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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

CryoET reveals organelle phenotypes in huntington disease patient iPSC-derived and mouse primary neurons

Huntington’s disease (HD) is caused by an expanded CAG repeat in the huntingtin gene, yielding a Huntingtin protein with an expanded polyglutamine tract. While experiments with patient-derived induced pluripotent stem cells (iPSCs) can help understand disease, defining pathological biomarkers remains challenging. Here, we used cryogenic electron tomography to visualize neurites in HD patient iPSC-derived neurons with varying CAG repeats, and primary cortical neurons from BACHD, deltaN17-BACHD, and wild-type mice. In HD models, we discovered sheet aggregates in double membrane-bound organelles, and mitochondria with distorted cristae and enlarged granules, likely mitochondrial RNA granules. We used artificial intelligence to quantify mitochondrial granules, and proteomics experiments reveal differential protein content in isolated HD mitochondria. Knockdown of Protein Inhibitor of Activated STAT1 ameliorated aberrant phenotypes in iPSC- and BACHD neurons. We show that integrated ultrastructural and proteomic approaches may uncover early HD phenotypes to accelerate diagnostics and the development of targeted therapeutics for HD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Remediation and upcycling of microplastics by algae with wastewater nutrient removal and bioproduction potential

Microplastics have emerged as major environmental hazards that require efficient, cost-effective, and sustainable remediation technologies. This study introduces an integrative platform for the remediation and upcycling of microplastics by algae, while synergizing with plastic upcycling, wastewater treatment, and algal production. The strategy employs a mechanism that enhances hydrophobic interactions between the cell surface and microplastics, enabling rapid aggregation and removal. The platform achieves a superior microplastic removal efficiency of 91.4% within 1 hour, with a capacity of 0.1-gram microplastic per gram of biomass. Furthermore, the study demonstrates an upcycling strategy that converts microplastics-enriched cyanobacteria into plastic composites with unique performance. This work also integrates microplastic removal with cyanobacterial bioproduction and wastewater treatment, offering an approach that synergizes remediation with these value-added processes. Ultimately, this platform provides a viable and sustainable pathway to address microplastic pollution by creating value through plastic upcycling, wastewater nutrient removal, and CO 2 -based bioproduction.

09 BIOMASS FUELS↗
DOE OSTI2022

Customized metallodielectric colloids and their behavior in dielectrophoretic fields

A synthetic strategy for fabricating colloidal particles with spatially segregated amine-functionalized lobes enables regioselective coating with gold to afford metallodielectric particles with a variety of shapes and lobe sizes. This approach can produce either dissymmetric dumbbell-shaped two-lobed Au-TPM particles (Au-T) or dissymmetric or symmetric three-lobed particles with gold coating on one (Au-T-T and T-Au-T) or two lobes (Au-T-Au). Dielectrophoretic (DEP) forces exerted by an AC field confined between two opposing electrodes generate aggregates ranging from 1D chains to 2D close-packed lattices, depending on the particle shape and lobe arrangement. Furthermore, the aggregate structures reflect the lowest energy configurations resulting from the induced dipole moments created in particle lobes within the confined electric field.

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

Institutional Framework of Variable Renewable Energy Forecasting in India

The share of variable renewable energy (VRE) in India is growing rapidly, with a national goal of reaching 50% capacity from non-fossil fuel generation by 2030. One implication of this growth is the need for improved VRE forecasting methods. For this reason, the Ministry of New and Renewable Energy (MNRE) in India commissioned this study with support from the United States Agency for International Development (USAID), the National Renewable Energy Laboratory (NREL) in the United States, and the National Institute of Wind Energy (NIWE) in India. The objective of this study was to review the existing institutional framework and suggest changes needed to support the plans for large-scale VRE integration in the country. To achieve that objective, the authors consulted local stakeholders about the status of VRE forecasting in India, reviewed existing studies, and examined VRE forecasting methods around the world to identify best practices. Based on those best practices, the study presents six potential approaches to improve the VRE forecasting framework in India. Approaches include incentivizing VRE forecast improvement and use of the most accurate VRE forecast, creating an institution that will optimize VRE forecasts while maintaining and ensuring access to necessary data for forecasting, implementing a review and certification process for VRE forecast providers, forecasting closer to dispatch time and allowing for more frequent forecast revisions, increasing the frequency of weather forecasts, and aggregating VRE forecasts at the point of interconnection.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Project: Corbomite - Product: ConsoleWorks REACT

TDi Technologies presents ConsoleWorks REACT, an advanced platform designed to tackle the complexities of cyber and operational risk assessment. This comprehensive solution goes beyond asset-focused approaches by considering the impact of both assets and people have on the security and operation of critical infrastructure, specifically targeting preventing gird mis-operation by evaluating real-time human interaction or commands with critical assets. The hypothesis suggests that by integrating the assessment of user commands into the overall risk assessment process, organizations can make more informed decisions, prioritize resources effectively, and respond promptly to potential threats. This hypothesis forms the basis for the development of a proactive and holistic risk management approach that is more comprehensive and context aware than traditional models which only look at assets, patch levels, configuration and threat intel by collecting that information off the network vs directly from the asset, human, and human interaction all in real-time. ConsoleWorks' unique man-in-the-middle architecture is a key feature that sets it apart in the cybersecurity landscape. This architecture enables the real-time observation, enforcement, and commands or interaction risk transparency of user interaction with critical infrastructure to be risk mitigated and audited as they are aggregated with device and human risk factors for a more comprehensive risk threat score across a device or group of devices. This architecture allows ConsoleWorks to act as a secure intermediary between users and critical assets, monitoring all interactions and ensuring that only authorized commands are sent to the asset to be executed. This not only enhances security but also provides a comprehensive audit trail of all user activities, contributing to compliance efforts and facilitating incident investigation and risk management. By integrating this unique architecture with our comprehensive risk assessment methodology, ConsoleWorks REACT provides a powerful solution for managing cyber and operational risks, enabling organizations to maintain a robust security posture and effectively mitigate potential threats. To that end, the primary objective of this project was to research and develop a robust solution that enables the energy industry to mitigate the risks associated with human actions that can compromise the security or operations of assets critical to energy delivery, generation, transmission and operation. ConsoleWorks REACT plays a pivotal role in achieving this goal by leveraging its unique capabilities to monitor and track all user activity. Through its Zero Trust approach, which emphasizes continuous verification, the platform ensures secure access to assets and serves as the centralized human response and notification platform for addressing cyber and operational issues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Aggregation Methods for Quantifying PTM and Structural Changes in Bottom-Up Proteomics

Bottom-up proteomic workflows rely on sequential preprocessing steps, commonly including peptide-to-protein aggregation (“roll-up”), to enhance data reliability and interpretability. While roll-up is effective for protein-centered analyses, it may be suboptimal for applications focused on post-translational modifications (PTMs) or protein structural changes, such as limited proteolysis–mass spectrometry (LiP-MS). Here, we investigate how different roll-up strategies influence site-level quantification in PTM differential analysis. Moreover, we introduce a novel site-centric roll-up approach tailored for LiP-MS, which quantifies proteolytic fragments rather than solely tryptic peptides. We benchmark these methods through simulation studies, comparing their sensitivity and specificity in detecting structural and PTM-driven changes. We found that the median and mean roll-up methods outperform the sum method in both PTM and LiP proteomics, and site-level quantification in LiP outperforms peptide-level quantification. Our findings offer the first systematic, data-driven guidance for selecting roll-up techniques in site-level proteomic analyses, with implications for both PTM-focused and structural proteomics studies.

aggregation↗
DOE OSTI2023

The Current State of Light-Duty Electric Vehicle Supply Equipment Costs: An Assessment of Contemporary Understanding

This study uses a hybrid meta-analysis and literature-review approach to understand the current state of knowledge regarding the costs of electric vehicle supply equipment (EVSE). We present a novel way to consider, categorize, and label measures of cost and show cost measure estimates from a sample of 13 recent studies. We find that in general, there is too much variation and too few commonly represented EVSE cost measures to reasonably provide aggregate figures for these measures. We propose a convention for presenting EVSE cost measures that includes the application (commercial or residential), the power level (Level 1, Level 2, DCFC [with further distinction based on rated power capacity]), and the type of cost measure (hardware, installation, operation, and total cost). We contend that providing researchers with standard cost measures will help to advance our knowledge of EVSE costs by ensuring that future work will use common metrics. Establishing common metrics will enable conventional meta-analyses that will make assessments of EVSE costs even more accessible. Additionally, common metrics will make tracking costs more reliable as the technology continues to evolve and become more ubiquitous.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Using Radiative Convective Equilibrium to Explore Clouds and Climate in the Community Atmosphere Model

Abstract Characteristics of, and fundamental differences between, the radiative‐convective equilibrium (RCE) climate states following the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) protocols in the Community Atmosphere Model version 5 (CAM5) and version 6 (CAM6) are presented. This paper explores the characteristics of clouds, moisture, precipitation and circulation in the RCE state, as well as the tropical response to surface warming, in CAM5 and CAM6 with different parameterizations. Overall, CAM5 simulates higher precipitation rates that result in larger global average precipitation, despite lower outgoing longwave radiation compared to CAM6. Differences in the structure of clouds, particularly the amount and vertical location of cloud liquid, exist between the CAM versions and can, in part, be related to distinct representations of shallow convection and boundary layer processes. Both CAM5 and CAM6 simulate similar peaks in cloud fraction, relative humidity, and cloud ice, linked to the usage of a similar deep convection parameterization. These anvil clouds rise and decrease in extent in response to surface warming. More generally, extreme precipitation, aggregation of convection, and climate sensitivity increase with warming in both CAM5 and CAM6. This analysis provides a benchmark for future studies that explore clouds, convection, and climate in CAM with the RCEMIP protocols now available in the Community Earth System Model. These results are discussed within the context of realistic climate simulations using CAM5 and CAM6, highlighting the usefulness of a hierarchical modeling approach to understanding model and parameterization sensitivities to inform model development efforts.

Reed, Kevin A.↗
DOE OSTI2020

Fusing subnational with national climate action is central to decarbonization: the case of the United States

Approaches that root national climate strategies in local actions will be essential for all countries as they develop new nationally determined contributions under the Paris Agreement. The potential impact of climate action from non-national actors in delivering higher global ambition is significant. Sub-national action in the United States provides a test for how such actions can accelerate emissions reductions. We aggregated U.S. state, city, and business commitments within an integrated assessment model to assess how a national climate strategy can be built upon non-state actions. We find that existing commitments alone could reduce emissions 25% below 2005 levels by 2030, and that enhancing actions by these actors could reduce emissions up to 37%. We show how these actions can provide a stepped-up basis for additional federal action to reduce emissions by 49%—consistent with 1.5 °C. Our analysis demonstrates sub-national actions can lead to substantial reductions and support increased national action.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Non-Intrusive Appliance Identification with Appliance-Specific Networks

The problem of noninstrusive load monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and do not generalize very well. In this article, we propose a novel NILM method that leverages advances in statistical learning that have not been properly applied in this domain before. The proposed method consists of three stages: first, a Bayesian nonparametric learning-based approach for appliance state extraction; second, synthetic minority oversampling technique for data augmentation and mitigating the heavy imbalance in switching events; and third, appliance-specific lightweight long short-term memory networks for status classification for each appliance. Here, we adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multiappliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples.

42 ENGINEERING↗
DOE OSTI2023

Three-dimensional nanoscale reduced-angle ptycho-tomographic imaging with deep learning (RAPID)

X-ray ptychographic tomography is a nondestructive method for three dimensional (3D) imaging with nanometer-sized resolvable features. The size of the volume that can be imaged is almost arbitrary, limited only by the penetration depth and the available scanning time. Here we present a method that rapidly accelerates the imaging operation over a given volume through acquiring a limited set of data via large angular reduction and compensating for the resulting ill-posedness through deeply learned priors. The proposed 3D reconstruction method “RAPID” relies initially on a subset of the object measured with the nominal number of required illumination angles and treats the reconstructions from the conventional two-step approach as ground truth. It is then trained to reproduce equal fidelity from much fewer angles. After training, it performs with similar fidelity on the hitherto unexamined portions of the object, previously not shown during training, with a limited set of acquisitions. In our experimental demonstration, the nominal number of angles was 349 and the reduced number of angles was 21, resulting in a x140 aggregate speedup over a volume of 4.48 x 93.18 x 3.92 μm 3 and with (14nm) 3 feature size, i.e. ~ 10 8 voxels. RAPID’s key distinguishing feature over earlier attempts is the incorporation of atrous spatial pyramid pooling modules into the deep neural network framework in an anisotropic way. We found that adjusting the atrous rate improves reconstruction fidelity because it expands the convolutional kernels’ range to match the physics of multi-slice ptychography without significantly increasing the number of parameters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

MEP pathway products allosterically promote monomerization of deoxy-D-xylulose-5-phosphate synthase to feedback-regulate their supply

Isoprenoids are a very large and diverse family of metabolites required by all living organisms. All isoprenoids derive from the double-bond isomers isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), which are produced by the methylerythritol 4-phosphate (MEP) pathway in bacteria and plant plastids. It has been reported that IPP and DMAPP feedback-regulate the activity of deoxyxylulose 5-phosphate synthase (DXS), a dimeric enzyme that catalyzes the main flux-controlling step of the MEP pathway. Here we provide experimental insights into the underlying mechanism. Isothermal titration calorimetry and dynamic light scattering approaches showed that IPP and DMAPP can allosterically bind to DXS in vitro, causing a size shift. In silico ligand binding site analysis and docking calculations identified a potential allosteric site in the contact region between the two monomers of the active DXS dimer. Modulation of IPP and DMAPP contents in vivo followed by immunoblot analyses confirmed that high IPP/DMAPP levels resulted in monomerization and eventual aggregation of the enzyme in bacterial and plant cells. Loss of the enzymatically active dimeric conformation allows a fast and reversible reduction of DXS activity in response to a sudden increase or decrease in IPP/DMAPP supply, whereas aggregation and subsequent removal of monomers that would otherwise be available for dimerization appears to be a more drastic response in the case of persistent IPP/DMAPP overabundance (e.g., by a blockage in their conversion to downstream isoprenoids). Our results represent an important step toward understanding the regulation of the MEP pathway and rational design of biotechnological endeavors aimed at increasing isoprenoid contents in microbial and plant systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Alignment frustration in block copolymer films with block copolymer grafted TiO 2 nanoparticles under soft-shear cold zone annealing

Block copolymers (BCPs) have received significant attention as promising candidates for sequestering nanoparticles and fabrication of aligned nanostructures with optimal optical or electrical properties. We investigate the influence of static and dynamic thermal field on the alignment of polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) BCP morphology with the loading of novel poly(methyl methacrylate-block-Polystyrene) (PMMA-b-PS)-grafted-TiO 2 nanoparticles (BCP-g-TiO 2 ). Observation of characteristics IR peaks for PMMA and PS in BCP-g-TiO 2 nanoparticles and Transmission Electron Microscopy (TEM) results of the outer coating of core nanoparticle, validate the grafting to approach in synthesizing BCP-g-TiO 2 . In this work, we report that under the sharp dynamic thermal field, at low loading of BCP-g-TiO 2 , there is good dispersion of nanoparticles in unidirectionally aligned BCP matrix in film interior probed by GISAXS, while, at high nanoparticle loading (~10 wt%), there is local frustration in the unidirectional alignment of the BCP matrix due to aggregation of BCP-g-TiO 2 nanoparticles. However, Grazing incidence small angle X-ray scattering (GISAXS) shows clearly that the BCP films remain largely locally ordered at the domain scale, despite these large perturbations to long-range ordering even at high loading level, while bringing in new TiO 2 functionality to the BCP films, such as UVO absorptivity or biofouling prevention, important to potential new applications of such membranes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Enhancing the Solubility of Semiconducting Polymers in Eco-Friendly Solvents with Carbohydrate-Containing Side Chains

Semiconducting polymers are at the forefront of next-generation organic electronics due to their robust mechanical and optoelectronic properties. However, their extended π-conjugation often leads to materials with low solubilities in common organic solvents, thus requiring processing in high-boiling-point and toxic halogenated solvents to generate thin-film devices. To address this environmental concern, a natural product-inspired side-chain engineering approach was used to incorporate galactose-containing moieties into semiconducting polymers toward improved processability in greener solvents. Novel isoindigo-based polymers with different ratios of galactose-containing side chains were synthesized to improve the solubilities of the organic semiconductors in alcohol-based solvents. The addition of carbohydrate-containing side chains to π-conjugated polymers was found to considerably impact the intermolecular aggregation of the materials and their microstructures in the solid state as confirmed by atomic force microscopy and grazing-incidence wide-angle X-ray scattering. The charge transport characteristics of the new semiconductors were evaluated by the fabrication of organic field-effect transistors prepared from both toxic halogenated and greener alcohol-based solvents. Importantly, the incorporation of carbohydrate-containing side chains was shown to have very little detrimental impact on the electronic properties of the polymer when processed from green solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A Multi-criteria CCUS Screening Evaluation of the Gulf of Mexico, USA - Supplementary Data

The Wendt et al. study aims to incorporate multiple and disparate carbon capture, utilization, and storage (CCUS) decision-making criteria into a systematic, quantitative analytical approach to help identify areas with potentially high suitability to serve as offshore CO2 storage or EOR regions. Spatially-distributed data from publicly-available sources within the Gulf of Mexico (GOM) study area (limited to federal waters; state waters were not evaluated) was compiled using the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) Cumulative Spatial Impact Layers™ (CSIL) tool to easily aggregate data based on evenly-distributed grids across the study region set at a resolution of approximately 25 square miles (65 square kilometers). The data included in this Microsoft Excel™ workbook provide the aggregated scores for each grid point across the study domain as well the weighting for each criterion under the four scenarios evaluated.

CCUS↗
DOE OSTI2024

A Generalized approach to the operationalization of Software Quality Models

Comprehensive measures of quality are a research imperative, yet the development of software quality models is a wicked problem. Definitive solutions do not exist and quality is subjective at its most abstract. Definitional measures of quality are contingent on a domain, and even within a domain, the choice of representative characteristics to decompose quality is subjective. Thus, the operationalization of quality models brings even more challenges. A promising approach to quality modeling is the use of hierarchies to represent characteristics, where lower levels of the hierarchy represent concepts closer to real-world observations. Building upon prior hierarchical modeling approaches, we developed the Platform for Investigative software Quality Understanding and Evaluation (PIQUE). PIQUE surmounts several quality modeling challenges because it allows modelers to instantiate abstract hierarchical models in any domain by leveraging organizational tools tailored to their specific contexts. Here, we introduce PIQUE; exemplify its utility with two practical use cases; address challenges associated with parameterizing a PIQUE model; and describe algorithmic techniques that tackle normalization, aggregation, and interpolation of measurements.

Data aggregation↗
DOE OSTI2020

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗