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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The sponge effect and carbon emission mitigation potentials of the global cement cycle

Cement plays a dual role in the global carbon cycle like a sponge: its massive production contributes significantly to present-day global anthropogenic CO 2 emissions, yet its hydrated products gradually reabsorb substantial amounts of atmospheric CO 2 (carbonation) in the future. The role of this sponge effect along the cement cycle (including production, use, and demolition) in carbon emissions mitigation, however, remains hitherto unexplored. Here, we quantify the effects of demand- and supply-side mitigation measures considering this material-energy-emissions-uptake nexus, finding that climate goals would be imperiled if the growth of cement stocks continues. Future reabsorption of CO 2 will be significant (~30% of cumulative CO 2 emissions from 2015 to 2100), but climate goal compliant net CO 2 emissions reduction along the global cement cycle will require both radical technology advancements (e.g., carbon capture and storage) and widespread deployment of material efficiency measures, which go beyond those envisaged in current technology roadmaps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A global comparison of building decarbonization scenarios by 2050 towards 1.5–2 °C targets

Buildings play a key role in the transition to a low-carbon-energy system and in achieving Paris Agreement climate targets. Analyzing potential scenarios for building decarbonization in different socioeconomic contexts is a crucial step to develop national and transnational roadmaps to achieve global emission reduction targets. This study integrates building stock energy models for 32 countries across four continents to create carbon emission mitigation reference scenarios and decarbonization scenarios by 2050, covering 60% of today’s global building emissions. These decarbonization pathways are compared to those from global models. Results demonstrate that reference scenarios are in all countries insufficient to achieve substantial decarbonization and lead, in some regions, to significant increases, i.e., China and South America. Decarbonization scenarios lead to substantial carbon reductions within the range projected in the 2 °C scenario but are still insufficient to achieve the decarbonization goals under the 1.5 °C scenario.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Towards atom-level understanding of metal oxide catalysts for the oxygen evolution reaction with machine learning

Abstract Green hydrogen production is crucial for a sustainable future, but current catalysts for the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce optimal designs, particularly through the calculation of descriptors for activity. In this study, we develop a dataset of density functional theory calculations of bulk and surface perovskite oxides, and adsorption energies of OER intermediates, which includes compositions up to quaternary and facets up to (555). We demonstrate that per-site properties of perovskite oxides such as Bader charge or band center can be tuned through element substitution and faceting, and develop a machine learning model that accurately predicts these properties directly from the local chemical environment. We leverage these per-site properties to identify promising perovskites with high theoretical OER activity. The identified design principles and promising materials provide a roadmap for closing the gap between current artificial catalysts and biological enzymes such as photosystem II.

Chemistry↗
DOE OSTI2022

The living interface between synthetic biology and biomaterial design

Recent far-reaching advances in synthetic biology have yielded exciting tools for the creation of new materials. Conversely, advances in the fundamental understanding of soft-condensed matter, polymers and biomaterials offer new avenues to extend the reach of synthetic biology. The broad and exciting range of possible applications have substantial implications to address grand challenges in health, biotechnology and sustainability. Despite the potentially transformative impact that lies at the interface of synthetic biology and biomaterials, the two fields have, so far, progressed mostly separately. This Perspective provides a review of recent key advances in these two fields, and a roadmap for collaboration at the interface between the two communities. Here we highlight the near-term applications of this interface to the development of hierarchically structured biomaterials, from bioinspired building blocks to 'living' materials that sense and respond based on the reciprocal interactions between materials and embedded cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A US perspective on closing the carbon cycle to defossilize difficult-to-electrify segments of our economy

Electrification to reduce or eliminate greenhouse gas emissions is essential to mitigate climate change. However, a substantial portion of our manufacturing and transportation infrastructure will be difficult to electrify and/or will continue to use carbon as a key component, including areas in aviation, heavy-duty and marine transportation, and the chemical industry. In this Roadmap, we explore how multidisciplinary approaches will enable us to close the carbon cycle and create a circular economy by defossilizing these difficult-to-electrify areas and those that will continue to need carbon. Here, we discuss two approaches for this: developing carbon alternatives and improving our ability to reuse carbon, enabled by separations. Furthermore, we posit that co-design and use-driven fundamental science are essential to reach aggressive greenhouse gas reduction targets.

09 BIOMASS FUELS↗
DOE OSTI2020

Expanded encyclopaedias of DNA elements in the human and mouse genomes

The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Cryo-EM structures of PP2A:B55–FAM122A and PP2A:B55–ARPP19

Progression through the cell cycle is controlled by regulated and abrupt changes in phosphorylation. Mitotic entry is initiated by increased phosphorylation of mitotic proteins, a process driven by kinases, whereas mitotic exit is achieved by counteracting dephosphorylation, a process driven by phosphatases, especially PP2A:B55. Although the role of kinases in mitotic entry is well established, recent data have shown that mitosis is only successfully initiated when the counterbalancing phosphatases are also inhibited. Inhibition of PP2A:B55 is achieved by the intrinsically disordered proteins ARPP19 and FAM122A. Despite their critical roles in mitosis, the mechanisms by which they achieve PP2A:B55 inhibition is unknown. Here, we report the single-particle cryo-electron microscopy structures of PP2A:B55 bound to phosphorylated ARPP19 and FAM122A. Consistent with our complementary NMR spectroscopy studies, both intrinsically disordered proteins bind PP2A:B55, but do so in highly distinct manners, leveraging multiple distinct binding sites on B55. Our extensive structural, biophysical and biochemical data explain how substrates and inhibitors are recruited to PP2A:B55 and provide a molecular roadmap for the development of therapeutic interventions for PP2A:B55-related diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Challenges and Opportunities in the Enzymatic Recycling of Nylons

Enzymatic depolymerization has the potential to contribute to nylon waste recycling. However, the implementation of a viable industrial process still lags far behind progress in enzymatic polyester recycling. Here we review the current biocatalytic nylon recycling landscape, highlighting biochemical, structural, and materials science barriers that currently limit depolymerization extents. We detail efforts to identify, engineer, and characterize new nylon depolymerases, where currently even the best biocatalysts rarely exceed ~1 wt% conversion of solid polymer to products. Based on our analyses, we suggest that limited substrate accessibility due to substrate crystallinity and hydrogen bonding, rather than intrinsic enzyme inefficiency or instability, is the primary bottleneck to enhanced depolymerization. Guided by successes in polyester enzymatic recycling, we outline a research roadmap combining nylonase engineering with polymer pretreatment, chemo-enzymatic cascades, and process analyses to accelerate development of viable enzymatic nylon recycling processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Catalytic pyrolysis as a platform technology for supporting the circular carbon economy

Catalytic pyrolysis, a process that combines pyrolysis and vapour-phase catalytic upgrading, is a versatile technology platform capable of direct liquefaction of biomass and waste plastic into intermediates that can enable the decarbonized production of chemicals and/or transportation fuels. Recently, catalytic pyrolysis has attracted substantial research and commercialization attention, with over 15,000 journal articles and patents published in the past decade alone. Here, in this Perspective, we chart a path towards commercial-scale catalytic pyrolysis of waste plastic and biomass by identifying key short-term and long-term technological barriers. Within the proposed development roadmap addressing these barriers, catalytic pyrolysis can move from the demonstration scale to integrated biorefinery networks producing fuels and plastics precursors at a scale of between 0.1 and 1 billion tonnes of carbon per year.

09 BIOMASS FUELS↗
DOE OSTI2025

Global tuning of hadronic interaction models with accelerator-based and astroparticle data

In high-energy and astroparticle physics, event generators play an essential role, even in the simplest data analyses. As analysis techniques become more sophisticated, e.g. based on deep neural networks, their correct description of the observed event characteristics becomes even more important. Physical processes occurring in hadronic collisions are simulated within a Monte Carlo framework. A major challenge is the modeling of hadron dynamics at low momentum transfer, which includes the initial and final phases of every hadronic collision. QCD-inspired phenomenological models used for these phases cannot guarantee completeness or correctness over the full phase space. These models usually include parameters which must be tuned to suitable experimental data. Until now, event generators have been developed and tuned mainly on the basis of data from high-energy physics experiments at accelerators. The wealth of data available from the latest generation of astroparticle experiments has not yet been fully exploited, and in many cases is not satisfactorily described. Both kinds of data sets are complementary as astroparticle experiments provide sensitivity especially to hadrons produced nearly parallel to the collision axis and cover center-of-mass energies up to several hundred TeV, well beyond those reached at colliders so far. In this report, we provide an overview of state-of-the-art event generators and their tuning, including the most relevant inputs from high-energy accelerator and astroparticle experiments. We present a road map that shows, for the first time, how the unified tuning of event generators with accelerator-based and astroparticle data can be performed.

Albrecht, J. [Ruhr U., Bochum, RAPP Ctr.; Ruhr U.,↗
DOE OSTI2023

Federated benchmarking of medical artificial intelligence with MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs

Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. Here, in this study, we report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core–shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in fields such as biosensing, super-resolution microscopy and three-dimensional printing, can emit visible and ultraviolet light from near-infrared excitations. We report a large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use these data to train a heterogeneous graph neural network using a physically motivated representation of UCNP nanostructure. Applying gradient-based optimization on the trained graph neural network, we identify structures with 6.5× higher predicted emission under 800-nm illumination than any UCNP in our training set. Our work reveals design principles for UCNP heterostructures and presents a roadmap for DL-based inverse design of nanomaterials.

Sivonxay, Eric [Lawrence Berkeley National Laborat↗
DOE OSTI2020

Colloidal quantum wells for optoelectronic devices

Colloidal quantum wells, also called nanoplatelets, are nanoscopic materials displaying quantum confinement in two dimensions. Unlike colloidal quantum dots, colloidal quantum well ensembles have no inhomogeneous broadening due to an atomically-precise definition of the short axis, a fact which results in much narrower ensemble absorption and emission. Thus, colloidal quantum wells can translate many advantages of colloidal nanocrsytals or other solution-processable materials, such as scalable synthesis and substrate-agnostic deposition (particularly compared to epitaxial quantum wells), without sacrificing material uniformity. Furthermore, due to very narrow photoluminescnece peaks, these materials have found a home in applications involving light emission, such as downconversion enhancement films, light-emitting diodes, and lasers, in which they represent some of the best performers among solution-cast materials. As argued in this review, the full spectrum of epitaxial quantum well devices offers a roadmap to other potential applications, such as detection, electronics, electro-optics, non-linear optics, or intersubband devices, in which only nascent efforts have been made.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Colloidal quantum dot based infrared detectors: extending to the mid-infrared and moving from the lab to the field

Quantum dots (QDs) that absorb in the mid-wave infrared (MWIR) regime (3–5 μm) have recently generated significant interest as possible detector materials for MWIR cameras, with promises to reduce materials and device fabrication costs and potentially increase device operating temperatures. However, these materials have been primarily explored in single pixel devices – not multi-pixel cameras – and their current performance lags behind that of commercially available MWIR cameras based on single crystalline materials. To realize the potential of QD MWIR photodetectors with imaging capabilities, improvements are needed in the quality of the MWIR-absorbing QD materials, ligands dictating inter-QD charge transport, ordering of QD films, device architectures, and scalability of these methods to larger areas. With three families of QDs being researched – lead-, silver-, and mercury-based chalcogenides – as well as a multitude of possible capping ligands and film deposition techniques, the experimental phase space for MWIR QDs is vast. Here, we provide a roadmap, considering the pros and cons of various film deposition and ligand exchange techniques, as well as reintroduce lessons learned over years of research on QD film formation. We also extracted and created a database of reported quantum dot photodetectors’ performance and fabrication methods and have developed an interactive data visualization dashboard for this database, which provides researchers in the field a quick snapshot of the existing state of the art. Finally, we outline figures of merit and information that should be presented in papers moving forward which would help to clarify the reported results, as well as offer ideas for future steps. We provide a database visualization of the QD photodetector literature at https://public.tableau.com/app/profile/tom.nakotte/viz/IRQDphotodetectors_16384709473480/Dashboard1#1.

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

Rapid scalable fabrication of roll-to-roll slot-die coated flexible perovskite solar cells using intense pulse light annealing

Perovskite solar cells have achieved high efficiencies at the lab scale using batch processes that often may not be scalable. Processes that can be integrated into a continuous manufacturing platform, such as roll-to-roll, where several processes can be carried out sequentially are necessary to develop a new cost-competitive solar technology. In this work, intense pulsed light thermal post-processing is coupled with scalable slot-die coating and air knife evaporation in a high-speed roll-to-roll process of up to 2 m min –1 . The optimization of IPL energy parameters, web speeds, solution flow rates, and concentration of precursor inks yielded a champion power conversion efficiency of 11.24% for a 1 cm 2 roll-to-roll coated device. The overall processing time for a single layer in this process is reduced from minutes to seconds and the overall footprint of the manufacturing process is decreased. Furthermore, this demonstrates the promising capabilities for intensed pulsed light to reduce manufacturing costs and paves a roadmap for scalable and efficient PSCs on a roll-to-roll platform.

14 SOLAR ENERGY↗
DOE OSTI2025

Geologic hydrogen: a review of resource potential, subsurface dynamics, exploration, production, transportation, and research opportunities

Hydrogen is a versatile resource with critical roles in decarbonization, industrial manufacturing, and energy integration. However, most hydrogen today is produced from fossil fuels, resulting in high emissions and energy consumption. Although low-carbon hydrogen production methods, such as steam methane reforming with carbon capture and renewable-powered electrolysis, are advancing, their high costs hinder large-scale deployment. Identifying alternative pathways for producing low-cost, low-emission hydrogen is therefore essential. Geologic hydrogen, referring to natural and stimulated hydrogen generated in the Earth's subsurface, has attracted growing attention as a potential source of sustainable, economically viable, and environmentally favorable hydrogen. This paper provides a comprehensive review of geologic hydrogen, covering its resource potential, origins, migration and trapping mechanisms, exploration techniques, production strategies, and pipeline transportation. It also identifies key knowledge gaps and proposes a roadmap for future research. The review indicates that geologic hydrogen has vast resource potential and can leverage existing subsurface technologies and geophysical exploration methods. However, major challenges persist, including uncertain hydrogen generation rates, limited understanding and control of serpentinization processes, costly transportation infrastructure, the lack of validated techno-economic analysis, and potential social and environmental issues. As the field is still in its early stages, progress will require interdisciplinary collaboration spanning geoscience, engineering, economics, environmental science, and policy and regulation.

42 ENGINEERING↗
DOE OSTI2026

Navigating the Path to Autonomy: Real-World Lessons from an Air-Free Self-Driving Laboratory

While autonomous experimentation has promise to accelerate discovery in physcial sciences, the real-world integration of predictive models and experimentation is non-trivial. Here we describe the genesis of a self-driving laboratory (SDL) for air-sensitive chemistry at Argonne National Laboratory and demonstrate the experimental design considerations needed for high-throughput experiments before predictive models can lead to scientific discovery. Our SDL was designed to explore battery electrolyte stability. Our final SDL utilized plate readers in a glovebox with a nitrogen atmosphere to perform kinetic assays and screen hundreds of battery-relevant solvents. However, the roadmap to autonomy and airfree-friendly experimentation required the complex evaluation of several spectroscopic and chromatographic methods. The greatest experimental challenges were (a) developing long-term sampling methods that remained air-free; (b) accelerating kinetics to advance reactivity projections; and (c) ensuring labware compatibility with nonaqueous solvents used in battery chemistry. Our experiences highlight the practical gap between closed-loop aspirations and the realities of chemical discovery, offering lessons on the challenges of transferring every day laboratory workflows to autonomy. These results suggest a more realistic blueprint for autonomy in chemistry—one that balances thoughtful and realistic experimental formulation.

Robertson, Lily A.↗
DOE OSTI2024

KiDS-1000 cosmology: Combined second- and third-order shear statistics

Aims.In this work, we perform the first cosmological parameter analysis of the fourth release of Kilo Degree Survey (KiDS-1000) data with second- and third-order shear statistics. This paper builds on a series of studies aimed at describing the roadmap to third-order shear statistics. Methods.We derived and tested a combined model of the second-order shear statistic, namely, the COSEBIs and the third-order aperture mass statistics 〈ℳ ap 3 〉 in a tomographic set-up. We validated our pipeline withN-body mock simulations of the KiDS-1000 data release. To model the second- and third-order statistics, we used the latest version of HMCODE2020 for the power spectrum and BIHALOFITfor the bispectrum. Furthermore, we used an analytic description to model intrinsic alignments and hydro-dynamical simulations to model the effect of baryonic feedback processes. Lastly, we decreased the dimension of the data vector significantly by considering only equal smoothing radii for the 〈ℳ ap 3 〉 part of the data vector. This makes it possible to carry out a data analysis of the KiDS-1000 data release using a combined analysis of COSEBIs and third-order shear statistics. Results.We first validated the accuracy of our modelling by analysing a noise-free mock data vector, assuming the KiDS-1000 error budget, finding a shift in the maximum of the posterior distribution of the matter density parameter, ΔΩ m < 0.02 σ Ω m , and of the structure growth parameter, ΔS 8 < 0.05 σ S 8 . Lastly, we performed the first KiDS-1000 cosmological analysis using a combined analysis of second- and third-order shear statistics, where we constrained Ω m = 0.248 −0.055 +0.062 andS 8 = σ 8 √(Ω m /0.3 )= 0.772 ± 0.022. The geometric average on the errors of Ω m andS 8 of the combined statistics decreases, compared to the second-order statistic, by a factor of 2.2.

Astronomy & Astrophysics↗