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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Techno-Economic Optimization of the SurgeWEC Device

This is the post-access report for a Teamer-funded effort to optimize the SurgeWEC device, a near-shore pivoting flap wave energy conversion device used to desalinate water. Parametrically driven cost and performance models enabled an integrated optimization approach at the farm scale. The metric used for this study was the levelized cost of water (LCOW). The data-set includes: 1. A public-domain post access report 2. An excel file with the data and plots generated under this study

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Self-Buffering system for Cost-Effective production of lactic acid from glucose and xylose using Acid-Tolerant Issatchenkia orientalis

This study presents a cost-effective strategy for producing organic acids from glucose and xylose using the acid- tolerant yeast, Issatchenkia orientalis. I. orientalis was engineered to produce lactic acid from xylose, and the resulting strain, SD108XL, successfully converted sorghum hydrolysates into lactic acid. In order to enable low- pH fermentation, a self-buffering strategy, where the lactic acid generated by the SD108XL strain during fermentation served as a buffer, was developed. As a result, the SD108 strain produced 67 g/L of lactic acid from 73 g/L of glucose and 40 g/L of xylose, simulating a sugar composition of sorghum biomass hydrolysates. Moreover, techno-economic analysis underscored the efficiency of the self-buffering strategy in streamlining the downstream process, thereby reducing production costs. These results demonstrate the potential of I. orientalis as a platform strain for the cost-effective production of organic acids from cellulosic hydrolysates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Data for Self-Buffering System for Cost-Effective Production of Lactic Acid from Glucose and Xylose Using Acid-Tolerant Issatchenkia orientalis

This study presents a cost-effective strategy for producing organic acids from glucose and xylose using the acid-tolerant yeast, Issatchenkia orientalis . I. orientalis was engineered to produce lactic acid from xylose, and the resulting strain, SD108XL, successfully converted sorghum hydrolysates into lactic acid. In order to enable low-pH fermentation, a self-buffering strategy, where the lactic acid generated by the SD108XL strain during fermentation served as a buffer, was developed. As a result, the SD108 strain produced 67 g/L of lactic acid from 73 g/L of glucose and 40 g/L of xylose, simulating a sugar composition of sorghum biomass hydrolysates. Moreover, techno-economic analysis underscored the efficiency of the self-buffering strategy in streamlining the downstream process, thereby reducing production costs. These results demonstrate the potential of I. orientalis as a platform strain for the cost-effective production of organic acids from cellulosic hydrolysates.

Conversion↗
DOE OSTI2021

Visualizing plant cell wall changes proves the superiority of hydrochloric acid over sulfuric acid catalyzed γ-valerolactone pretreatment

Thermochemical pretreatment is one of the key steps to process lignocellulosic biomass for production of biofuels and biomaterials. γ-valerolactone (GVL) has been used as a green and renewable solvent to efficiently dissolve lignin under acidic conditions and enhance subsequent enzyme digestibility. Further improvement is still required to lower the pretreatment temperature in order to reduce sugar degradation and irreversible lignin condensation, as well as the capital cost. In this study, we compared the use of HCl and H 2 SO 4 as catalysts during GVL pretreatment, and found the performance of GVL-HCl at 100 °C was comparable to that of GVL-H 2 SO 4 at 120 °C in terms of xylan and lignin removal and enzyme digestibility. We further monitored the lignin removal and cellulose accessibility in the cell walls at different pretreatment time points by imaging the changes in lignin auto-fluorescence and in CtCBM3-GFP binding with confocal laser scanning microscopy (CLSM), respectively. We found GVL-HCl pretreatment at a relative low temperature (100 °C) could rapidly remove lignin in the compound middle lamella and cell corner areas, which concurred with the increase of CtCBM3-GFP binding in these areas. Real-time imaging of cell wall degradation by cellulases further revealed that the secondary cell walls could be digested from both cell lumen and CML sides, and eventually fully deconstructed within 24 h. Furthermore, our results provide new insight into the effects of chloride anions on plant cell wall structure during GVL pretreatment, and offer potential routes to further optimize and enhance the efficiency of GVL-based pretreatment.

09 BIOMASS FUELS↗
DOE OSTI2024

In tube condensation of low global warming potential refrigerants in an axial micro-fin aluminum tube

Environmental regulations have driven the development of refrigerants with low global warming potential (GWP). To design heat exchangers using these new refrigerants, data are needed concerning the heat transfer coefficient and pressure drop in two-phase flow. Another change is the increasing use of aluminum tubes rather than copper tubes to reduce heat exchanger cost. Hence, this study presents an experimental investigation of flow condensation using an expanded axial micro-fin aluminum tube with a fin-tip diameter of 5.96 mm. Here, the experiments included single compounds R-32, R-1234yf, and R-1234ze(E), zeotropic mixtures with low glide (R-454B), and zeotropic mixtures with high-glide (R-454C and R-455A). Experiments were conducted at condensation temperatures ranging from 40 °C to 50 °C, reduced pressures ranging from 0.21 to 0.55, and mass fluxes ranging from 150 to 350 kg/(m 2 s). Data obtained for these refrigerants constitute one of the first reports for high-glide refrigerants using axial micro-fin aluminum tubes. An evaluation of heat transfer degradation of zeotropic mixtures due to mass transfer resistance at the liquid/vapor interface is presented. This information can be used to design heat exchangers for next generation air conditioning and refrigeration systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Composite biopolymer electrolytes for high-performance reversible zinc-air batteries

Zinc-air batteries (ZABs) are regarded as one of the most promising candidates for next-generation energy storage systems due to their inherent safety, high energy density, and low cost. In this study, we optimized the composition of gel polymer electrolytes (GPEs), which play a crucial role in enhancing ZAB reversibility due to their water retention capabilities. The GPEs are synthesized using a combination of polyacrylic acid (PAA), polyacrylamide (PAM), and biopolymer kappa-carrageenan (KC), with and without additional additives. Among these, the composite GPE containing KC, PAA, and PAM demonstrated superior ionic conductivity (6.68 mS/cm at 30 °C) and enhanced water retention compared to the PAA–PAM-based GPE. Furthermore, this composite electrolyte operates within a wider electrochemical window of −1.0 V–2.2 V. A bifunctional catalyst, nickel–iron layered double hydroxide (NiFe-LDH), is electrochemically synthesized on a modified activated carbon cloth (CC). The catalyst loading is optimized based on oxygen evolution reaction (OER) performance, showing an overpotential of 555 mV. The oxygen reduction reaction (ORR) occurred at a half-wave potential of 0.620 V, which is lower than that of Pt/C (0.852 V), indicating better catalytic activity. The KC based cell exhibit better cycling stability than PAA–PAM-based GPE. The electrochemical performance of ZAB cells is evaluated under various atmospheres: ambient air and humid conditions and temperatures. Notably, in a humid environment, the cells achieved extended cycling stability, operating for over 500 h—significantly longer than under open-air conditions. Overall, this study highlights the importance of tailored GPE design and efficient bifunctional catalysts to enhance the performance and longevity of zinc-air batteries for long-duration energy storage applications.

Catalyst↗
DOE OSTI2024

Sustainability and affordability of building electrification: A state-by-state holistic approach for multifamily buildings

This paper explores the evolving narrative of building electrification, considering its potential to solve climate change. Previous research has predominantly focused on hybrid renewable energy portfolios and increasing home pricing premiums, overlooking the perspectives of renters burdened by housing costs. Moreover, existing studies have primarily examined the effects of electrification on single-family homes and case studies, neglecting multifamily buildings and their relationship with the grid. This comprehensive study leverages calibrated building energy models to address these gaps and evaluates sustainability through carbon dioxide emissions and affordability through economic performance. The findings demonstrate significant progress in electrification since 2017, with nearly all states showing decreased energy usage in the electrified models. Environmentally, twenty-two states perform better or comparably with the electrified models in the most recent study. Economically, challenges persist, but a sensitivity analysis demonstrates how results could improve soon. Furthermore, the study discusses the outlook of electrified multifamily buildings, considering ongoing decarbonization plans for the electric grid. In three case studies, electrification demonstrates improvements in over half of the states by 2026. Overall, the research highlights the importance of expanding analysis to include multifamily buildings and emphasizes the positive impact of electrification as a viable energy, environmental, and economic solution.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Internal Standard Triggered-Parallel Reaction Monitoring Mass Spectrometry Enables Multiplexed Quantification of Candidate Biomarkers in Plasma

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Previously, mass spectrometry-based approaches have been used to fill this gap but feature poor quantitative performance and were generally limited to hundreds of proteins. Here, we demonstrate the capability of an internal standard triggered-parallel reaction monitoring (IS-PRM) assay to greatly expand the numbers of candidates that can be tested with improved quantitative performance. The assay couples immunodepletion and fractionation with IS-PRM and was developed and implemented in human plasma to quantify 5176 peptides representing 1314 breast cancer biomarker candidates. Characterization of the IS-PRM assay demonstrated the precision (median % CV of 7.7%), linearity (median R 2 > 0.999 over 4 orders of magnitude), and sensitivity (median LLOQ < 1 fmol, approximately) to enable rank-ordering of candidate biomarkers for validation studies. Using three plasma pools from breast cancer patients and three control pools, 893 proteins were quantified, of which 162 candidate biomarkers were verified in at least one of the cancer pools and 22 were verified in all three cancer pools. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

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

Nonadiabatic Molecular Dynamics by Multiconfiguration Pair-Density Functional Theory

Herein, we present the first implementation of multiconfiguration pair-density functional theory (MC-PDFT) ab initio molecular dynamics. MC-PDFT is a multireference electronic structure method that in many cases has a similar accuracy (or even better accuracy) the complete active space second-order perturbation theory (CASPT2) at a significantly lower computational cost. In this study, we introduced MC-PDFT analytical gradients into the SHARC molecular dynamics program for ab initio, nonadiabatic molecular dynamics simulations. We verify our implementation by examining the intersystem crossing dynamics of thioformaldehyde, and we observe excellent agreement with recent CASPT2 and experimental findings. Moreover, with MC-PDFT, we could perform dynamics simulations with the 12 electron in 10 orbitals active space that was computationally too expensive for direct dynamics with CASPT2.

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

Amorphous Manganese Oxides: An Approach for Reversible Aqueous Zinc-Ion Batteries

Crystalline manganese oxides have attracted the most attention in aqueous zinc-ion batteries due to their diverse nanostructures and low cost. However, extensive studies on amorphous manganese oxides are lacking. Here in this paper, we report a mesoporous amorphous manganese oxide (UCT-1-250) as a cathode material with high capacity (222 mAh g –1 ), good cyclability (57% capacity retention after 200 cycles), and an acceptable discharge plateau (between 1.2 and 1.4 V). An approach to mechanistic studies was performed by comparison of UCT-1-250 and other crystalline manganese oxides through electrochemical, elemental, and structural analyses. An in situ conversion to ZnMn 2 O 4 spinel phase after initial cycling contributes to the high performance. The irreversible capacity fading is due to the formation of the woodruffite phase.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Influence of Alkyne Precursor Structure on Carbon Nanotube Chiral Distribution: Data-Dense Analysis Across Multiple Catalyst Types

Carbon nanotubes (CNTs) are a desirable material in the field of optoelectronics and semiconductors due to electronic properties (e.g., bandgap) that are dependent upon their chirality, defined by their diameter and lattice angle. Unfortunately, industrial-scale syntheses have yet to realize growth of a single desired chirality and instead rely on postsynthetic separation techniques to refine a chiral mixture, which increases process complexity and cost. Here, we studied the influence of precursor structure on chiral distribution, using a series of terminal alkyne precursors (acetylene, methylacetylene, vinylacetylene, 1-butyne, two enantiomers of 3-butyn-2-ol and a racemic mixture thereof) to grow CNTs across five transition-metal catalysts (Fe, FeMo, and three proportions of CoMo). Multiwavelength Raman spectroscopy on 5,145 spots (5 catalysts, 7 precursors, 3 lasers, and 49 distinct substrate locations on each) determined that acetylene grew the smallest diameter CNTs, while vinylacetylene produced fewer subnanometer CNTs. Though precursor structure did not dictate a uniform chiral shift, it was shown to broaden or narrow chiral distribution, while catalyst structure played a dominant role. In conclusion, this is consistent with metal-precursor binding occurring through unsaturated bonds in the hydrocarbons via the alkyne polymerization mechanism.

Carbon nanotubes↗
DOE OSTI2025

Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.

Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U↗
DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗
DOE OSTI2025

Techno-economic and life cycle analyses of the synthesis of a platinum–strontium titanate catalyst

The heterogeneous platinum/strontium titanate (Pt/SrTiO 3 or Pt/STO) catalyst has garnered significant attention as a promising candidate for the hydrogenolysis of polyolefins to hydrocarbon oils. This study evaluates the cost and environmental impacts of a newly developed scalable Pt/STO catalyst production, which includes the synthesis of the STO support and the deposition of Pt onto the support. This two-step synthesis plays a significant role in assessing the commercial feasibility of the catalyst, while energy consumption during the process plays an important role in its environmental impacts. The CatCost and the Research and Development Greenhouse Gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) models were used, respectively, to perform techno-economic analysis (TEA) and life cycle analysis (LCA) of the newly developed catalyst. The TEA showed that the raw materials, accounting for approximately 76% of the total operation cost, has a profound effect on the estimated catalyst cost, mainly due to the platinum precursor. The LCA findings indicated that the catalyst production generates greenhouse gas (GHG) emissions of 66 kg CO 2 e per kg, primarily due to the use of solvents and electricity in the process. The sensitivity analysis indicated that the total operating costs (OpEX), platinum precursor cost, and spent catalyst value (SCV) significantly impact the cost of the synthesized catalyst. Additionally, adopting solvent recovery strategies and using renewable electricity can reduce the GHG emissions of catalyst production to 29 kg CO 2 e per kg.

Heterogeneous Platinum/strontium titanate Catalyst↗
DOE OSTI2024

Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing

The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Needed computations and computational capabilities for stellarators

Stellarator plasmas are externally controlled to a degree unparalleled by any other fusion concept, magnetic or inertial. This control is largely through the magnetic fields produced by external coils. The development of fusion energy could be expedited by carrying out remarkably straight-forward computations to define strategies for exploiting this external control. In addition to these computations, which have a reliability limited only by competence, certain physics areas that affect the development of stellarator power plants should have a more intense study. The low cost and speed with which computations can be carried out relative to experiments have implications for the development of fusion. Computations should be used to develop a strategy that to the extent possible allows major issues to be circumvented. Required computations for this strategy are the subject of this paper.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Modeling prebiotic chemistries with quantum accuracy at classical costs

Molecular Dynamics (MD) simulations using classical force-fields are commonly employed in numerous scientific investigations. However, many natural processes involve bond breaking and quantum forces. This complexity is compounded by the presence of multiple competing length and timescales. For example, accurately modeling the thermodynamics and dynamics of a chemical reaction requires accounting for the concerted movements of numerous solvent molecules and ions with their own fast or slow timescales. While widely used static Density Functional Theory (DFT) calculations at 0 temperature can be beneficial for such investigations, they do not account for dynamics, and lack precision in describing the molecular environments. They particularly fail at correct, rigorous treatments of finite-temperature fluctuations, and thus generalization to experimentally relevant conditions. In PNAS Benayad et al develop a scalable, generalizable approach for designing Neural Network Potentials (NNPs) that can handle chemical reactivity in solvated systems with quantum accuracy at classical costs. Specifically, they study phosphoester bond formation and rupture, which is fundamentally relevant to the Phosphorus-Oxygen bond formation central to life, and especially for the RNA world hypothesis. The framework developed here has the potential to generalize to different chemical reactions of energy and biological relevance.

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

Efficient emulation of relativistic heavy ion collisions with transfer learning

Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on these properties must combine measurements from different collision systems and methodically account for experimental and theoretical uncertainties. Such studies require a vast number of costly numerical simulations. While computationally inexpensive surrogate models (“emulators”) can be used to efficiently approximate the predictions of heavy ion simulations across a broad range of model parameters, training a reliable emulator remains a computationally expensive task. We use transfer learning to map the parameter dependencies of one model emulator onto another, leveraging similarities between different simulations of heavy ion collisions. By limiting the need for large numbers of simulations to only one of the emulators, this technique reduces the numerical cost of comprehensive uncertainty quantification when studying multiple collision systems and exploring different models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗