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At least 883 records · Page 49

Report of the Basic Energy Sciences Roundtable on Chemical Upcycling of Polymers

Plastics are ubiquitous in modern life. They are primarily made from synthetic carbon-based polymers— organic macromolecules made up of many repeating subunits called monomers—and are designed to be durable and resistant to degradation. Global plastics production has reached a rate of more than 400 million metric tons (MTs) per year, with more than 8 billion MTs produced in the past 50 years. Average production has increased by 36% in the past decade and is projected to grow to 700 million MTs in 2030—about 80 kg of plastics produced for every human on earth. Globally, 20% of discarded plastics are recycled (<10% in the United States), primarily using mechanical processes, and about 25% are incinerated for energy recovery. More than half are deposited into landfills or released into the environment. Thus, discarded plastics pose a long-term environmental challenge: For example, at the current rate of plastics production and disposal, the mass of plastics in the ocean is predicted to exceed the mass of fish by 2050. Current approaches to reducing unwanted plastics are insufficient to address the growing accumulation of discarded plastics. Although incineration eliminates unwanted plastics and recovers some of the energy used to make them, it uses up the potential resource and creates unwanted byproducts. Mechanical recycling—which involves shredding, heating, and remolding of the plastics—is more efficient than making them from petroleum products, using less than half as much energy to generate new plastics. However, mechanical recycling generally degrades, or downcycles, the polymers. Chemical recycling involves deconstruction of polymers by chemical processes to monomers for conversion back into polymers or to molecular intermediates that can be used as fuel or feedstock by the chemical industry. However, present methods (e.g., pyrolysis—high-temperature decomposition in the absence of oxygen), are energy-intensive and require further processing to make products. Chemical upcycling of polymers—the process of selectively converting discarded plastics into chemicals, fuels, or materials with higher value—holds the promise of changing the paradigm for discarded plastic from waste to valued resource. A significant opportunity exists for fundamental research to provide the foundational knowledge required to move toward a circular lifecycle for plastics, in which the chemical constituents of plastics are reformed into polymers or repurposed to give them another life. To identify the fundamental challenges and research opportunities that could accelerate the transformation of discarded plastics to higher-value fuels, chemicals, and materials, the US Department of Energy, Office of Science, Office of Basic Energy Sciences sponsored a Roundtable on Chemical Upcycling of Polymers, which was held near Washington, DC, on April 30–May 1, 2019. This roundtable identified four Priority Research Opportunities to address the complex chemical transformations and physical processes underlying the upcycling of discarded plastics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Scaling open-weight large language models for hydropower regulatory information extraction: A systematic analysis

Information extraction from regulatory and technical documents using large language models (LLMs) involves practical trade-offs between extraction quality and computational cost. We evaluate eight open-weight LLMs spanning 0.6B–70B parameters on hydropower licensing documents and report deployment-oriented evidence under a unified extraction schema and evaluation protocol. Across the model set, we observe clear scale-dependent trends in both baseline extraction quality and the effectiveness of reflective reasoning (self-checking) under our fixed-prompt, no-augmentation setting. Mid-scale models often provide a favorable balance of accuracy and efficiency, whereas the smallest models show limited or inconsistent gains from the reasoning variants tested. Larger models achieve the highest overall F1 scores but incur substantially greater compute and infrastructure requirements. We further find that reliability failure modes can distort conventional metrics in this domain: in particular, high recall can coincide with systematic extraction errors when models fabricate values for fields that are absent from the source text, underscoring the importance of conservative null handling and evidence-grounded evaluation. Overall, our study provides a reproducible resource–performance comparison for open-weight LLM-based extraction in hydropower regulatory documentation and offers practical guidance for model selection under different deployment constraints.

Evaluation protocol↗

One-step catalytic growth of carbon nanofiber arrays vertically aligned on carbon substrate

Highlights: ► Acetylene as carbon resource and copper foil as catalyst. ► Three carbon nanostructures are synthesized by modulating feeding gas compositions. ► NH{sub 3} is a key factor in the growth of VA-CNF arrays. -- Abstract: Vertically aligned carbon nanofiber (VA-CNF) arrays on carbon substrate have been synthesized via one-step chemical vapor deposition process on copper foil, by using acetylene as carbon resource. Three types of carbon nanostructures, viz. bare carbon films, CNFs and VA-CNFs grown on carbon substrate, could be selectively synthesized by only modulating the concentration of C{sub 2}H{sub 2} and NH{sub 3} in the feeding gases. It was found that NH{sub 3} was a key factor in the growth of VA-CNF arrays, which could increase the diffusion capability of copper atoms in carbon materials, therefore promote forming larger spherical Cu NPs catalysts for the growth of VA-CNFs. Furthermore, a growth mechanism in different feeding gas compositions was proposed.

36 MATERIALS SCIENCE↗

Uncovering novel liquid organic hydrogen carriers: a systematic exploration of chemical compound space using cheminformatics and quantum chemical methods

We present a comprehensive, in silico-based discovery approach to identifying novel liquid organic hydrogen carrier (LOHC) candidates using cheminformatics methods and quantum chemical calculations. We screened over 160 billion molecules from ZINC15 and GDB-17 chemical databases for structural similarity to known LOHCs and employed a data-driven selection criterion connecting molecular features with dehydrogenation enthalpy. This scoring criterion effectively predicts dehydrogenation enthalpies from SMILES strings, streamlining the LOHC screening process. After rigorous screening and down-selection, we compiled a database of 3000 dehydrogenation reactions for the most promising LOHC candidates, setting the stage for future selection based on kinetics and catalysis. This work demonstrates the significant impact of integrating quantum chemistry and cheminformatics in materials discovery, accelerating the selection process while reducing experimental efforts and time. By proposing new molecules as prospective LOHC candidates, our study provides a valuable resource for researchers and engineers in the development of advanced LOHC systems and showcases a successful approach for high-throughput discovery, contributing to more efficient and sustainable energy storage solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Life-Cycle Evaluation of Concrete Building Construction as a Strategy for Sustainable Cities

Structural materials in commercial buildings in the United States account for a significant fraction of national energy use, resource consumption, and greenhouse gas (GHG) emissions. Robust decisions for balancing and minimizing these various environmental effects require that structural materials selections follow a life-cycle, systems modeling approach. This report provides a concise overview of the development and use of a new life-cycle assessment (LCA) model for structural materials in U.S. commercial buildings-the Berkeley Lab Building Materials Pathways (B-PATH) model. B-PATH aims to enhance environmental decision-making in the commercial building LCA, design, and planning communities through the following key features: (1) Modeling of discrete technology options in the production, transportation, construction, and end of life processes associated U.S. structural building materials; (2) Modeling of energy supply options for electricity provision and directly combusted fuels across the building life cycle; (3) Comprehensiveness of relevant building mass and energy flows and environmental indicators; (4) Ability to estimate modeling uncertainties through easy creation of different life-cycle technology and energy supply pathways for structural materials; and (5) Encapsulation of the above features in a transparent public use model. The report summarizes literature review findings, methods development, model use, and recommendations for future work in the area of LCA for commercial buildings.

25 ENERGY STORAGE↗

Advanced Grid-Friendly Controls Demonstration Project for Utility-Scale PV Power Plants

A typical photovoltaic (PV) power plant consists of multiple power electronic inverters and can contribute to grid stability and reliability through sophisticated 'grid-friendly' controls. The availability and dissemination of actual test data showing the viability of advanced utility-scale PV controls among all industry stakeholders can leverage PV's value from being simply an energy resource to providing additional ancillary services that range from variability smoothing and frequency regulation to power quality. Strategically partnering with a selected utility and/or PV power plant operator is a key condition for a successful demonstration project. The U.S. Department of Energy's (DOE's) Solar Energy Technologies Office selected the National Renewable Energy Laboratory (NREL) to be a principal investigator in a two-year project with goals to (1) identify a potential partner(s), (2) develop a detailed scope of work and test plan for a field project to demonstrate the gird-friendly capabilities of utility-scale PV power plants, (3) facilitate conducting actual demonstration tests, and (4) disseminate test results among industry stakeholders via a joint NREL/DOE publication and participation in relevant technical conferences. The project implementation took place in FY 2014 and FY 2015. In FY14, NREL established collaborations with AES and First Solar Electric, LLC, to conduct demonstration testing on their utility-scale PV power plants in Puerto Rico and Texas, respectively, and developed test plans for each partner. Both Puerto Rico Electric Power Authority and the Electric Reliability Council of Texas expressed interest in this project because of the importance of such advanced controls for the reliable operation of their power systems under high penetration levels of variable renewable generation. During FY15, testing was completed on both plants, and a large amount of test data was produced and analyzed that demonstrates the ability of PV power plants to provide various types of new grid-friendly controls.

14 SOLAR ENERGY↗

Evaluation of Machine Learning Approaches to Estimate Aerosol Mixing State Metrics in Atmospheric Models

Aerosol mixing state describes how aerosol compositions are distributed among atmospheric aerosol particles in a population. Oversimplified assumptions of aerosol mixing state in atmospheric modeling can introduce errors in estimations of weather and climate-relevant aerosol microphysical properties. A more comprehensive representation of the aerosol mixing state can be achieved in principle with a Particle-resolved Monte Carlo (PartMC) model but at added computational cost that may be prohibitive for direct invocation in operational numerical weather prediction or multi-year climate simulations. The aim of our research is to explore the machine learning (ML) methodologies for estimating aerosol mixing state metrics, which we define here in three different ways: with respect to (a) hygroscopicity; (b) optical properties; and (c) chemical species abundance. We adopted a data-driven approach, leveraging deep learning and statistical learning techniques, to take advantage of massive PartMC model simulations. First, we performed particle-resolved simulations by PartMC to create a series of scenarios considering a range of global environmental conditions. Each scenario consists of aerosol populations and corresponding mixing state metrics. The gas concentration, aerosol mass concentration, environmental variables and mixing state metrics of each population constitute the datasets for machine learning implementations. We have adopted and evaluated various configurations of machine learning methodologies in this investigation, embracing deep learning, Extreme Gradient Boosting (XGBoost) algorithm, and the ensemble approaches. After a rigorous model selection process, we identified an appropriate model to derive estimates of aerosol mixing state metrics. We used the computational and data resources of the Oak Ridge Leadership Computing Facility (OLCF) and the ORNL Compute and Data Environment for Science (CADES). The NVIDIA DGX-1 hardware was used for the prototyping of the ML models. Our approach has allowed us to gain a new understanding of how machine learning methodologies can be applied to improve the representation of aerosol mixing state in atmospheric models and benefit the atmospheric research community. Next, we plan to extend our research and methodology to quantify some of the aerosol-related uncertainties in the E3SM Atmospheric Model (EAM) Version 1.

54 ENVIRONMENTAL SCIENCES↗

Guideline for Characterizing and Evaluating a Candidate Project Site for Solar Thermal Applications

This document presents a structured procedure for characterizing and evaluating candidate project sites for concentrating solar power (CSP) and solar heat for industrial processes (SHIP) applications. The objective is to provide project developers, researchers, and other stakeholders with a consistent, technology-agnostic framework for early-stage site assessment, enabling informed decision-making prior to significant investment in project development. Site selection is a critical factor in project success or failure for both CSP and SHIP projects. Key factors such as solar resource availability, land characteristics, environmental and regulatory constraints, infrastructure availability, and community context are determined by the choice of project site and can materially impact project performance, cost, schedule, and overall viability. This procedure is designed to systematically evaluate these factors, identify potential fatal flaws, and prioritize the most favorable candidate sites for further development. The process begins with rapid screening-level evaluation, using publicly available data to assess solar resource, land availability and suitability, zoning and land-use compatibility, and exclusion zones such as protected lands or sensitive habitats. Sites that meet the minimum screening criteria advance to a more detailed characterization. Subsequent sections of this report provide guidance for a next-level assessment of the most important technical and environmental parameters, including: 1) Solar resource quality, variability, and uncertainty using multiyear datasets and, where appropriate, on-site measurement campaigns; 2) Meteorological conditions such as wind, temperature, extreme weather events, and soiling impacts; 3) Land characteristics including slope, shading, and geotechnical conditions; and 4) Environmental and regulatory considerations, including permitting processes, endangered species, cultural resources, and visual impacts. The procedure also addresses infrastructure and integration considerations, including: 1) Grid interconnection requirements for CSP power generation projects; 2) Electrical and operational integration for SHIP facilities; 3) Water availability, quality, and permitting constraints, which are particularly critical for CSP in arid regions; and 4) Site access, construction logistics, and availability of workforce and supporting services. Recognizing the importance of social and economic context, the procedure includes evaluation of community engagement factors, such as stakeholder sentiment, proximity to sensitive visual receptors, workforce development opportunities, and local economic incentives. The outputs of these assessments are synthesized in a cost and risk evaluation, translating site characteristics into expected impacts on capital cost, operating cost, schedule, and technical risk. This is complemented by screening-level performance modeling, including 8760 simulations and long-term projections, to quantify expected energy or thermal output, assess variability thereof, and support comparison between candidate sites. Finally, the procedure provides high-level guidance on a structured go/no-go decision framework, categorizing sites based on identified risks and constraints, and outlining a clear path forward to feasibility studies and front-end engineering design for viable projects. By standardizing the site characterization process across both CSP and SHIP applications, this guideline aims to: 1) Improve consistency and transparency in early-stage project evaluation; 2) Reduce development risk and avoid investment in nonviable project sites; 3) Support collaboration between developers, researchers, and public agencies; and 4) Accelerate successful deployment of concentrating solar technologies for both power generation and industrial process heat.

14 SOLAR ENERGY↗

Mapping Atlantic coastal marshlands, Maryland, Georgia, using ERTS-1 imagery

Eastern coastal marshes are the most extensive and productive in the United States. A relatively low cost, moderately accurate method is needed to map these areas for management and protection. Groundbased and low-altitude aircraft methods for mapping are time-consuming and quite expensive. The launch of NASA's Earth Resources Technology Satellite has provided an opportunity to test the feasibility of mapping wetlands using small scale imagery. The test sites selected were in Chesapeake Bay, Maryland, and Ossabaw Island, Georgia. Results of the investigation indicate that the following may be ascertained from ERTS imagery, enlarged to 1:250,000: (1) upper wetland boundary; (2) drainage pattern in the wetland; (3) plant communities; (4) ditching activities associated with agriculture; and (5) lagooning for water-side home development. Conclusions are that ERTS will be an excellent tool for many types of coastal wetland mapping.

Anderson, R. R.↗

Resource allocation using constraint propagation

The concept of constraint propagation was discussed. Performance increases are possible with careful application of these constraint mechanisms. The degree of performance increase is related to the interdependence of the different activities resource usage. Although this method of applying constraints to activities and resources is often beneficial, it is obvious that this is no panacea cure for the computational woes that are experienced by dynamic resource allocation and scheduling problems. A combined effort for execution optimization in all areas of the system during development and the selection of the appropriate development environment is still the best method of producing an efficient system.

Rogers, John S.↗

Minimizing distortion in truss structures - A Hopfield network solution

Distortions in truss structures can result from random errors in element lengths that are typical of a manufacturing process. These distortions may be minimized by an optimal selection of elements from those available for placement between the prescribed nodes - a combinatorial optimization problem requiring significant investment of computational resource for all but the smallest problems. The present paper describes a formulation in which near-optimal element assignments are obtained as minimum-energy stable states, of an analogous Hopfield neural network. This requires mapping of the optimization problem into an energy function of the appropriate Liapunov form. The computational architecture is ideally suited to a parallel processor implementation and offers significant savings in computational effort. A numerical implementation of the approach is discussed with reference to planar truss problems.

Fu, B.↗

Minimizing distortion in truss structures -- a Hopfield network solution

Distortions in truss structures can result from random errors in elemental lengths that are typical of a manufacturing process. These distortions may be minimized by an optimal selection of elements from those available for placement between the prescribed nodes -- a combinatorial optimization problem requiring significant investment of computational resource for all but the smallest problems. The present paper describes a formulation in which near-optimal element assignments are obtained as minimum energy, stable states, of an analogous Hopfield neural network. This requires mapping of the optimization problem into an energy function of the appropriate Lyapunov form. The computational architecture is ideally suited to a parallel processor implementation and offers significant savings in computational effort. A numerical implementation of the approach is discussed with reference to planar truss problems.

Fu, B.↗

Optimizing Input/Output Using Adaptive File System Policies

Parallel input/output characterization studies and experiments with flexible resource management algorithms indicate that adaptivity is crucial to file system performance. In this paper we propose an automatic technique for selecting and refining file system policies based on application access patterns and execution environment. An automatic classification framework allows the file system to select appropriate caching and pre-fetching policies, while performance sensors provide feedback used to tune policy parameters for specific system environments. To illustrate the potential performance improvements possible using adaptive file system policies, we present results from experiments involving classification-based and performance-based steering.

Madhyastha, Tara M.↗

Testing and Validation of the Dynamic Inertia Measurement Method

The Dynamic Inertia Measurement (DIM) method uses a ground vibration test setup to determine the mass properties of an object using information from frequency response functions. Most conventional mass properties testing involves using spin tables or pendulum-based swing tests, which for large aerospace vehicles becomes increasingly difficult and time-consuming, and therefore expensive, to perform. The DIM method has been validated on small test articles but has not been successfully proven on large aerospace vehicles. In response, the National Aeronautics and Space Administration Armstrong Flight Research Center (Edwards, California) conducted mass properties testing on an "iron bird" test article that is comparable in mass and scale to a fighter-type aircraft. The simple two-I-beam design of the "iron bird" was selected to ensure accurate analytical mass properties. Traditional swing testing was also performed to compare the level of effort, amount of resources, and quality of data with the DIM method. The DIM test showed favorable results for the center of gravity and moments of inertia; however, the products of inertia showed disagreement with analytical predictions.

moments of inertia↗

Guidance, Navigation, and Control for NASA Lunar Pallet Lander

The NASA Lander Technology project is leading the development and integration of the Lunar Pallet Lander (LPL) concept. The objective is to demonstrate precision landing by delivering a payload to the lunar surface within 100 meters of a landing target. Potential landing sites are selected near the lunar pole where water may be present in permanently shadowed regions that could enable future in-situ resource utilization. The LPL is part of a sequence of missions aimed at maturing the necessary technologies, such as lunar precision landing sensors, that will enable the next generation of multi-ton lunar payloads and human landers. This paper provides an overview of the Mission Design, Guidance Navigation and Control (GNC) algorithms, and sensor suite. The results show the LPL simulated trajectory and landing precision performance under nominal and dispersed conditions. The landing precision simulation confirms the need to rely on high-accuracy navigation techniques and sensors such as Terrain Relative Navigation (TRN) and the Navigation Doppler Lidar (NDL), currently being developed for space applications. The results also demonstrate the ability of the guidance and control system to perform a soft lunar touchdown by combining thrust vector control during the solid rocket motor deceleration phase, and pulse engine control, for the liquid powered descent phase.

Orphee, Juan↗

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

A system and method of integrating diverse sources of data and data streams is presented. The method can include selecting a scenario based on a topic, creating a multi-relational directed graph based on the scenario, identifying and converting resources in accordance with the scenario and updating the multi-directed graph based on the resources, identifying data feeds in accordance with the scenario and updating the multi-directed graph based on the data feeds, identifying analytical routines in accordance with the scenario and updating the multi-directed graph using the analytical routines and identifying data outputs in accordance with the scenario and defining queries to produce the data outputs from the multi-directed graph.

97 MATHEMATICS AND COMPUTING↗

Fact Sheet: Best Practices Handbook for the Collection and Use of Solar Energy Applications

This fact sheet presents highlights from the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, which provides detailed information about solar resource data and the resulting data products needed for planning each stage of large concentrating solar power systems, from initial site selection to system operations.

14 SOLAR ENERGY↗

Best Bang for the Buck: Part 1 – The Size of Experiments Relative to Design Performance

There are many choices to make, when designing an experiment for a study, such as: what design factors to consider, which levels of the factors to use and which model to focus on. One aspect of design, however, is often left unquestioned: the size of the experiment. When learning about design of experiments, problems are often posed as "select a design for a particular objective with N runs." It’s tempting to consider the design size as a given constraint in the design-selection process. If you think of learning through designed experiments as a sequential process, however, strategically planning for the use of resources at different stages of data collection can be beneficial: Saving experimental runs for later is advantageous if you can efficiently learn with less in the early stages. Alternatively, if you’re too frugal in the early stages, you might not learn enough to proceed confidently with the next stages. Therefore, choosing the right-sized experiment is important—not too large or too small, but with a thoughtful balance to maximize the knowledge gained given the available resources. It can be a great advantage to think about the design size as flexible and include it as an aspect for comparisons. Sometimes you’re asked to provide a small design that is too ambitious for the goals of the study. Finally, if you can show quantitatively how the suggested design size might be inadequate or lead to problems during analysis—and also offer a formal comparison to some alternatives of different (likely larger) sizes—you may have a better chance to ask for additional resources to deliver statistically sound and satisfying results

97 MATHEMATICS AND COMPUTING↗