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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ambient Synthesis for Fe(II) Polypyridyl Complexes with an Order of Magnitude Increase in Charge-Transfer Excited-State Lifetimes over [Fe(bpy) 3 ] 2+

Replacing precious metals with abundant metals is an important research focus in photochemical energy conversion and storage to meet global energy demands. However, transition metal complexes (TMCs) based on abundant 3d metals typically possess photochemical disadvantages─such as short charge-transfer excited-state lifetimes─and molecular modifications have focused on optimizing the interplay of structure, dynamics, and energetics to overcome their limitations. One strategy to do so is the use of bespoke ligands that can extend the lifetimes of chemically useful excited states. Here, in this study, we report the synthesis and characterization of novel Fe(II) complexes featuring lengthy polypyridyl ligands that can be readily synthesized. Steady-state and transient absorption spectroscopies indicate that these complexes have desirable properties and their excited metal-to-ligand charge-transfer states live an order of magnitude longer than in the benchmark [Fe(bpy) 3 ] 2+ . This lifetime is largely preserved in the heteroleptic complexes, thereby enabling the preparation of asymmetric complexes. Additionally, we apply nonradiative transition theory to explain the long-time decay kinetics. In light of their ease of preparation and reasonable excited-state lifetimes, we suggest the use of these complexes in Fe(II) dye-sensitized solar cells, where the rate of charge injection would be competitive with increased lifetime.

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

Translocation mechanism of xeroderma pigmentosum group D protein on single-stranded DNA and genetic disease etiology

Abstract XPD is a key nucleotide excision repair (NER) protein whose function is vital for genome integrity. During NER, XPD serves as a 5′−3′ single-strand DNA translocase that enables lesion scanning and verification in genomic DNA. Yet, its translocation mechanism is incompletely understood. Here we use molecular simulations and chain-of-replicas path optimization methods to model the ATP-driven translocation mechanisms of XPD and its bacterial homolog DinG, revealing all on-path metastable intermediates and corresponding kinetic rates. We identify the XPD(DinG) global domain motions that modulate the strength of DNA association at the opposing ends of the DNA-binding groove. During the ATP hydrolysis cycle, alternating weak and strong interactions at two defined groove constrictions enable DNA reptation and forward displacement of the ATPase. Moreover, we show that DNA- or ATP-binding residues directly involved in translocation are hotspots for genetic disease mutations. Thus, our findings shed light on the etiology of XPD-associated genetic syndromes.

Paul, Tanmoy↗
DOE OSTI2025

Opportunity Assessment for Sustainable Aviation Fuel Production from Woody Biomass via Ex Situ Catalytic Fast Pyrolysis and Refinery Hydroprocessing

Biofuels have become a promising solution to reduce emissions in hard-to-electrify transportation sectors, such as aviation. However, several biofuel conversion pathways will likely be needed to scale sustainable aviation fuel (SAF) production to meet lower carbon intensity goals when biomass feedstock availability constraints are considered. This study evaluates the future potential of the catalytic fast pyrolysis (CFP) pathway to contribute to U.S. SAF production goals as outlined in the U.S. Department of Energy’s (DOE) SAF Grand Challenge, understanding that the CFP pathway is still under development toward maturity and scaleup. National forestland resource data from the DOE’s 2023 Billion Ton Study are integrated with recent experimental results demonstrating end-to-end woody biomass conversion to SAF via CFP and hydroprocessing in a novel bioeconomy optimization framework. U.S. refinery hydroprocessing capacity is also considered with repurposing and coprocessing strategies. Results indicate that the CFP process can contribute up to 4.6 billion gallons of SAF annually by 2040, meeting 13% of the 2050 SAF Grand Challenge target while reducing the carbon intensity (CI) of the U.S. jet fuel pool by 16%. The study further explores optimal processing strategies, suggesting that colocating CFP operations with existing petroleum refineries and repurposing some U.S. hydrocracking capacity may offer significant cost advantages and enhance the commercial viability of the CFP pathway. These results underscore the CFP pathway’s potential to support the global aviation industry’s lower carbon intensity objectives while producing other renewable fuels and products.

09 BIOMASS FUELS↗
DOE OSTI2025

Automated Resonance Fitting for Nuclear Data Evaluation

Global and national efforts to deliver high-quality nuclear data to users have a wide-ranging impact, affecting applications in national security, reactor operations, basic science, medicine, and more. Cross section evaluation is a major part of this effort, combining theory and experimentation to produce recommended values and uncertainties for reaction probabilities. Resonance region evaluation is a specialized type of nuclear data evaluation that can require significant manual effort and months of time from expert scientists. In this article, non-convex non-linear optimization methods are combined with concepts of inferential statistics to infer a resonance model from experimental data in an automated manner that is not dependent on prior evaluation(s). This methodology aims to enhance the workflow of a resonance evaluator by minimizing time, effort, and the potential for bias from prior assumptions, while enhancing reproducibility and documentation, thereby addressing well-known challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

An Evaluation of the Effect of Network Cost Optimization for Leadership Class Supercomputers

Dragonfly-based networks are an extensively deployed network topology in large-scale high-performance computing due to their cost-effectiveness and efficiency. The US will soon have three Exascale supercomputers for leadership class workloads deployed using dragonfly networks. Compared to indirect networks of similar scale, the dragonfly network has considerably reduced cable lengths, cable counts, and switch counts, resulting in significant network cost savings for a given system size, however, these cost reductions result in reduced global minimal paths and more challenging routing. Additionally, large scale dragonfly networks often require a taper at the global link level, resulting in less bisection bandwidth than is achievable in other traditional non-blocking topologies of equivalent scale. While dragonfly networks have been extensively studied, they have yet to be fully evaluated in an extreme scale (i.e., exascale) system that targets capability workloads. In this paper, we present the results of the first large scale evaluation of a dragonfly network on an exascale system (Frontier) and compare its behavior to a similar scale fat-tree network on a previous generation TOP500 system (Summit). This evaluation aims to determine the effect of network cost optimizations by measuring a tapered topology’s impact on capability workloads. Our evaluation is based on a collection of synthetic microbenchmarks, mini-apps, and full scale applications. It compares the scaling efficiencies of each benchmark between the dragonfly-based Frontier and the fat-tree-based Summit systems. Our results show that a dragonfly network is $\sim \mathbf{3 0 \%}$ more cost efficient than a fat-tree topology, which amortizes to $\sim 3 \%$ of an exascale system cost. Furthermore, while tapered dragonfly networks impose significant tradeoffs, the impacts are not as broad as initially thought and are mostly seen in applications with global communication patterns, particularly all-to-all (e.g., FFT-based algorithms), but also local communication patterns (e.g., nearest-neighbor algorithms) that are sensitive to network performance variability.

Khan, Awais↗
DOE OSTI2026

Integrative SP3 Workflow for Multi-PTM Proteomics Profiling (TZ-DP0)

The goal of the experiment was to demonstrate that the optimized multiplexed multi-PTM profiling workflow can comprehensively and quantitatively capture dynamic changes in protein abundance, cysteine oxidation, phosphorylation, and acetylation in cytokine-induced inflammatory stress in mouse pancreatic ß-cells. Global proteomic, redox proteomic, phosphoproteomic, and acetylomic were data collected from mouse Beta-TC-6 pancreatic Beta-cells, untreated (mock) and cytokine-treated Beta-cells at 4, 8, and 24 hours with 4 biological replicates. Samples were digested with trypsin and Lys-C, then analyzed by LC-MS/MS. Data were searched with MS-GF+, MASIC, and MaxQuant using PNNL's DMS processing pipeline.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Comparative life cycle assessment of a modular cross-laminated timber residential building designed for disassembly and reuse versus traditional wood frame construction

There is a need for affordable housing across the U.S., with high-performance modular and prefabricated buildings providing a logical avenue for meeting some of this demand. However, there is a need to balance high performance construction – including low emissions – with affordability. To provide a proof-of-concept in meeting these goals, the Circular Home is a cross-laminated timber (CLT)-based deconstructible and reconfigurable single-family residence that meets high performance targets in moisture, energy, design, economics, and life cycle assessment (LCA). This study focuses on the LCA, presenting a cradle-to-cradle whole-building life cycle assessment (WBLCA) for the Circular Home and a functionally equivalent Baseline Home constructed with traditional materials and methods. The functional unit is 1 m 2 of gross floor area across 60 years. Revit building information models (BIM) provided material quantities and Tally LCA was utilized for impact data (inclusive of biogenic carbon sequestration), supplemented with manufacturer environmental product declarations (EPDs). The Circular Home outperforms the baseline residence in most measured impact categories, including global warming potential (GWP), producing −2.73 kgCO 2 eq/m 2 in embodied emissions, whereas the modeled baseline has an embodied GWP of 428 kgCO 2 eq/m 2 . The careful material selection and advanced building design optimizes performance, with the Circular Home containing only −0.006 times the embodied emissions and −0.02 times the operational emissions of its traditional counterpart. Finally, the unique contribution of this work is in the environmental impact comparison of a high-performance modular CLT structure that can be affordably scaled and mass produced in a U.S. market, compared to typical single family home construction.

Circularity↗
DOE OSTI2024

Bridging length scales in hard materials with ultra-small angle X-ray scattering – a critical review

Owing to their exceptional properties, hard materials such as advanced ceramics, metals and composites have enormous economic and societal value, with applications across numerous industries. Understanding their microstructural characteristics is crucial for enhancing their performance, materials development and unleashing their potential for future innovative applications. However, their microstructures are unambiguously hierarchical and typically span several length scales, from sub-ångstrom to micrometres, posing demanding challenges for their characterization, especially for in situ characterization which is critical to understanding the kinetic processes controlling microstructure formation. This review provides a comprehensive description of the rapidly developing technique of ultra-small angle X-ray scattering (USAXS), a nondestructive method for probing the nano-to-micrometre scale features of hard materials. USAXS and its complementary techniques, when developed for and applied to hard materials, offer valuable insights into their porosity, grain size, phase composition and inhomogeneities. We discuss the fundamental principles, instrumentation, advantages, challenges and global status of USAXS for hard materials. Using selected examples, we demonstrate the potential of this technique for unveiling the microstructural characteristics of hard materials and its relevance to advanced materials development and manufacturing process optimization. We also provide our perspective on the opportunities and challenges for the continued development of USAXS, including multimodal characterization, coherent scattering, time-resolved studies, machine learning and autonomous experiments. Our goal is to stimulate further implementation and exploration of USAXS techniques and inspire their broader adoption across various domains of hard materials science, thereby driving the field toward discoveries and further developments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimality of Gradient-MUSIC for Spectral Estimation

We introduce the Gradient-MUSIC algorithm for estimating the unknown frequencies and amplitudes of a nonharmonic signal from noisy time samples. While the classical MUSIC algorithm performs a computationally expensive search over a fine grid, Gradient-MUSIC is significantly more efficient and eliminates the need for discretization over a fine grid by using optimization techniques. It coarsely scans the 1D landscape to find initialization simultaneously for all frequencies followed by parallelizable local refinement via gradient descent. We also analyze its performance when the noise level is sufficiently small and the signal frequencies are separated by at least 8π/m, where π/m is the standard resolution of this problem. Even though the 1D landscape is nonconvex, we prove a global convergence result for Gradient-MUSIC: coarse scanning provably finds suitable initialization and gradient descent converges at a linear rate. In addition to convergence results, we also upper bound the error between the true signal frequencies and amplitudes with those found by Gradient-MUSIC. For example, if the noise has $\ell^\infty$ norm at most ϵ, then the frequencies and amplitudes are recovered up to error at most Cϵ/m and Cϵ respectively, which are minimax optimal in m and ϵ. Our theory can also handle stochastic noise with performance guarantees under nonstationary independent Gaussian noise. Our main approach is a comprehensive geometric analysis of the landscape, a perspective that has not been explored before.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Scaling the Offshore Wind Industry and Optimizing Turbine Size

NYSERDA's Offshore Wind team is hosting an educational webinar series to connect the public with independent experts in key topics in offshore wind, including wind farm technologies, development practices, regulatory processes, and research initiatives. The presentation focuses on the challenges and opportunities of wind turbine upscaling in the global offshore wind market place. It addresses key concerns about risks of new technology and the opportunity cost of increasing turbine size too rapidly.

ENERGY PLANNING, POLICY, AND ECONOMY,WIND ENERGY↗
DOE OSTI2024

Surrogate Model Guided Optimization of Expensive Black-Box Multi-Objective Problems: A Posteriori Methods

Many engineering applications require the simultaneous optimization of multiple conflicting objective functions. Often, these objective functions are evaluated using highly accurate computer simulations that are computationally too expensive to be evaluated hundreds or thousands of times during optimization. Thus, the goal is to find good approximations of the Pareto front using as few of these expensive simulations as possible. Here, we describe an optimization approach based on surrogate models and diverse sampling strategies to accelerate the search for the Pareto solutions. We use a separate surrogate model for approximating each objective function and then we use the surrogate models to inform where additional expensive simulations should be run. The surrogate models are updated in an active learning framework whenever new information from the expensive simulations becomes available. The sampling strategies aim at balancing local improvements of the approximate Pareto front and global exploration to identify the extrema and fill in large gaps of the approximate Pareto front. We demonstrate on a large set of benchmark problems the effectiveness of the method for finding good approximations of the Pareto front.

MATHEMATICS AND COMPUTING↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗
DOE OSTI2025

Methods integrating innate and adaptive immune responses in human in vitro immunization assays

Rapid vaccine development and innovative immunotherapeutics are critical in the fight against emerging outbreaks and global pandemic threats, yet the high costs and prolonged timelines for developing new vaccines underscore the urgent need for robust, predictive pre-clinical testing platforms. The rapid down-selection of vaccine candidates and identification of optimal vaccine formulations can be performed using human in vitro immunization (IVI) assays that recapitulate the complex interactions of the innate and adaptive human immune response. In this review, we present a comprehensive evaluation of three key IVI platforms: the whole blood assay (WBA), monocyte-derived dendritic cell (MoDC) assay with dendritic cell-T cell interface assay (DTI), and the microphysiological human tissue construct assay (HTC). The WBA offers a cost-effective and straightforward approach, while the MoDC + DTI system represents the current gold standard for balancing experimental efficiency with immunological complexity. The HTC assay, by mimicking both spatial and temporal aspects of immune interactions, provides enhanced physiological relevance. We discuss the methodological advantages and limitations of each platform, explore their roles in rapid vaccine candidate screening, and propose strategies for integrating these assays with complementary in vivo models. These insights pave the way for refining IVI assays and accelerating the translational pipeline for next-generation vaccines and immunotherapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Parametric reduced order models for graded lattice structures

Graded lattice structures, characterized by smoothly varying mechanical properties, hold significant promise for optimizing material distribution in advanced engineering applications. However, accurately modeling these structures poses substantial computational challenges due to the continuous geometric variations within their unit cells. Here, to address these challenges, this paper introduces a novel Efficient Reduced Order Model (EROM) that integrates the Matrix Discrete Empirical Interpolation Method (MDEIM) and Discrete Empirical Interpolation Method (DEIM) with polynomial regression to efficiently manage geometric parametrization in lattice structures. Unlike traditional reduced order models (ROMs) that require extensive precomputed libraries for each geometric configuration, our approach enables continuous geometric variations through a flexible algebraic formulation, significantly reducing computational costs while preserving high accuracy. The method constructs projection matrices for individual unit cells that can be efficiently assembled into global systems, leveraging the repetitive nature of lattice structures. Numerical studies demonstrate that our EROM achieves displacement errors below 1% and von Mises stress prediction errors below 4%, coupled with computational speedups exceeding two orders of magnitude compared to full-order simulations. The proposed method's modularity and scalability make it particularly suitable for design optimization and real-time simulation of functionally graded lattice structures, with applications spanning aerospace to biomedical engineering.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Effects of human presence on African mammal waterhole attendance and temporal activity patterns

Abstract Human impacts on the environment and wildlife populations are increasing globally, threatening thousands of species with extinction. While wildlife‐based tourism is beneficial for educating tourists, generating income for conservation efforts, and providing local employment, more information is needed to understand how this industry may impact wildlife. In this study, we used motion‐activated cameras at 12 waterholes on a private game reserve in northern Namibia to determine if the presence of humans and permanent infrastructure affected mammal visits by examining their (1) number of visits, (2) time spent, and (3) diel activity patterns. Our results revealed no differences in the number of visits based on human presence for any of the 17 mammal species studied. However, giraffes ( Giraffe camelopardalis ) spent more time at waterholes before observer presence compared to during. Additionally, several species changed diel activity patterns when human observers were present. Notably, several carnivore and ungulate species increased overlap in their activity patterns during periods while humans were present relative to when humans were absent. These modifications of mammal temporal activity patterns due to human presence could eventually lead to changes in community structure and trophic dynamics because of altered predator–prey interactions. As humans continue to expand into wildlife habitats, and wildlife‐based tourism increases globally, it is imperative that we fully understand the effects of anthropogenic pressures on mammal behavior. Monitoring of wildlife behavioral changes in response to human activity is crucial to further develop wildlife tourism opportunities in a way that optimizes the impact of conservation goals.

Patterson, J. R. [Savannah River Ecology Lab, Warn↗
DOE OSTI2026

A framework and tool for designing cost-effective, resilient, and circular net-zero supply chains under uncertainty with an application to multilayer plastic films

While 55% of Fortune 500 companies have committed to achieving net-zero emissions and/or zero-waste operations by 2035, only 2% are currently on track, revealing a critical gap between ambition and action. Designing supply chains that reduce both emissions and waste is a complex non-intuitive, multi-objective challenge, compounded by the high costs of new technologies and the need for resilient, profitable solutions. This paper aims to address this challenge by presenting a generic framework and multi-objective optimization formulation for designing cost-effective, circular, and resilient supply chains under uncertainty, implemented through a user-friendly decision-support tool with intuitive data visualization capabilities, enabling communication of results to both technical and non-technical stakeholders. We demonstrate the application of this framework in the context of multilayer plastic films (barrier films), which are widely used in food packaging and composite materials. The model quantifies trade-offs across three objectives: minimizing global warming potential, maximizing circularity, and minimizing cost. A key contribution of this work is the explicit modeling of technological resilience, the ability of supply chains to maintain function under disruption. In the cost-minimization case, the resilience constraint makes the design approximately three times more expensive in the short-term metric, but shifts the system from relying on a single recovery pathway to a portfolio of four recovery pathways, improving the robustness of the optimization solution under uncertainty. Lastly, we introduce TranZero, a decision-support tool that integrates material flow analysis, hotspot identification, and optimization-based scenario planning to support net-zero and circularity decisions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Uncertainty quantification and reliability assessment for intermodal freight transportation

Intermodal freight optimization models support cost-effective, low-emission, and timely goods movement by coordinating trucks, rail, and barges. These models determine optimal flows, routing, and modal switches while respecting infrastructure and operational constraints. However, their real-world utility is often undermined by pervasive uncertainties-such as fluctuating transportation costs and emissions, variable terminal capacities, and uncertain freight demand-that distort key performance outcomes, including total system cost, carbon footprint, and transit time reliability. This study presents a structured framework for quantifying uncertainty in intermodal freight transportation (IFT) optimization. The framework evaluates how input uncertainty affects system performance and reliability, a critical need for ensuring that model-based decisions remain robust under real-world variability, especially amid volatile fuel prices, shifting demand, and growing disruptions. It integrates three complementary methods: (1) Sobol-based global sensitivity analysis to identify influential parameters affecting cost, emissions, and transit time, (2) Monte Carlo-based capacity perturbation analysis to assess robustness under probabilistic facility disruptions, and (3) Monte Carlo filtering with Bayesian inference to detect threshold-based performance vulnerabilities. The results highlight diesel truck unit cost as the dominant driver of variability. To improve system resilience, planners should prioritize uncertainty in fuel-related parameters when designing intermodal strategies.

Intermodal freight transportation↗