Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “throughput”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Review of multi-faceted morphologic signatures of actinide process materials for nuclear forensic science

Particle morphology is an emerging signature that has the potential to identify the processing history of unknown nuclear materials. Using readily available scanning electron microscopes (SEM), the morphology of nearly any solid material can be measured within hours. Coupled with robust image analysis and classification methods, the morphological features can be quantified and support identification of the processing history of unknown nuclear materials. The viability of this signature depends on developing databases of morphological features, coupled with a rapid data analysis and accurate classification process. With developed reference methods, datasets, and throughputs, morphological analysis can be applied within days to (i) interdicted bulk nuclear materials (gram to kilogram quantities), and (ii) trace amounts of nuclear materials detected on swipes or environmental samples. In conclusion, this review aims to develop validated and verified analytical strategies for morphological analysis relevant to nuclear forensics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Identifying high-impact and high-uncertainty parameters in MiniFuel model predictions

The MiniFuel irradiation platform at Oak Ridge National Laboratory's High Flux Isotope Reactor (HFIR) is a flexible, high-throughput separate effects test capability. Finite element thermal models are relied upon to design MiniFuel experiments and to achieve experimental objectives. Recent reports show good agreement in the model prediction of target fuel temperatures, but as the capability of the experiments is extended to higher temperatures, the uncertainty in the model predictions must be quantified. To that end, high-impact, high-uncertainty parameters that contribute the most uncertainty to the model are identified. The uncertainty quantification was accomplished through a series of screening and sensitivity analyses. The first analysis utilizes the method of Morris to perform a computationally efficient preliminary screening that considers uncertainty in a large number of the model inputs. The most important parameters identified in the Morris screening study were then considered in a Sobol sensitivity analysis that more robustly ranks and quantifies the uncertainty associated with each parameter. From these analyses, it was determined that thermal contact conductance between components is the parameter that contributes the highest uncertainty. The estimated uncertainty of the MiniFuel model fuel temperature predictions is ±80 °C in the removable beryllium and ±40 °C in the vertical experiment facilities. In conclusion, the framework established by the series of sensitivity analyses presented herein could easily be adapted to fit the needs of accelerated fuel qualification processes.

Fuel, Irradiation↗
DOE OSTI2025

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗
DOE OSTI2020

Rapid Open-Air Fabrication of Perovskite Solar Modules

We report on the open-air fabrication of perovskite solar modules with key advances including scalable large-area spray deposition, new monolithic integration scribing techniques, advanced photoluminescence characterization, and reproducible high-throughput manufacturability. Perovskite deposition with linear speeds of 12 m/min without a post-anneal are demonstrated with improved device performance, luminescent yield and >10X carrier lifetimes. Manufacturability using monolithic integration of series-connected modules is accomplished with a new indirect fiber laser ablation scribing method. A stable cell and module power output of 18.0% and 15.5%, respectively, was achieved with a subcell V oc > 1.06V. A comprehensive supporting technoeconomic analysis details the entire in-line manufacturing process from the glass substrate to the junction box of the encapsulated module. Here, the module manufacturing cost, balance of system costs and levelized cost of energy for a range of module efficiencies and lifetimes provides insights for the necessary tool speeds, efficiencies, and lifetimes for utility-scale energy generation.

14 SOLAR ENERGY↗
DOE OSTI2022

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2024

Technoeconomic analysis of perovskite/silicon tandem solar modules

Tandem photovoltaic (PV) modules offer an opportunity to improve the efficiency and energy yield of available solar resources compared with single-junction devices. Here, we present a cost model and sensitivity analysis of perovskite/silicon (Si) tandem modules to understand how design choices impact overall module costs. We find a minimum sustainable price (MSP) of $\$$0.428/W DC for our baseline two-terminal design and $\$$0.423/W DC for our baseline four-terminal design, each at a module efficiency of 25% and module production of 3 GW per year in the United States. We find that the choice of Si cell architecture, overall module efficiency, and factory throughput have the most significant impacts on cost and competitiveness. The dynamic model provided is designed for researchers, companies, and interested individuals to use and edit as needed to better understand the trade-offs among PV module technologies and enable targeted research to improve module design and fabrication.

14 SOLAR ENERGY↗
DOE OSTI2022

Analysis of phospholipids and triacylglycerols in intravenous lipid emulsions

Intravenous lipid emulsions (ILEs) are used for parenteral nutrition, providing a vital source of essential fatty acids and concentrated energy for patients who are unable to absorb nutrients via the digestive track. They are commonly used to treat local and non-local anesthetic toxicity, and lipophilic drug overdose. ILE are composed of natural lipids, and the composition of these natural lipids can be varied based on their source. The lipids are susceptible to hydrolytic degradation with time, resulting various lipid degradation products such as Lysophosphatidylcholines (LPs), affecting the actual composition of nutrients in the formulation. As a result, the identification and quantification of lipid components, including degradation products, in ILEs are crucial in quality control. In this study, lipids from different batches of ILE Intralipid® 20%, were separated and identified using a UHPLC-ESI-QTOF system and SimLipid® high throughput lipid identification software. Out of 47 lipids identified, 34 were phospholipids (PLs) and the others were triacylglycerols (TAGs). Most of the phospholipids detected were phosphatidylcholines (PC) and Lysophosphatidylcholines (LPC). A total of 9 LPCs, 18 PCs, 6 phosphoethanolamines (PEs), and 1 sphingomyelin (SM) were identified. The LPCs concentration changed with the manufacturing date and storage time. Furthermore, this UHPLC method enabled the identification and quantification of lipids and their decomposition products in complex ILE emulsion mixtures on a single 20-minute chromatographic run.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Novel platinum group metal-free catalyst ink deposition system for combinatorial polymer electrolyte fuel cell performance evaluation

Here, a new system for rapid and precise fabrication of polymer electrolyte fuel cell (PEFC) membrane-electrode assemblies (MEAs) is introduced to facilitate rapid testing and characterization of platinum group metal-free (PGM-free) catalysts, with minimal waste of materials, using commercial combinatorial hardware and multi-channel potentiostat. The commercial combinatorial system, designed for high-throughput catalyst activity evaluation (i.e., low overpotential and current densities) using gas-diffusion electrodes (GDEs), has been further developed to enable simultaneous evaluation of the high current density performance on air of twenty-five cathodes prepared by coating catalyst-ionomer inks directly onto the membrane using a newly-developed deposition system. A nanopipette or micropipette coupled to a precise peristaltic pump is being used for precise and even catalyst ink deposition onto the membrane in each of twenty-five circular electrodes with areas of <1 cm 2 and in the precise locations required by the commercial combinatorial MEA hardware. The automated ink deposition system is coupled with a custom-designed heated vacuum table mounted on a computer-controlled XY stage to prevent deformation of the membrane and electrode layers during ink deposition onto the membrane. This system was utilized to evaluate the impact of ionomer to catalyst ratio on the hydrogen-air fuel cell performance of the PGM-free catalyst.

25 ENERGY STORAGE↗
DOE OSTI2020

Roll-to-roll production of catalyst coated membranes for low-temperature electrolyzers

Here we demonstrate a roll-to-roll (R2R) process for direct coating of anode catalyst layers on a polymer electrolyte membrane for low-temperature water electrolysis. To develop this process, we studied catalyst ink formulation, ink-membrane interactions, and coating quality. The catalyst inks were a mixture of iridium oxide (IrO2) and Nafion in a water and alcohol dispersion medium. The type of alcohol (methanol, ethanol, propanols) and water-to-alcohol ratio were varied to determine their influence on membrane swelling, dispersion quality, and coatability. Interactions of the ink dispersion medium with the membrane were characterized using sessile-drop contact-angle measurements. These measurements show that the ratio of water to alcohol has a strong influence on how rapidly the dispersion media is absorbed by the membrane. Rheology of the catalyst inks was measured to understand the microstructure of the catalyst particles in the ink. This analysis found that 1-propanol leads to better dispersion of the IrO2 particles than ethanol. Small-scale coating samples were prepared to understand coating uniformity and formation of irregularities. Subsequently, two water/1-propanol ratios (90:10 and 75:25) were down-selected for large-scale R2R slot die coating. The R2R catalyst-coated membrane (CCM) coating process increased catalyst layer production throughput by over 500x compared to our standard lab-scale spray coating. The CCMs obtained from this process were tested as single-cell membrane electrode assemblies. They exhibited a cell voltage of 1.91 V at a current density of 2 A/cm2, which is comparable to spray-coated CCMs. In conclusion, the work presented here demonstrates a continuous, scalable manufacturing process that eliminates the need for the decal transfer step typically used in CCM production.

42 ENGINEERING↗
DOE OSTI2021

Scaleup and manufacturability of symmetric-structured metal-supported solid oxide fuel cells

Metal-supported solid oxide fuel cells with symmetric architecture, having metal supports on both sides of the cell, are scaled up from button cell size to large 50 cm 2 active area cell size. The cells remain flat after sintering assisted by the symmetric structure. Equivalent performance is achieved for button cells and large cells, and thermal cycling and redox cycling tolerance are demonstrated for the large cells. The catalyst infiltration process is improved to enable high-throughput manufacturing. The cumbersome lab-scale molten nitrate infiltration process is replaced with a room-temperature process in which a shelf-stable aqueous solution of nitrate salts is applied to the cell by spraying, painting, or other scalable techniques. Here, a fast-ramp thermal conversion of the nitrate salts to the final oxide catalyst composition is implemented, allowing many infiltration cycles to be accomplished in a single work shift. Increasing the number of infiltration cycles from 5 to 10 led to an increase in peak power density from approximately 0.3 to 0.52 W cm -2 .

25 ENERGY STORAGE↗
DOE OSTI2024

Characterization of ultrafast-laser ablation of micro-structures in Li-ion battery anode and cathode materials: Morphology, rate, and efficiency

Characterization of the rate and quality of ultrafast-laser ablation of Li-ion battery (LIB) electrode materials is presented for a collection of common and next-generation electrodes. Laser ablated micro-structures on the surface of LIB electrodes have been shown to provide dramatic enhancement of high-rate capability and electrode wetting. However, industrial adoption is hampered by a lack of data enabling informed choice of laser parameters and predicting process throughput. Here this work bridges this gap by providing characterization of the ablation process at more laser parameters (laser fluence and number of pulses used) than are currently available in the literature. Further, we expand on previous graphite and lithium iron phosphate (LFP) ablation work by extending ablation characterization to new LIB materials, providing high data resolution and adopting new characterization metrics which are relevant for industrial application of this technology. Ablated pores are characterized by their ablated depth, volume, and how the depth and volume ablation rate changes as a function of pore depth. Finally, we provide a detailed characterization of the morphology of laser ablated micro-structures which informs how material and laser parameters affect the quality of laser-processed electrodes.

25 ENERGY STORAGE↗
DOE OSTI2024

Machine learning for the redox potential prediction of molecules in organic redox flow battery

Here, organic redox flow batteries (ORFB) are recognized as an innovative technology for the large-scale storage of renewable energy. The redox potential of organic redox-active molecules plays a vital role in their performance. Advanced screening techniques like high-throughput experiment and machine learning (ML) have significantly enhanced organic material performance and transformed the field of ORFB. However, the scarcity of experimental data poses a considerable challenge for ML model development in this domain. In our study, we developed lightweight graph-based Gaussian process regression (GPR) models with GPU-accelerated marginalized graph kernel and hybrid kernel to predict the redox potentials of organic redox-active molecules for ORFBs, specifically focusing on small datasets. To evaluate model accuracy, we created a new experimental database of organic redox-active molecules by the data from hundreds of published papers and assembled previous computational datasets. We also considered some key parameters, such as pH conditions and solvent type, to assess their impact on redox potential prediction. Our GPR model predicted redox potentials with high accuracy across all datasets using minimal training data. The study provides powerful tools for molecule screening and design and delivers valuable guidance on designing training datasets for costly experiments.

25 ENERGY STORAGE↗
DOE OSTI2020

An algorithm for resolving intragranular orientation fields using coupled far-field and near-field high energy $\mathrm{X}$-ray diffraction microscopy

Here, we present a novel algorithm for reconstructing spatial intragranular lattice orientation fields using both far-field and near-field high energy X-ray diffraction microscopy (HEDM) measurements. An established far-field indexing algorithm is modified to include lattice orientation distribution information (grain orientation envelopes) in addition to average grain orientations. The near-field data reduction algorithm utilizes this enriched far-field orientation data as a seed for reconstructing intragranular spatial maps of lattice orientation from the diffraction images. The primary benefit of the new algorithm is a significant decrease in the number of trial calculations that must be performed in the reconstruction process compared to existing methodologies while maintaining scalability. The resulting gains in efficiency facilitate the use of relatively modest computational resources and improve throughput at the point of measurement. We provide two example applications: volumetric orientation field reconstructions for a Ti-Al alloy both before and after the application of 3% uniaxial strain. The results showcase the efficiency of the new method and the ability to resolve subtle changes in microstructure, which are associated with incipient plastic deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated phase segmentation and quantification of high-resolution TEM image for alloy design

In the alloy design and development process, a wealth of atomically resolved structural high-resolution transmission electron microscopy (HRTEM) images are produced. Identifying the different nano-precipitate phases and tracking their evolution under various compositions and during manufacturing or post-processing requires hundreds of HRTEM images and thousands of precipitates. The nanoscopic phase information labeling and analysis purely relies on humans are prohibitively costly and time-consuming, sometimes not reliable because of the lack of authoritative knowledge. Here, in this work, we develop a novel unsupervised machine learning approach coupled with adaptive computer vision techniques with features in the Fourier space to automatically determine the number of phases and segment/quantify the phases with nanoscale resolution, allowing for quantitative correlation between nanostructure formation, processing and functional properties. To automate the phase extraction/quantification and ascertain its applicability, we have applied the developed framework to the HRTEM images from several alloy systems, processing conditions, image magnifications, and phase types and morphologies (precipitates, nano-twins, stacking faults, crystalline matrix, and amorphous structures) for verification. This study paves the road for compression, visualization, and translation of raw image structural data into physically relevant information in real-time with minimal human supervision. It shows the promise of enabling high-throughput materials characterization for the acceleration of alloy manufacturing and design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Process-structure-property considerations for wire-based directed energy deposition of Ti-6Al-4V

Directed energy deposition (DED) is an attractive additive manufacturing (AM) process for large structural components. The rapid solidification and layer-by-layer process associated with DED results in non-ideal microstructures, such as large grains with strong crystallographic textures. These non-ideal microstructures can lead to severe anisotropy in the mechanical properties. Despite these challenges, DED has been identified as a potential solution for the manufacturing of near net shape Ti-6Al-4V preforms, replacing lost casting and forging capabilities. Two popular wire-based directed energy deposition (W-DED) processes were considered for the manufacturing of Ti-6Al-4V with assessments on their respective metallurgical and mechanical properties, as compared to a conventionally processed material. The two W-DED processes explored were wire arc additive manufacturing (WAAM) and electron beam additive manufacturing (EBAM). High throughput inspection and tensile testing procedures were utilized to generate statistically relevant data sets related to each process and sample orientation. The 2 AM technologies produced material with remarkably different microstructures and mechanical properties. Importantly, results revealed key differences in strength and ductility for the two disparate processes which were found to be related to differences in the metallurgical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Microscopy modality transfer of steel microstructures: Inferring scanning electron micrographs from optical microscopy using generative AI

Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.

Computer vision↗
DOE OSTI2023

Multimodal and multiscale strengthening mechanisms in Al-Ni-Zr-Ti-Mn alloy processed by laser powder bed fusion additive manufacturing

The unique thermokinetics of laser-powder bed fusion additive manufacturing (L-PBFAM) has been exploited for development of a novel high-strength Al-Ni-Ti-Zr-Mn alloy. The addition of 0.5 wt% Mn leads to extraordinary improvement in ultimate tensile strength (502 MPa) and work hardening due to the activation of two Mn-induced strengthening mechanisms. First, by a bimodal particle strengthening effect due to Al 31 Mn 6 Ni 12 nano-quasi-crystal particles rejected in inter-dendritic spaces and fibrous Al 3 Ni eutectic dendritic channels, which predominately contributes to the strength improvement, and second by solid solution strengthening from remaining Mn entrapped in Al. These mechanisms supplement the particle strengthening effect imparted by coherent and incoherent Al 3 (Ti,Zr) co-precipitates present at melt pool boundaries, dislocation strengthening due to solidification induced strain, and Hall-Petch and backstress strengthening effect due to heterogenous grain size distribution occurring at various length scales. In conclusion, the solidification dynamics and hierarchical heat distribution that are associated with L-PBFAM resulted in complex spatial variations in these strengthening phenomena and were investigated via a high-throughput multiscale structure–property correlation that involved thermokinetic simulation, X-ray diffraction, high-resolution nanoindentation mapping, and site-specific transmission electron microscopy of the alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning

For decades, materials scientists have relied on the process-structure–property paradigm to guide investigations into material behaviors. Traditional studies often examine a limited number of process-structure–property variables, striving to elucidate mechanisms governing material response. However, this approach is time consuming and can limit exploration, as well as the discovery of process-structure–property relations in novel materials. In this paper, we combined combinatorial sputter deposition and multi-modal high-throughput materials characterization with feedforward neural networks to establish high-dimensional process-structure–property relations in Pt-Au alloys, yielding nanocrystalline alloys with high hardness and low resistivity relevant to electrical contact switch applications. We mapped three indicators of process conditions (composition and two atomic deposition characteristics) onto four indicators of material structure (X-ray diffraction, film thickness, density, and surface roughness) and two indicators of material properties (hardness and resistivity), resulting in 784 unique combinations evaluated over a 13-dimensional space. The neural networks predicted Pt-Au alloys with 18–24 at.% Au, when deposited at specific conditions, to have a nanoindentation hardness up to 7.2 GPa. This high hardness value, comparable to some steels, represents a 3-fold improvement in hardness over “hard gold”, a commonly used electrical contact alloy, while maintaining requisite electrical conductivity. The neural network models provide an avenue to identify expected process windows capable of maximizing material performance.

Electrical contact materials↗