement using density-functional tight-binding (DFTB) potentials parameterized for materials science applications. We first determined the quantum chemical-refined structures of models with CH, CHO, CHON, CHOB, and CHOBN compositions with various degrees of heteroatom substitution. These structural models comprise the characteristic morphological features of highly porous carbon materials, such as open-blade surfaces, edges, saddles, and closed-strut formations, spanning a range of curvatures and characteristic sizes. Second, we carried out alternating compression and expansion of the CHO model unit cell to determine the lowest energy structure as well as to obtain its bulk modulus in order to demonstrate a close connection between macroscopic observations and atomic-scale structures. Further, the agreement between experimental measurements and the computational model is remarkable and demonstrates the power of approximate density functional theory as a cost-effective computational tool with chemical accuracy for the investigation of structure/property relationships in real-world carbon-based solids.

03 NATURAL GAS↗