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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

XANES spectroscopy of sulfides stable under reducing conditions

X-ray absorption near-edge structure (XANES) spectroscopy is a powerful technique to quantitatively investigate sulfur speciation in geologically complex materials such as minerals, glasses, soils, organic compounds, industrial slags, and extraterrestrial materials. This technique allows non-destructive investigation of the coordination chemistry and oxidation state of sulfur species ranging from sulfide (2–oxidation state) to sulfate (6+ oxidation state). Each sulfur species has a unique spectral shape with a characteristic K-edge representing the s → p and d hybridization photoelectron transitions. As such, sulfur speciation is used to measure the oxidation state of samples by comparing the overall XANES spectra to that of reference compounds. Although many S XANES spectral standards exist for terrestrial applications under oxidized conditions, new sulfide standards are needed to investigate reduced (oxygen fugacity, f O 2 , below IW) silicate systems relevant for studies of extraterrestrial materials and systems. Sulfides found in certain meteorites (e.g., enstatite chondrites and aubrites) and predicted to exist on Mercury, such as CaS (oldhamite), MgS (niningerite), and FeCr 2 S 4 (daubréelite), are stable at f O 2 below IW-3 but rapidly oxidize to sulfate and/or produce sulfurous gases under terrestrial surface conditions. XANES spectra of these compounds collected to date have been of variable quality, possibly due to the unstable nature of certain sulfides under typical (e.g., oxidizing) laboratory conditions. A new set of compounds was prepared for this study and their XANES spectra are analyzed for comparison with potential extraterrestrial analogs. S K -edge XANES spectra were collected at Argonne National Lab for FeS (troilite), MnS (alabandite), CaS (oldhamite), MgS (niningerite), Ni 1–x S, NiS 2 , CaSO 4 (anhydrite), MgSO 4 , FeSO 4 , Fe 2 (SO 4 ) 3 , FeCr 2 S 4 (daubréelite), Na 2 S, Al 2 S 3 , Ni 7 S 6 , and Ni 3 S 2 ; the latter five were analyzed for the first time using XANES. These standards expand upon the existing S XANES end-member libraries at a higher spectral resolution (0.25 eV steps) near the S K -edge. Processed spectra, those that have been normalized and “flattened,” are compared to quantify uncertainties due to data processing methods. Future investigations that require well-characterized sulfide standards, such as the ones presented here, may have important implications for understanding sulfur speciation in reduced silicate glasses and minerals with applications for the early Earth, Moon, Mercury, and enstatite chondrites.

Anzures, Brendan A.↗
DOE OSTI2024

Integrated experimental studies of pore structure and fluid uptake in the Bossier Shale in eastern Texas, USA

Within the Haynesville-Bossier Shale complex, the Bossier Shale has not been extensively studied by either industry and academia, despite it being an unconventional gas reservoir and a potential caprock for carbon storage in the underlaying Haynesville Shale. The lack of knowledge of the complex pore structures and fluid-rock interactions hinders the effective extraction of gas and the characterization of fluid reservoirs and sealing capacity. Integrated experimental studies of pore structure and fluid-rock interactions were conducted in seven Bossier Shale core samples collected in eastern Texas. Petrographic, geochemical, and petrophysical properties such as mineral composition, organic richness, thermal maturity, porosity, pore/pore throat diameter distribution, water-accessible pores, liquid water imbibition, and water vapor adsorption were characterized using complementary approaches of thin-section petrography, scanning electron microscopy, X-ray diffraction, total organic matter, pyrolysis, mercury intrusion porosimetry, nuclear magnetic resonance, (Ultra-) small angle X-rays scattering as well as small angle neutron scattering with deuterated liquids and contrast variation. Further, the results show that the thermally mature Bossier Shales are composed of mixed argillaceous mudstone, mixed mudstone, and mixed carbonate mudstone. The shale contains both organic and inorganic pores, with porosities of 3.24-9.37 %, pore-to-throat ratios of 1.65 to 19.4, and water-accessible pores accounting for 28.7-72.6 % of total pores. Approaches of liquid water imbibition and water vapor adsorption, with and without direct contact of water with shale samples, indicate that liquid water first enters the nano-sized pores under high capillary pressures, and water vapor adsorption is mainly controlled by both clay minerals and pores with diameters less than 10 nm. These findings contribute to a better understanding of pore structures and water-shale interactions and their controlling factors in the Bossier Shale.

58 GEOSCIENCES↗
DOE OSTI2023

Grating-Based Imaging-Scattering with Portable Neutron Generator

Company: Adelphi Technology, Inc. Title: Grating-Based Imaging-Scattering with Portable Neutron Generator PI: Dr. Jay Theodore Cremer, Jr. Topic: 26a Statement of the problem or situation that is being addressed. Successful plant growth depends upon an efficient and robust root system. The plant root is part of a larger system of water and microbial flows in the soil system. While much effort has been exerted to develop an imaging system for water, microbes, and roots, the problem is challenging, and no widely accepted imaging method currently exists. The optical solutions use a highly modified soil system. X-ray imaging methods are insensitive to the soft tissues in the presence of sand. Thermal neutron imaging has been often tested, but found inadequate, due to limited access and low image resolution. This project will develop a new strategy for neutron imaging of plant/soil systems. The project will allow long duration experiments in greenhouse environments and increase the image information content to the micron scale. General statement of how this problem is being addressed. Portable, rugged thermal and fast neutron sources are being developed where portable means a two-soldier team can carry the source and power unit to survey rough terrain for explosives. In the past decade, microfabrication of X-ray and thermal/cold neutron optics has opened a new imaging strategy. The standard transmission image is now supplemented with simultaneous acquisition of a phase contrast image and an image revealing scattering features. In materials science, the interferometric neutron scattering image has been used to detect early crack formation in stressed additive manufacturing test samples. The detection requires sensitivity to scattering features at the 1-micron scale. By addition of our proposed grating-optic to Adelphi Technology’s radiographic/tomographic imaging system, which is based on portable thermal neutron source, the resulting thermal neutron scatter image of the plant/soil system, will reveal details at 1-micron. Commercial Applications and Other Benefits Neutron interferometry imaging has greater penetration through large metal components compared to industrial X-ray imaging. The low-cost, large area optics developed for greenhouse applications, combined with the robust, portable neutron generator, can then be marketed as a system for inspection of additive manufactured components. In the aerospace industry, all freshly printed components are validated with X-ray CT. Scheduled maintenance again requires X-ray CT as the ability to predict aerospace component lifetime does not yet exist. Large aerospace components are only partially observed with X-ray imaging. Key Words Neutron radiography/tomography, grating interferometry, thermal neutron generator imaging, plant root and soil imaging, rhizosphere imaging, deployable neutron imaging systems Summary for Members of Congress A rugged, portable source of thermal neutrons is adapted for neutron interferometry imaging with the addition of low-cost, 3D printed optics. The first application of our proposed deployable, compact thermal neutron generator imaging system, using a grating optic, is plant root/soil science in greenhouse settings and agricultural laboratories.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Towards Low-Temperature CVD Synthesis and Characterization of Mono- or Few-Layer Molybdenum Disulfide

Molybdenum disulfide (MoS 2 ) transistors are a promising alternative for the semiconductor industry due to their large on/off current ratio (>10 10 ), immunity to short-channel effects, and unique switching characteristics. MoS 2 has drawn considerable interest due to its intriguing electrical, optical, sensing, and catalytic properties. Monolayer MoS 2 is a semiconducting material with a direct band gap of ~1.9 eV, which can be tuned. Commercially, the aim of synthesizing a novel material is to grow high-quality samples over a large area and at a low cost. Although chemical vapor deposition (CVD) growth techniques are associated with a low-cost pathway and large-area material growth, a drawback concerns meeting the high crystalline quality required for nanoelectronic and optoelectronic applications. This research presents a lower-temperature CVD for the repeatable synthesis of large-size mono- or few-layer MoS 2 using the direct vapor phase sulfurization of MoO 3 . The samples grown on Si/SiO 2 substrates demonstrate a uniform single-crystalline quality in Raman spectroscopy, photoluminescence (PL), scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and scanning transmission electron microscopy. These characterization techniques were targeted to confirm the uniform thickness, stoichiometry, and lattice spacing of the MoS 2 layers. The MoS 2 crystals were deposited over the entire surface of the sample substrate. With a detailed discussion of the CVD setup and an explanation of the process parameters that influence nucleation and growth, this work opens a new platform for the repeatable synthesis of highly crystalline mono- or few-layer MoS 2 suitable for optoelectronic application.

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

Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes

Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. Here in this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The \projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction

42 ENGINEERING↗
DOE OSTI2023

Structural analysis of molten materials by a remote fiber optic Raman sensor

This study presents a novel in situ high-temperature fiber optic Raman probe that enables the study of the physical properties and structure of molten samples at temperatures up to 1400 °C. To demonstrate the functionality of the high-temperature fiber optic Raman probe, different composition mold fluxes were evaluated in this report. The Raman spectra at flux molten temperature were successfully collected and analyzed. A deconvolution algorithm was employed to identify peaks in the spectra associated with the molecular structure of the components in each sample. Here, the experimental results demonstrate that the composition-dependent Raman signal shift can be detected at high temperatures, indicating that molten materials analysis using a high-temperature Raman system shows significant promise. This flexible and reliable high-temperature Raman measurement method has great potential for various applications, such as materials development, composition and structure monitoring during high-temperature processing, chemical identification, and process monitoring in industrial production.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Rapid and high-throughput determination of sorghum ( Sorghum bicolor ) biomass composition using near infrared spectroscopy and chemometrics

Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum (Sorghum bicolor) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models’ effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.

09 BIOMASS FUELS↗
DOE OSTI2026

Scalable, low-cost ink-based processing of high-performance silver selenide thermoelectrics

The growing global energy demand and its accelerating contribution to climate change emphasize the urgent need for sustainable energy conversion/harvesting technologies. Thermoelectric (TE) devices offer a compelling route to directly convert waste heat into electricity and enable solid-state cooling without moving parts or harmful refrigerants. Achieving their full potential requires not only higher TE performance (zT) but also scalable, low-cost manufacturing processes. Here, we introduce a transformative ink-based processing approach for scalable manufacturing of high-performance silver selenide-based TE materials and devices. Using a simple, high-throughput ink-mixing and blade coating strategy, our Ag 2 Se-based materials under the optimized composition and processing conditions yield an ultrahigh room-temperature power factor of 2.8 mW m −1 K −2 , over 100% higher than baseline samples and a reproducible figure of merit zT of 1 at room temperature. A thermoelectric generator (TEG) achieves a very competitive power density of 112 mW cm −2 at a 90 °C temperature difference between the hot and cold sides of the device, which is among the highest reported for silver selenide-based TE devices to date. This facile, scalable ink-based processing establishes a practical pathway toward industrial-scale manufacturing and widespread adoption of thermoelectric devices, advancing sustainable energy technologies.

Bappy, Md. Omarsany [University of Notre Dame, IN ↗
DOE OSTI2024

Data for Rapid and High-Throughput Determination of Sorghum ( Sorghum bicolor ) Biomass Composition using Near Infrared Spectroscopy and Chemometrics

Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum ( Sorghum bicolor ) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models’ effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.

Biomass Analytics↗
DOE OSTI2020

Air compressor load forecasting using artificial neural network

Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network inair compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems’ electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load managementand look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Networkand Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. Here, the forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient ofdetermination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and meanabsolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield goodresults for compressors using variable speed drive (average R 2 = 0.8 and no naïve forecasting), only the longshort-term memory model gives acceptable results for compressors using on/off control (average R 2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting active forecasting).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Densification, microstructure, and mechanical properties of Mo–30W alloys fabricated from conditioned powders

Refractory alloys, such as molybdenum-based systems, are attracting growing interest for applications in extreme environments, such as in the nuclear and aerospace industries. Recent advances in sintering technologies, coupled with mechanical alloying, have enabled the tailored design of these alloys by leveraging powder characteristics to control final microstructures and mechanical properties. In this study, Mo-30W alloys were fabricated using electric field-assisted sintering (EFAS) from ball-milled powders with and without hydrogen treatment to investigate the influence of surface oxides on material properties and sintering behavior. The results revealed that samples processed from as-ball-milled powder contained a high density of oxides within the microstructure, whereas oxide presence was significantly reduced in samples fabricated from hydrogen-treated powders. Interestingly, the two powder types led to opposite trends in grain size distribution: samples from untreated powders exhibited grain refinement from sample periphery to the center, while samples from hydrogen-treated powders showed grain coarsening toward the center. This behavior is attributed to temperature gradients present during sintering due to electrical percolation pathway differences during Joule heating. The powder surface oxides may have influenced the temperature distribution and grain evolution. Microhardness profiles measured along both axial and thickness directions were consistent with the grain size distribution. Furthermore, oxide films on powder surfaces have delayed densification by hindering particle necking and atomic diffusion during sintering.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Co-Location of Cellulosic Bioethanol and Alcohol-to-Jet (ATJ) Production Facilities for Targeted Scale-Up of Sustainable Aviation Fuel (SAF) Production

Achieving aerospace industry net-zero emissions by 2050 requires rapid scaling of sustainable aviation fuel (SAF) production. Leveraging existing infrastructure, proven technologies like Alcohol-to-Jet (ATJ), and low carbon intensity (CI) feedstocks (e.g., switchgrass and miscanthus) can support this transition and help achieve near-term emissions reduction targets. This study evaluates the implications of lignocellulosic ethanol biorefinery siting and integration with petroleum refineries to produce SAF across 1000 sites randomly sampled from areas suitable for perennial grasses in the U.S. rainfed region. To better understand the logistics of material transport and handoffs, we integrated models of biomass harvest, transport, ethanol, and ATJ production in a stochastic framework based on Monte Carlo simulations to characterize SAF minimum selling price (MSP) and carbon intensity (CI), considering site-specific parameters (e.g., feedstock production, transportation, taxes, incentives). The results indicate trade-offs between MSP and CI across locations, with median MSP ranging from 7.9 to 12.8 USD·gal −1 and CI from −9.7 to 39.4 gCO 2 e·MJ −1 . Despite high estimated decarbonization costs (580 USD·tonCO 2 e −1 ), our results indicate that site-specific deployment of ATJ with low-CI feedstocks can improve sustainability outcomes. The framework provides a systematic approach to assess cost and sustainability trade-offs across locations, considering the end-to-end supply chain and supporting an informed investment in SAF production.

09 BIOMASS FUELS↗
DOE OSTI2021

Modeling freight mode choice using machine learning classifiers: a comparative study using Commodity Flow Survey (CFS) data

This study explores the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naïve Bayes, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, Classification and Regression Tree, Random Forest, Boosting and Bagging, along with the classical Multinomial Logit model. US 2012 Commodity Flow Survey data are used as the primary data source; we augment it with spatial attributes from secondary data sources. The performance of the classifiers is compared based on prediction accuracy results. The current research also examines the role of sample size and training-testing data split ratios on the predictive ability of the various approaches. In addition, the importance of variables is estimated to determine how the variables influence freight mode choice. The results show that the tree-based ensemble classifiers perform the best. Specifically, Random Forest produces the most accurate predictions, closely followed by Boosting and Bagging. With regard to variable importance, shipment characteristics, such as shipment distance, industry classification of the shipper and shipment size, are the most significant factors for freight mode choice decisions.

42 ENGINEERING↗
DOE OSTI2024

Utilizing coal-derived solid carbon materials towards next-generation smart and multifunction pavements

This project focused on developing an eco-friendly, multifunctional pavement system, called Coal-Derived Carbon Enabled Smart Pavement (CDC-SP), using coal-derived materials. The system utilizes coal-derived pyrolyzed char as a key component to create electrically conductive asphalt concrete for smart pavements that offer self-heating, self-sensing, and self-healing capabilities. These functions are made possible by the conductive properties of coal-char, which enable Ohmic heating for snow and ice deicing, piezoresistivity for self-sensing, and induction heating for self-healing of cracks caused by stress or aging. The team successfully created CDC-SP samples with over 50% coal char composition, demonstrating desirable mechanical properties such as rutting resistance, moisture susceptibility, and cracking resistance. These samples also exhibited strong electrical and thermal conductivity, making them ideal for heating applications. Extensive tests confirmed the pavement’s effectiveness in melting ice and snow and maintaining durability under environmental conditions. The CDC-SP presents a promising approach to integrating U.S. domestic coal resources into infrastructure projects, providing environmental and economic benefits by enhancing pavement performance while utilizing low-cost coal-derived materials. Further research and compliance with industry standards are recommended before commercial scaling.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Adaptation and Plasticity of Nannochloropsis sp. in Response to Seasonal and Geographic Climate Variation

Phytoplankton respond to their environment through genetic adaptation and plasticity to maintain fitness. This poses challenges when growing microalgae for industrial applications because, inherently, outdoor mass cultivation may lead to adaptations that alter desirable phenotypic traits and environmental niches. Here, we used common garden experiments to quantify the plasticity and adaptive responses to seasonal and geographic climate variation of Nannochloropsis, a microalga commonly used in biotechnology. An initially monoclonal strain was grown outdoors across four locations in Hawaii, Texas, California, and New Mexico. Following 17 and 22 months of cultivation outdoors, we collected samples during winter and summer, respectively, and we compared strains' growth from the four sites across temperature and light gradients in the laboratory. Despite hundreds of generations of exposure to divergent climates, with ~20°C and three-fold differences in daily light intensity, strains showed only minor differences in performance. Thermal performance varied more among seasons than sites, whereas light performance varied with both season and site. Our study indicates that Nannochloropsis exhibits broad plasticity in response to light and temperature, which may inhibit genetic adaptation in space or time. Highly variable field conditions, with daily and seasonal climate fluctuations, may favor plasticity and prevent the rapid adaptation often seen in laboratory studies of microorganisms in constant environments.

common garden experiment↗
DOE OSTI2022

Luminescent silica microagglomerates, synthesis, and environmental testing

Abstract Tracking mass through harsh environments requires surrogate particles that withstand the event and endure until sampling. Silica-covered quantum dots have been shown to withstand a range of environmental pHs from months to years; in this work they are shown to endure in anticipated local environments. Two methods of particle synthesis were employed to produce luminescent silica with particle diameters 0.1–4 μm. These tracer particles scale for mass production, tolerate harsh environments, and endure in debris. They could be deployed in places such as chemical explosions, industrial processes, geologic test beds, oil and gas fields, nuclear reactors, and geothermal plants to track mass under harsh conditions. Graphical abstract

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

Manufacturing Dissolvable Alloy Components using ShAPE: A Study on Solid Phase Processing of Mg-W Composites and WJ11 Alloys

Dissolvable alloys and composites are an emerging class of materials that demonstrate tailored sorptivity in corrosive environments. This work explored the viability of solid phase processing (SPP) techniques, namely the shear assisted processing and extrusion (ShAPETM) method, to manufacture dissolvable magnesium composites and alloys components such as rods and tubes. The study also determined the effects of material composition and manufacturing process conditions on material microstructures developed during SPP and the resulting performance of the components. Explicit properties of interest included ultimate tensile strength, ultimate compressive strength, and yield strength. The application fields of interest for dissolvable alloys were identified as oil-and-gas operations and biomedical implants. Accordingly, PNNL collaborated with industrial and academia partners, Fortek Industries, Houston and University of Pittsburgh, during the course of this project for material and ShAPE process development. PNNL determined the viability of the using ShAPE to process the custom-designed materials provided by the project partners. This was done by identifying process parameters such as tool rotation rate, tool plunge rate, and process cooling optimal for manufacturing components with consolidated microstructures. Subsequently, PNNL also performed microstructural characterization on unprocessed and processed samples and mechanical property testing. One of the major findings from this work is that ShAPE was able to manufacture dissolvable magnesium composite and alloy rods and tubes with consolidated homogeneous microstructures and minimal macroscale defects on component surfaces. It was observed that samples processed via ShAPE demonstrated an average grain size < 2 - 5 µm, which was markedly lower than that of the precursors with 8 – 13 µm grains. Results showed that the ShAPE synthesized dissolvable magnesium composite and alloy strength was on par with or better than those components that were manufactured using traditional manufacturing processes such as forging and extrusion. It is noteworthy that the ShAPE samples were made with considerably lower number of process steps.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Freeform thermoelectrics in single-step manufacturing: additive manufacturing of bismuth-telluride thermoelectrics

The project succeeded in producing crack-free Bismuth Telluride thermoelectric parts with density exceeding 98% through laser powder bed fusion (LPBF) additive manufacturing (AM). This greatly exceeded the highest previously reported density of 88% and is the highest among all semiconducting materials processed by LPBF. The additively manufactured material shows comparable Seebeck coefficient as conventional form and can be made into complex geometries with reduced material loss. On the other hand, measured properties are dramatically sensitive to the AM process parameters used, such that with identical composition, the Seebeck coefficient can be controllably tuned from +120 µV/K to -207 µV/K, which means the material switches between an n-type to a p-type semiconductor depending on processing. These changes are accompanied by significant differences in the as-processed microstructure due to rapid solidification. A machine learning protocol was developed and greatly reduced the experimental burden of the project, reducing the typical process optimization period of 2 years to 6 months. The project was fully successful in the objective of producing defect-free, complex geometry of bismuth-telluride parts through LPBF, but only partially successful in achieving performance goals. First, cost reduction of manufacturing, as measured by material waste, was successfully reduced by up to 70% compared to conventional manufacturing methods. This exceeded the proposed 30% reduction in materials waste needed to reach the 20% cost reduction goal of the project. On the other hand, the device performance, as measured by Seebeck coefficient, failed to reach the 40% improvement in efficiency. Rather, we observe comparable Seebeck coefficient between AM samples and conventionally processed counterparts. The device-level efficiency improvement does exceed 40% for complex geometry samples due to shape-induced increase in temperature gradients, but this was not the originally proposed metric. The machine learning approach developed in this project greatly accelerated the process optimization and can be adopted for fast development of AM processing parameters for other brittle and otherwise difficult-to-print materials. For the public, we deliver an efficient and widely adoptable process for incorporating waste-heat harvesting thermoelectric devices in both industrial and commercial heat exchangers. The geometric flexibility allows the capturing device to conform to the shape of the heat source to improve the system-level conversion efficiency. The technique can be deployed on any commercial LBPF systems with zero modifications, thus poses minimal adoption barrier for any manufacturer that already employed AM technology. Beyond bismuth-telluride, the machine-learning guided optimization protocol can be used in the future to reduce both the time and cost of process development for AM of other energy conversion and harvesting materials.

36 MATERIALS SCIENCE↗