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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Nano-Engineering of Detector Surfaces to Offer Unprecedented Imager Sensitivity to Soft X-rays and Low Energy Electrons

The focus of this document is to record the learning and process development achieved by the completion of the Nano-Engineering of Detector Surfaces to Offer Unprecedented Imager Sensitivity to Soft X-rays and Low Energy Electrons LDRD. The goal of this effort was to study different silicon detector surface preparation methods such as ion implant parameters, and the addition of a quantum 2-layer superlattice. Enabling the preparation of the surface of silicon detectors (front side illuminated or bonded backside illuminated) increases the responsivity of the diode to shallowly absorbed photons. This increased sensitivity in turn allows for greater fidelity in imaging events that emit low soft X-rays or low energy electrons. Prior work has focused on passivating the surface of a silicon detectors with thin layers (tens of nm thick) of materials to reduce surface recombination sites. Measurements of visible light quantum efficiency, electron responsivity, and pulsed x-ray response indicate that detectors with a 2- layer superlattice enjoy a significant benefit over equivalent detectors using an ion implant at the illuminated surface.

42 ENGINEERING↗
DOE OSTI2016

Progress on Resuming Full Construction on the Waste Treatment and Immobilization Plant's High-Level Waste Facility - 16283

The U.S. Department of Energy (DOE) restricted certain engineering, procurement and construction work on the Waste Treatment and Immobilization Plant's (WTP) High-Level Waste (HLW) Facility in 2012 and early 2013 because of unresolved technical issues, and a need to align the nuclear safety basis with the design as the HLW Facility technical issues were resolved. The DOE Office of River Protection (ORP) needed a way to minimize the government's risk exposure, while continuing limited critical path construction work and resolving the technical and safety basis issues. ORP issued the Plan for Evaluation and Decision to Proceed for the High-Level Waste Facility in October 2013 to establish the process to evaluate the WTP contractor's progress on resolving the issues and to identify actions and deliverables needed to facilitate an ORP decision to resume production activities. This process provided for a phased resumption of HLW engineering, procurement and construction activities, with two decision points: Decision 1: Authorization to resume design/production engineering along with limited procurements and construction ramp-up. Decision 2: Full authorization to resume engineering, procurement and construction. In August 2014, ORP provided authorization for the WTP contractor to resume all engineering work necessary to finalize the design of the HLW Facility. The decision to proceed with production engineering in the HLW Facility was based on significant progress in resolving technical issues and improving work processes to align the HLW design and safety basis. ORP is working closely with the WTP contractor to facilitate decisions for resuming procurement of HLW Facility equipment with safety functions, and resuming full construction in the most complex process cells in the Facility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Engineered reduction of S-adenosylmethionine alters lignin in sorghum

Abstract Background Lignin is an aromatic polymer deposited in secondary cell walls of higher plants to provide strength, rigidity, and hydrophobicity to vascular tissues. Due to its interconnections with cell wall polysaccharides, lignin plays important roles during plant growth and defense, but also has a negative impact on industrial processes aimed at obtaining monosaccharides from plant biomass. Engineering lignin offers a solution to this issue. For example, previous work showed that heterologous expression of a coliphage S -adenosylmethionine hydrolase (AdoMetase) was an effective approach to reduce lignin in the model plant Arabidopsis. The efficacy of this engineering strategy remains to be evaluated in bioenergy crops. Results We studied the impact of expressing AdoMetase on lignin synthesis in sorghum ( Sorghum bicolor L. Moench). Lignin content, monomer composition, and size, as well as biomass saccharification efficiency were determined in transgenic sorghum lines. The transcriptome and metabolome were analyzed in stems at three developmental stages. Plant growth and biomass composition was further evaluated under field conditions. Results evidenced that lignin was reduced by 18% in the best transgenic line, presumably due to reduced activity of the S -adenosylmethionine-dependent O -methyltransferases involved in lignin synthesis. The modified sorghum features altered lignin monomer composition and increased lignin molecular weights. The degree of methylation of glucuronic acid on xylan was reduced. These changes enabled a ~20% increase in glucose yield after biomass pretreatment and saccharification compared to wild type. RNA-seq and untargeted metabolomic analyses evidenced some pleiotropic effects associated with AdoMetase expression. The transgenic sorghum showed developmental delay and reduced biomass yields at harvest, especially under field growing conditions. Conclusions The expression of AdoMetase represents an effective lignin engineering approach in sorghum. However, considering that this strategy potentially impacts multiple S -adenosylmethionine-dependent methyltransferases, adequate promoters for fine-tuning AdoMetase expression will be needed to mitigate yield penalty.

Tian, Yang↗
DOE OSTI2020

Genetic circuit design automation for yeast

Cells can be programmed to monitor and react to their environment using genetic circuits. Design automation software maps a desired circuit function to a DNA sequence, a process that requires units of gene regulation (gates) that are simple to connect and behave predictably. This poses a challenge for eukaryotes due to their complex mechanisms of transcription and translation. To this end, we have developed gates for yeast (Saccharomyces cerevisiae) that are connected using RNA polymerase flux as the signal carrier and are insulated from each other and host regulation. They are based on minimal constitutive promoters (~120 base pairs), for which rules are developed to insert operators for DNA-binding proteins. Using this approach, in this work we constructed nine NOT/NOR gates with nearly identical response functions and 400-fold dynamic range. In circuits, they are transcriptionally insulated from each other by placing ribozymes downstream of terminators to block nuclear export of messenger RNAs resulting from RNA polymerase readthrough. Based on these gates, Cello 2.0 was used to build circuits with up to 11 regulatory proteins. A simple dynamic model predicts the circuit response over days. Genetic circuit design automation for eukaryotes simplifies the construction of regulatory networks as part of cellular engineering projects, whether it be to stage processes during bioproduction, serve as environmental sentinels or guide living therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

A Conceptual Architecture for National Biosurveillance: Moving Beyond Situational Awareness to Enable Digital Detection of Emerging Threats

Despite numerous calls for improvement, the U.S. biosurveillance enterprise remains a patchwork of uncoordinated systems that fail to take advantage of the rapid progress in information processing, communication, and analytics made in the past decade. By synthesizing components from the extensive biosurveillance literature, we propose a conceptual framework for a national biosurveillance architecture and provide suggestions for implementation. The framework differs from the current federal biosurveillance development pathway in that it is not focused on systems useful for “situational awareness,” but is instead focused on the long-term goal of having true warning capabilities. Therefore, a guiding design objective is the ability to digitally detect emerging threats that span jurisdictional boundaries, because attempting to solve the most challenging biosurveillance problem first provides the strongest foundation to meet simpler surveillance objectives. Core components of the vision are: (1) a whole-of-government approach to support currently disparate federal surveillance efforts that have a common data need, including those for food safety, vaccine and medical product safety, and infectious disease surveillance; (2) an information architecture that enables secure, national access to electronic health records, yet does not require that data be sent to a centralized location for surveillance analysis; (3) an inference architecture that leverages advances in ‘big data’ analytics and learning inference engines—a significant departure from the statistical process control paradigm that underpins nearly all current syndromic surveillance systems; and, (4) an organizational architecture with a governance model aimed at establishing national biosurveillance as a critical part of the U.S. national infrastructure. Although it will take many years to implement, and a national campaign of education and debate to acquire public buy-in for such a comprehensive system, the potential benefits warrant increased consideration within the U.S. government.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2016

Controlled Mechanical Buckling for Origami-Inspired Construction of 3D Microstructures in Advanced Materials

Origami is a topic of rapidly growing interest in both the scientific and engineering research communities due to its promising potential in a broad range of applications. Previous assembly approaches of origami structures at the micro/nanoscale are constrained by the applicable classes of materials, topologies and/or capability of control over the transformation. In this work, we introduce an approach that exploits controlled mechanical buckling for autonomic origami assembly of 3D structures across material classes from soft polymers to brittle inorganic semiconductors, and length scales from nanometers to centimeters. This approach relies on a spatial variation of thickness in the initial 2D structures as an effective strategy to produce engineered folding creases during the compressive buckling process. The elastic nature of the assembly scheme enables active, deterministic control over intermediate states in the 2D to 3D transformation in a continuous and reversible manner. Demonstrations include a broad set of 3D structures formed through unidirectional, bidirectional, and even hierarchical folding, with examples ranging from half cylindrical columns and fish scales, to cubic boxes, pyramids, starfish, paper fans, skew tooth structures, and to amusing system-level examples of soccer balls, model houses, cars, and multifloor textured buildings.

36 MATERIALS SCIENCE↗
DOE OSTI2020

AUTONOMIE VID

Autonomie Vehicle Information Database (VID) offers a comprehensive list of vehicle specifications since 1990. The database details more than 65,000 vehicles with hundreds of attributes. The database is the result of the development of a general automated data collection framework as well as the development of building blocks for processing, cleaning, integrating and analyzing complex data. The data has undergone several layers of outlier detections processes, machine learning based imputations methods have been used to deal with missing data problems, and new fields have been created according to the rules of feature engineering. Thanks to this streamlined data pipelines, the resulting processed aggregated data should deliver a unique level of information to the user in which the content can be efficiently maintained and updated.

Moswd, Ayman↗
DOE OSTI2024

Atomic Precision Processing of Two-Dimensional Materials for Next-Generation Microelectronics

The growth of the information era economy is driving the pursuit of advanced materials for microelectronics, spurred by exploration into “Beyond CMOS” and “More than Moore” paradigms. Atomically thin 2D materials, such as transition metal dichalcogenides (TMDCs), show great potential for next-generation microelectronics due to their properties and defect engineering capabilities. This perspective delves into atomic precision processing (APP) techniques like atomic layer deposition (ALD), epitaxy, atomic layer etching (ALE), and atomic precision advanced manufacturing (APAM) for the fabrication and modification of 2D materials, essential for future semiconductor devices. Additive APP methods like ALD and epitaxy provide precise control over composition, crystallinity, and thickness at the atomic scale, facilitating high-performance device integration. Subtractive APP techniques, such as ALE, focus on atomic-scale etching control for 2D material functionality and manufacturing. In APAM, modification techniques aim at atomic-scale defect control, offering tailored device functions and improved performance. Achieving optimal performance and energy efficiency in 2D material-based microelectronics requires a comprehensive approach encompassing fundamental understanding, process modeling, and high-throughput metrology. Finally, the outlook for APP in 2D materials is promising, with ongoing developments poised to impact manufacturing and fundamental materials science. Integration with advanced metrology and codesign frameworks will accelerate the realization of next-generation microelectronics enabled by 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Accelerating Discovery to Deployment: Argonne's Materials Engineering Research Facility (MERF) and Its Role in Scaling Materials Technologies for Water and Resource Solutions

The U.S. Department of Energy (DOE) national laboratories represent a unique class of government‐owned, contractor‐operated research institutions dedicated to conducting research and development (R&D) related activities that address national priorities, supporting and advancing the DOE mission. They play a vital role in sustaining U.S. innovation capacity, stewarding the nation's technical base, and nurturing science and technologies. In this perspective, we highlight the processing science and scaleup capabilities of the Materials Engineering Research Facility (MERF) at DOE's Argonne National Laboratory to demonstrate how DOE National Laboratories bridge fundamental science and applied technology development to accelerate deployment. Case studies are presented on selective membranes for critical mineral recovery, sensors for per‐ and polyfluoroalkyl substances (PFAS) detection, surface functionalization via atomic layer deposition (ALD) and sequential infiltration synthesis (SIS), and lithium recovery from battery recycling waste streams using a novel electrodialysis process. These examples underscore MERF's role in translating innovative technologies into practical solutions for renewable water and critical resource recovery, which also leverage Argonne's analytical and computational capabilities. This perspective also outlines mechanisms for collaborating with the DOE national laboratories to strengthen partnerships across government, the national laboratories, academia, and industry.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Use of Tethys Engineering by the Marine Energy Community (User Review Report)

In partial fulfillment of the Annual (SMART) Milestone for Q3 of FY22, this report details the results of the peer review process carried out on the use and function of Tethys Engineering. During Q3 of FY22, Pacific Northwest National Laboratory (PNNL) solicited broad feedback from a self-selected subset of the marine energy community on the use and effectiveness of key pages on Tethys Engineering. A total of 39 responses were collected from the greater marine energy community.

42 ENGINEERING↗
DOE OSTI2019

Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation

Abstract Machine learning is receiving more attention in classical engineering fields, and in particular, recurrent neural networks (RNNs) coupled with ensemble regression tools have demonstrated the capability of modeling nonlinear dynamic processes. In Part I of this two‐article series, the Lyapunov‐based model predictive control (LMPC) method using a single RNN model and an ensemble of RNN models, respectively, was rigorously developed for a general class of nonlinear systems. In the present article, computational implementation issues of this new control method ranging from training of the RNN models, ensemble regression of the RNN models, and parallel computation for accelerating the real‐time model calculations are addressed. Furthermore, a chemical reactor example is used to demonstrate the implementation and effectiveness of these machine‐learning tools in LMPC as well as compare them with standard state‐space model identification tools.

Wu, Zhe↗
DOE OSTI2022

Two-stage membrane-based process utilizing highly CO 2 -selective membranes for cost and energy efficient carbon capture from coal flue gas: A process simulation study

Membrane technology for CO 2 capture has become an attractive strategy due to its cost and energy efficiency and low materials costs. In the past decade, membrane-based process designs for post-combustion power plant CO 2 capture have been developed, utilizing existing highly CO 2 -permeable membranes with relatively low CO 2 /N 2 selectivity (<50), and have obtained reasonably economic carbon capture. However, few membrane-based process designs were proposed for moderate to highly CO 2 -selective membranes (CO 2 /N 2 selectivity of 50–300, and >300, respectively), which have vastly emerged in recent years, such as various facilitated transport membranes (FTMs). Herein, we proposed a two-stage membrane-base process design targeting economic carbon capture from coal-fired flue gas. This process design features the utilization of highly CO 2 -selective membranes for one-stage CO 2 enrichment to 95% dry-base purity in the first stage and recycle of the remaining CO 2 by a highly CO 2 -permeable membrane in the second stage, in order to achieve economic CO 2 capture with 90% capture rate and >95% CO 2 product purity. Through an integration-iteration membrane model and the Aspen Plus process simulation, a sensitivity study of operating pressures (feed and permeate pressures) and membrane properties (CO 2 permeance and CO 2 /N 2 selectivity) was conducted. Critical CO 2 /N 2 selectivity of 300–400 was found for the highly CO 2 -selctive membranes to meet the demand for cost and energy efficient results. The lowest possible membrane area of 4.8 × 10 5 m 2 and fractional energy of 19.3% were obtained, which is comparable to or even more attractive than reported membrane-based process designs. Here, this work provides a new membrane process design option for highly CO 2 -selective membranes and gives insights on the influence of membrane performance and operation condition.

42 ENGINEERING↗
DOE OSTI2023

Operation Optimization using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Melt-Spun PAN Precursor for Cost-Effective Carbon Fibers in High Pressure Compressed Gas Tankage

The objective of this program was to synthesize melt-spinnable polyacrylonitrile (PAN) precursor that will lead to carbon fiber 25% less expensive than carbon fiber produced from conventional PAN precursor when produced in large volumes. The carbon fiber produced from this precursor needs to achieve ≥ 700 ksi tensile strength with this cost reduction to replace the incumbent fiber. In this program, the technology for producing precursor and then converted carbon fiber (CF) capable of meeting the 700 ksi target was to be demonstrated at a small scale in tows, containing ≥ 100 filaments. If this program moved on to a “Phase 2”, the processes would be scaled up to produce and test Type IV compressed gas storage tanks capable of meeting the 700-bar ultimate targeted FOA performance level. Precursor fiber production typically contributes a large fraction (~50%) to finished CF manufactured cost. Presently, all high strength CFs are manufactured from solution spun PAN precursor, accomplished at low temperatures using large amounts of environmentally unfriendly solvents that impart major impact to line speed and costs involved with handling and reclaiming the solvents and associated “waste streams” required in spinning and extracting those solvents. PAN melt spinning was demonstrated by the company BASF in CF precursor production and used in production of some other acrylic fibers, but significant production challenges remain for meeting the established CF metrics. Melt spinning requires use of plasticizers to suppress the melt temperature of the predominantly PAN formulation to a point sufficiently below the temperature of significant cross-linking. Fortunately, BASF and this team’s earlier work had demonstrated acrylic formulations melt spun with combinations of plasticizing solvents at much lower solvent levels. This project team made significant progress in understanding technical issues and identifying formulation pathways in earlier work in this area where >250ksi strength was achieved. The goal of this program was to pursue a more comprehensive approach with a larger, more diverse team having greater equipment design and overall production capabilities to push rapidly beyond the science to resolve remaining “engineering” issues associated with establishing stable, continuous processes for precursor fiber leading to enhanced, cost-effective CF and composite tank production.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Smoothing-based compressed state Kalman filter for joint state-parameter estimation: Applications in reservoir characterization and CO 2 storage monitoring

The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. Here, we present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO 2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.

42 ENGINEERING↗