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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Isotopic Studies for Tracking Biogenic Carbon during Co-processing of Biomass and Vacuum Gas Oil

Co-processing bio-oils with petroleum-derived feeds in the existing multitrillion dollar refining and distribution infrastructure is an attractive option for introducing renewable energy into the fuels marketplace. Considerable research on co-processing bio-oils and vacuum gas oil (VGO) in fluid catalytic cracking units (FCC) using equilibrium catalysts (E-Cats) demonstrated that biomass-derived molecules decreased activity of and increased carbon laydown on E-Cat. In this work, 13 C-labeled biomass was co-processed with VGO using either an E-Cat or a proprietary zeolite Johnson Matthey catalyst (CP758), which is optimzed for upgrading biomass vapors in a riser reactor, to evaluate the fate of biogenic carbon in FCC products. The results from both catalysts indicate that biogenic carbon was incorporated into alkenes and aromatic hydrocarbons. While no biogenic carbon was found in linear alkanes, it was observed in cycloalkanes during experiments with E-Cat but not with CP758. All produced CO2 was biogenic as was most of the carbon laydown on the catalysts. Several partially deoxygenated biomass compounds, such as alkylphenols and benzofurans, unexpectedly contained small amounts of carbon from VGO. Although this study was conducted with labeled biomass and not bio-oil, it provides insights to reaction mechanisms for co-processing bio-oil and VGO in an FCC, which is required to develop catalysts that efficiently convert bio-oil components during FCC co-processing. This work also demonstrates that comprehensive catalyst development utilizing both biomass- and fossil-derived feeds is required to maximize the incorporation of biogenic carbon into transportation fuels.

09 BIOMASS FUELS↗
DOE OSTI2023

Train, Inform, Borrow, or Combine? Approaches to Process–Guided Deep Learning for Groundwater–Influenced Stream Temperature Prediction

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Solution-Processed Cu 2 Se Nanocrystal Films with Bulk-Like Thermoelectric Performance

Thermoelectric power generation can play a key role in a sustainable energy future by converting waste heat from power plants and other industrial processes into usable electrical power. Current thermoelectric devices, however, require energy intensive manufacturing processes such as alloying and spark plasma sintering. Here, we describe the fabrication of a p-type thermoelectric material, copper selenide (Cu 2 Se), utilizing solution-processing and thermal annealing to produce a thin film that achieves a figure of merit, ZT, which is as high as its traditionally processed counterpart, a value of 0.14 at room temperature. This is the first report of a fully solution-processed nanomaterial achieving performance equivalent to its bulk form and represents a general strategy to reduce the energy required to manufacture advanced energy conversion and harvesting materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. Furthermore, we discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Physical conditions for the r-process

Recent works show that the r-process can proceed by competition between neutron capture and {beta}-decay in low temperature environments (< 5 Multiplication-Sign 10{sup 8} K; cold r-process) where photo-disintegration plays no role. This is in contrast to the traditional picture of the r-process in high temperature environments ({approx} 1 Multiplication-Sign 10{sup 9} K; hot r-process) where the (n, {gamma})-({gamma}, n) equilibrium holds. In this study, we explore nucleosynthesis calculations based on a site-independent model to elucidate the physical conditions leading to cold and hot r-processes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

Effects of a new 3-alpha reaction on the s-process in massive stars

Effect of a new 3-alpha reaction rate on the s-process during the evolution of a massive star of 25 solar mass is investigated for the first time, because the s-process in massive stars have been believed to be established with only minor change. We find that the s-process with use of the new rate during the core helium burning is very inefficient compared to the case with the previous 3-alpha rate. However, the difference of the overproduction is found to be largely compensated by the subsequent carbon burning. Since the s-process in massive stars has been attributed so far to the neutron irradiation during core helium burning, our finding reveals for the first time the importance of the carbon burning for the s-process during the evolution of massive stars.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

The r-process nucleosynthesis: Nuclear physics challenges

About half of the nuclei heavier than iron observed in nature are produced by the socalled rapid neutron capture process, or r-process, of nucleosynthesis. The identification of the astrophysics site and the specific conditions in which the r-process takes place remains, however, one of the still-unsolved mysteries of modern astrophysics. Another underlying difficulty associated with our understanding of the r-process concerns the uncertainties in the predictions of nuclear properties for the few thousands exotic neutron-rich nuclei involved and for which essentially no experimental data exist. The present contribution emphasizes some important future challenges faced by nuclear physics in this problem, particularly in the determination of the nuclear structure properties of exotic neutron-rich nuclei as well as their radiative neutron capture rates and their fission probabilities. These quantities are particularly relevant to determine the composition of the matter resulting from the r-process. Their impact on the r-abundance distribution resulting from the decompression of neutron star matter is discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

Plasma process optimization for N-type doping applications

Plasma doping (PLAD) has been adopted across the implant technology space and into high volume production for both conventional DRAM and NAND doping applications. PLAD has established itself as an alternative to traditional ion implantation by beamline implantation. The push for high doping concentration, shallow doping depth, and conformal doping capability expand the need for a PLAD solution to meet such requirements. The unique doping profile and doping characteristics at high dose rates allow for PLAD to deliver a high throughput, differentiated solution to meet the demand of evolving transistor technology. In the PLAD process, ions are accelerated to the wafer as with a negative wafer bias applied to the wafer. Competing mechanisms, such as deposition, sputtering, and etching inherent in plasma doping require unique control and process optimization. In this work, we look at the distinctive process tool control and characterization features which enable an optimized doping process using n-type (PH{sub 3} or AsH{sub 3}) chemistries. The data in this paper will draw the relationship between process optimization through plasma chemistry study to the wafer level result.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Nucleosynthesis and the rp-process

Production of elements heavier than iron, their abundance and cite of production remain an active field of research to-date. In this paper I would present a brief review of the nucleosynthesis process and then focus further on the proton capture processes with particular emphasis on the nuclear physics aspects of the rp-process. The present calculation clearly shows that the electron capture rates on waiting point nuclei are at least of similar magnitude as the competing positron decay rates under rp-process conditions. The study strongly suggests that electron capture rates form an integral part of weak-interaction mediated rates under rp-process conditions and should not be neglected in nuclear reaction network calculations as done in past.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Impact of the uncertainty in α-captures on {sup 22}Ne on the weak s-process in massive stars

Massive stars at solar metallicity contribute to the production of heavy elements with atomic masses between A = 60 and A = 90 via the so-called weak s-process (which takes place during core He and shell C burning phases). Furthermore, recent studies have shown that rotation boosts the s-process production in massive stars at low metallicities, with a production that may reach the barium neutron-magic peak. These results are very sensitive to neutron source and neutron poison reaction rates. For the weak s-process, the main neutron source is the reaction {sup 22}Ne(α,n){sup 25}Mg, which is in competition with {sup 22}Ne(α,γ){sup 26}Mg. The uncertainty of both rates strongly affects the nucleosynthesis predictions from stellar model calculations. In this study, we investigate the impact of the uncertainty in α-captures on {sup 22}Ne on the s-process nucleosynthesis in massive stars both at solar and at very low metallicity. For this purpose, we post-process, with the Nugrid mppnp code, non-rotating and rotating evolutionary models 25M{sub ⊙} stars at two different metallicities: Z = Z{sub ⊙} and Z = 10{sup −5}Z{sub ⊙}, respectively. Our results show that uncertainty of {sup 22}Ne(α,n){sup 25}Mg and {sup 22}Ne(α,γ){sup 26}Mg rates have a significant impact on the final elemental production especially for metal poor rotating models. Beside uncertainties in the neutron source reactions, for fast rotating massive stars at low metallicity we revisit the impact of the neutron poisoning effect by the reaction chain {sup 16}O(n,γ){sup 17}O(α,γ){sup 21}Ne, in competition with the {sup 17}O(α,n){sup 20}Ne, recycling the neutrons captured by {sup 16}O.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Development of process parameters for 22 nm PMOS using 2-D analytical modeling

The complementary metal-oxide-semiconductor field effect transistor (CMOSFET) has become major challenge to scaling and integration. Innovation in transistor structures and integration of novel materials are necessary to sustain this performance trend. CMOS variability in the scaling technology becoming very important concern due to limitation of process control; over statistically variability related to the fundamental discreteness and materials. Minimizing the transistor variation through technology optimization and ensuring robust product functionality and performance is the major issue.In this article, the continuation study on process parameters variations is extended and delivered thoroughly in order to achieve a minimum leakage current (I{sub LEAK}) on PMOS planar transistor at 22 nm gate length. Several device parameters are varies significantly using Taguchi method to predict the optimum combination of process parameters fabrication. A combination of high permittivity material (high-k) and metal gate are utilized accordingly as gate structure where the materials include titanium dioxide (TiO{sub 2}) and tungsten silicide (WSi{sub x}). Then the L9 of the Taguchi Orthogonal array is used to analyze the device simulation where the results of signal-to-noise ratio (SNR) of Smaller-the-Better (STB) scheme are studied through the percentage influences of the process parameters. This is to achieve a minimum I{sub LEAK} where the maximum predicted I{sub LEAK} value by International Technology Roadmap for Semiconductors (ITRS) 2011 is said to should not above 100 nA/µm. Final results shows that the compensation implantation dose acts as the dominant factor with 68.49% contribution in lowering the device’s leakage current. The absolute process parameters combination results in I{sub LEAK} mean value of 3.96821 nA/µm where is far lower than the predicted value.

36 MATERIALS SCIENCE↗
DOE OSTI2015

An in-depth analysis of the physico-mechanical properties imparted by agricultural fibers and food processing residues in polypropylene biocomposites

The use of agricultural and food processing residues as potential reinforcements in plastics has been extensively studied. However, there is a large variation in the mechanical performance of agricultural fiber-based biocomposites due to different processing materials and parameters. An in-depth comparison of the resulting effect of the agricultural filler on the matrix is often not possible given the discrepancy in processing conditions. This study seeks to determine the intrinsic properties of agricultural fibers and food processing residues for their use in polypropylene biocomposites based on a standardization of experimental design. The effect of 25wt% loading of miscanthus, fall-and spring-harvest switchgrass, wheat straw, oat hull, soy hull, soy stalk, hemp and flax on the physico-mechanical properties of polypropylene biocomposites was investigated. The addition of fiber led to an improvement in flexural strength, flexural modulus, and tensile modulus, and a general decrease in tensile strength at yield, elongation at break and Izod impact strength. Scanning electron microscopy highlighted the interfacial adhesion, orientation and distribution of the fibers within the matrix, confirming that fiber length and dispersion within the matrix are positively correlated with mechanical properties. The crystallization of the polypropylene phase and a compositional analysis of the agricultural fibers and processing residues were also compared to offer insight into the effect of the filler’s intrinsic properties on the resulting material performance.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A concept for non-invasive temperature measurement during injection moulding processes

Current models of the injection moulding process insufficiently consider the thermal interactions between melt, solidified material and the mould. A detailed description requires a deep understanding of the underlying processes and a precise observation of the temperature. Because todays measurement concepts do not allow a non-invasive analysis it is necessary to find new measurement techniques for temperature measurements during the manufacturing process. In this work we present the idea of a set up for a tomographic ultrasound measurement of the temperature field inside a plastics melt. The goal is to identify a concept that can be installed on a specialized mould for the injection moulding process. The challenges are discussed and the design of a prototype is shown. Special attention is given to the spatial arrangement of the sensors. Besides the design of a measurement set up a reconstruction strategy for the ultrasound signals is required. We present an approach in which an image processing algorithm can be used to calculate a temperature distribution from the ultrasound scans. We discuss a reconstruction strategy in which the ultrasound signals are converted into a spartial temperature distribution by using pvT curves that are obtained by dilatometer measurements.

42 ENGINEERING↗
DOE OSTI2016

Some functional limit theorems for compound Cox processes

An improved version of the functional limit theorem is proved establishing weak convergence of random walks generated by compound doubly stochastic Poisson processes (compound Cox processes) to Lévy processes in the Skorokhod space under more realistic moment conditions. As corollaries, theorems are proved on convergence of random walks with jumps having finite variances to Lévy processes with variance-mean mixed normal distributions, in particular, to stable Lévy processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Data-driven materials research enabled by natural language processing and information extraction

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains - enabled by techniques adapted from the field of natural language processing - therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. Here, we discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Processing behavior evolution of recycled polypropylene: An integrated experimental and Computer-Aided engineering simulation study

Polypropylene (PP) comprises 21% of global plastics production and 18% of plastics waste, yet less than 1% of solid-waste PP is recycled in the United States (U.S.), representing significant environmental and economic challenges. Mechanical recycling, the most prevalent recycling method, subject's materials to thermomechanical stresses, which typically degrade polymer properties, affecting the quality of polymer products. This study replicates the impact of mechanical recycling through multiple extrusion cycles to examine the effects on PP's processing behavior. Dynamic scanning calorimetry (DSC) measurements showed stable melting behavior across all processing conditions, while crystallization analysis exhibited consistent shifts in kinetic parameters. Rheological characterization demonstrated progressive viscosity reductions through successive cycles, particularly pronounced at elevated reprocessing temperatures. Here, the integration of this experimental data into injection molding simulations showed that recycled PP maintains viable processing characteristics. Our findings establish quantitative correlations between processing history and material behavior, enabling optimization of processing parameters directly rather than relying on trial-and-error approaches. While these results reflect idealized recycling conditions with minimal contamination, they provide a framework for understanding fundamental property evolution during mechanical recycling.

42 ENGINEERING↗
DOE OSTI2012

DETECTION OF ELEMENTS AT ALL THREE r-PROCESS PEAKS IN THE METAL-POOR STAR HD 160617

We report the first detection of elements at all three r-process peaks in the metal-poor halo star HD 160617. These elements include arsenic and selenium, which have not been detected previously in halo stars, and the elements tellurium, osmium, iridium, and platinum, which have been detected previously. Absorption lines of these elements are found in archive observations made with the Space Telescope Imaging Spectrograph on board the Hubble Space Telescope. We present up-to-date absolute atomic transition probabilities and complete line component patterns for these elements. Additional archival spectra of this star from several ground-based instruments allow us to derive abundances or upper limits of 45 elements in HD 160617, including 27 elements produced by neutron-capture reactions. The average abundances of the elements at the three r-process peaks are similar to the predicted solar system r-process residuals when scaled to the abundances in the rare earth element domain. This result for arsenic and selenium may be surprising in light of predictions that the production of the lightest r-process elements generally should be decoupled from the heavier r-process elements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗