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A Novel Additive Manufacturing Process Metric for Predicting Spatter-related Porosity in Laser Powder Bed Fusion

Components fabricated using the powder bed fusion laser beam metallic (PBF) additive manufacturing (AM) process comprise a multitude of sequential weld passes. Porosity defects resulting from weld pass inconsistencies are a concern for PBF materials and have a strong adverse effect on the mechanical properties. In an effort to understand and avoid spatter induced porosity, this work aims to provide a novel computational technique that can be used to quantify and predict the sequence-sensitive impact of spatter upon the PBF process stability and consequential porosity in as-printed material. A novel spatter impact AM model-based process metric (AM-PM) is introduced to model and quantify the impact that spatter has on the PBF process. The occurrence of spatter induced porosity is studied using thermal rise, lack of fusion, and spatter impact AM-PMs synchronized with porosity measured by high-resolution X-ray computed tomography. Six cylindrical specimens were designed to compare the AM-PM correlations with porosity across two different laser powers and three hatch scanning strategies. A point field based computational approach is shown to be effective for testing the influence of the different AM-PMs and interrogating the physical process conditions underlying the porosity formation. Further, characterization using optical metallography and scanning electron microscopy indicated that both keyhole and lack of fusion porosity correlated with the spatter impact AM-PM, which can be exploited to predict and control spatter related porosity by the hatch strategy. Efforts to predict porosity composition within PBF material should incorporate the PBF process disturbances that can result from spatter. The spatter impact AM-PM can be readily incorporated into the development of defect prediction models at the part scale.

Additive Manufacturing↗

DOE OSTI2019

Synthesis of operable process intensification systems: advances and challenges

Process intensification (PI) has been gaining increasing momentum in the chemical engineering research community and the chemical/energy industry. While many PI alternative technologies and their conventional counterparts exist, systematic approaches and tools to decide on the most promising intensified process solutions are currently rather lacking. Process Systems Engineering (PSE) can contribute uniquely to this challenge by leveraging process synthesis, design, analysis, and optimization tools. However, key open questions in this area still remain, including: (i) how to efficiently address the combinatorial design space and systematically deliver intensified designs, and (ii) how to ensure the operablility performances of the derived intensified structures at an early design stage. Here, this paper provides a brief review of recent progress towards the synthesis of operable process intensification systems, highlights major challenges, and discusses future research opportunities.

42 ENGINEERING↗

DOE OSTI2024

Tetrahydrofuran Processable Organic Solar Cells with 19.45% Efficiency Realized by Introducing High Molecular Dipole Unit Into the Terpolymer

Developing organic solar cells (OSCs) processable with halogen‐free, non‐aromatic solvents is crucial for practical applications, yet challenging due to the limited solubility of most photoactive materials. Here, this study introduces high‐performance terpolymers processable in tetrahydrofuran (THF) by incorporating dithienophthalimide (DPI) into the PM6 backbone. DPI extends the absorption band, lowers HOMO levels, and improves THF solubility and film crystallinity through its large dipole moment effect. Optimal PBD‐10:L8‐BO devices processed with THF achieved a competitive power conversion efficiency (PCE) of 18.79%, approaching chloroform‐processed devices (19.04%). By introducing PBTz‐F as a second donor, ternary OSCs reached an impressive 19.45% PCE when processed with THF. This improvement stems from enhanced photon generation, improved morphology, better charge transport, longer exciton lifetimes, efficient charge dissociation and collection, and suppressed recombination. These PCEs of 18.79% and 19.45% for binary and ternary blend OSCs, respectively, represent the highest reported efficiencies for OSCs processed with halogen‐free, non‐aromatic solvents. This work demonstrates significant progress in eco‐friendly OSC fabrication, paving the way for more sustainable and commercially viable organic photovoltaic technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Solution‐Processed Spin‐Polarized Light‐Emitting Diodes of Colloidal Quantum Wells and Magnetic Nanoparticles

Electrical injection of spin-polarized carriers into semiconductors enables circularly-polarized emission from spin-polarized light-emitting diodes (spin-LEDs). The incredible level of tunability of magnetic and electronic properties in colloidal nanocrystals offers unprecedented opportunities for the modulation of polarization of light in solution-processed spin-LEDs based on magnetic nanoparticles unlike epitaxially grown spin-LEDs restricted by a very limited range of materials for their exploitation, and solution-processed spin-LEDs based on chiral molecules, which do not allow the modulation of polarization in general. Here, it is shown that electrical injection of spin-polarized electrons from magnetic Fe 3 O 4 nanoparticles into CdSe/CdZnS core/shell colloidal quantum wells (CQWs) in solution-processed LEDs that allows for polarization modulation of electroluminescence. In this structure, a monolayer of face-down oriented CQWs is deposited as an active layer to avoid polarization losses due to the hopping of the electrons between the CQWs before the radiative recombination process. In this solution-processed spin-LED, the circular polarization reaches 4.5% at 3 K and survives up to 100 K. A net circular polarization is observed at zero magnetic field up to 100 K because of the remnant magnetization of the Fe 3 O 4 nanoparticles. This new colloidal spin-LED architecture presents significant prospects for future solution-processed advanced opto-spintronic devices.

circularly polarized electroluminescence↗

DOE OSTI2023

Information Processing Capacity of Spintronic Oscillator

Physical reservoir computing is a framework that enables energy‐efficient information processing by using physical systems. Nonlinear dynamics in physical systems provide a computational capability that is unique to reservoirs. It is, however, difficult to find an appropriate task for a reservoir because of the complexity of nonlinear information processing. The information processing capacity has recently been used to clarify systematically the tasks that are solved by reservoirs; it quantifies the memory capacity of reservoirs in accordance with the order of nonlinearity. Herein, an experimental evaluation of the information processing capacity of a spintronic oscillator consisting of nanostructured ferromagnets is reported. The spintronic reservoir state is electrically manipulated by adding a delayed‐feedback circuit. The total capacity reaches a maximum of 5.6 at the edge of the echo state property. A trade‐off between the linear and nonlinear components of the capacity is also found. The result can be used to better understand the nonlinear information processing in reservoirs and to find good matches between reservoirs and tasks. As an example, a function‐approximation task is performed and it is found that it can be efficiently solved when the reservoir state is appropriately tuned so that its information processing capacity matches that of the task.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Exposure assessment of process-related contaminants in food by biomarker monitoring

Exposure assessment is a fundamental part of the risk assessment paradigm, but can often present a number of challenges and uncertainties. This is especially the case for process contaminants formed during the processing, e.g. heating of food, since they are in part highly reactive and/or volatile, thus making exposure assessment by analysing contents in food unreliable. New approaches are therefore required to accurately assess consumer exposure and thus better inform the risk assessment. Such novel approaches may include the use of biomarkers, physiologically based kinetic (PBK) modelling-facilitated reverse dosimetry, and/or duplicate diet studies. This review focuses on the state of the art with respect to the use of biomarkers of exposure for the process contaminants acrylamide, 3-MCPD esters, glycidyl esters, furan and acrolein. From the overview presented, it becomes clear that the field of assessing human exposure to process-related contaminants in food by biomarker monitoring is promising and strongly developing. The current state of the art as well as the existing data gaps and challenges for the future were defined. They include (1) using PBK modelling and duplicate diet studies to establish, preferably in humans, correlations between external exposure and biomarkers; (2) elucidation of the possible endogenous formation of the process-related contaminants and the resulting biomarker levels; (3) the influence of inter-individual variations and how to include that in the biomarker-based exposure predictions; (4) the correction for confounding factors; (5) the value of the different biomarkers in relation to exposure scenario’s and risk assessment, and (6) the possibilities of novel methodologies. Here, in spite of these challenges it can be concluded that biomarker-based exposure assessment provides a unique opportunity to more accurately assess consumer exposure to process-related contaminants in food and thus to better inform risk assessment.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2023

Physics-informed machine-learning model of temperature evolution under solid phase processes

We model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermo-mechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting micro-structural scale mechanisms of heat generation to macro-scale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the micro-structural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental data-set. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Techno-economic and life cycle analyses for a supercritical biodiesel production process from waste cooking oil for a plant located in the Midwest United States

Existing literature lacks detailed techno-economic and environmental life cycle analyses (TEA-LCA) focused on supercritical biodiesel production pathways produced via waste products. Therefore, in this work, the TEA-LCA was conducted to generate supercritical biodiesel from waste cooking oil for plants located in the Midwest region of USA having annual production capacities of 10,600 and 128,000 t using Aspen Plus and GREET softwares. Here, two plant production capacities were chosen to capture the economies of scale impact on biodiesel manufacture cost. In the supercritical process, methanol and propane were used as a cosolvent to synthesize biodiesel from waste cooking oil (WCO) at 280 °C and 128 bar at a residence time of 8.4 min and a WCO conversion rate of 97%. Economic analysis revealed that the supercritical process was an economically attractive pathway with a 2-year payback period for the 10,600 t/year capacity along with a break-even selling price of $\$$ 2.42/gal of diesel. In case of the larger plant (128,000 t/year capacity), the payback period and the breakeven selling price were considerably lower at values of 0.4 years and $1.31/gal of diesel, respectively, due to economies of scale impact. The generated biodiesel from supercritical process for the both the plant capacities met European (EN14214) and US (ASTM D6751) fuel quality standards while the obtained commercially valuable side product, i.e., glycerol, adhered to a pharmaceutical grade of 99.7%. Cradle to gate life cycle analysis using GREET revealed that supercritical process possessed 17% lower CO2 emissions than alkali-catalyzed process and 4% lower CO2 emissions than the conventional diesel production process. Other biodiesel production pathways in the literature were also compared to the results of the TEA-LCA of supercritical biodiesel production pathway.

Biodiesel production↗

DOE OSTI2018

Statistical Analysis of the First Passage Path Ensemble of Jump Processes

The transition mechanism of jump processes between two different subsets in state space reveals important dynamical information of the processes and therefore has attracted considerable attention in the past years. In this paper, we study the first passage path ensemble of both discrete-time and continuous-time jump processes on a finite state space. The main approach is to divide each first passage path into nonreactive and reactive segments and to study them separately. The analysis can be applied to jump processes which are non-ergodic, as well as continuous-time jump processes where the waiting time distributions are non-exponential. In the particular case that the jump processes are both Markovian and ergodic, our analysis elucidates the relations between the study of the first passage paths and the study of the transition paths in transition path theory. We provide algorithms to numerically compute statistics of the first passage path ensemble. The computational complexity of these algorithms scales with the complexity of solving a linear system, for which efficient methods are available. Several examples demonstrate the wide applicability of the derived results across research areas.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Characteristics of Bi-metallic Interfaces Formed During Direct Energy Deposition Additive Manufacturing Processing

Various additive manufacturing processes are being evaluated to reduce the time and cost for fabrication of low volume, complex, and multifunctional assemblies. This study evaluated two direct energy deposition processes for the fabrication of large bi-metallic structures. The materials evaluated were Inconel 625 and copper alloy C18150, which are used in various high heat flux applications. Inconel was deposited onto the C18150 substrate using blown powder and wire-fed processes. Complete bonding was obtained in both processes and the resulting interfaces were evaluated using microscopy and indentation testing. Differences were observed in the interface region suggesting the kinetic energy of the blown powder process resulted in more residual stress at the interface, promoting recrystallization and enhanced diffusion. This created a broader interface in the blown powder specimens compared to a narrower mechanically mixed interface with the wire-fed process.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A multi-stage stochastic programming model for adaptive biomass processing operation under uncertainty

Variations of physical and chemical characteristics of biomass reduce equipment utilization and increase operational costs of biomass processing. Biomass processing facilities use sensors to monitor the changes in biomass characteristics. Integrating sensory data into the operational decisions in biomass processing will increase its flexibility to the changing biomass conditions. In this paper, we propose a multi-stage stochastic programming model that minimizes the expected operational costs by identifying the initial inventory level and creating an operational decision policy for equipment speed settings. These policies take the sensory information data and the current biomass inventory level as inputs to dynamically adjust inventory levels and equipment settings according to the changes in the biomass' characteristics. We ensure that a prescribed target reactor utilization is consistently achieved by penalizing the violation of the target reactor feeding rate. A case study is developed using real-world data collected at Idaho National Laboratory's biomass processing facility. We show the value of multi-stage stochastic programming from an extensive computational experiment. Our sensitivity analysis indicates that updating the infeed rate of the system, the processing speed of equipment, and bale sequencing based on the moisture level of biomass improves the processing rate of the reactor and reduces operating costs.

09 BIOMASS FUELS↗

DOE OSTI2019

High-Resolution Mass Spectrometry Quantification: Impact of Differences in Data Processing of Centroid and Continuum Data

High-resolution mass spectrometry (HRMS) in full scan mode acquires all ions present in the sample of interest offering a lot of qualitative information. This, in combination with the improved performance of the new generation HRMS systems, triggers more (bio) analysts to switch from triple quad MS systems to HRMS for quantitative analysis. Quantitative processing of HRMS data is performed based on narrow mass extraction windows rather than on nominal mass product ion chromatograms (SRM or MRM). Optimal processing of HRMS data requires different considerations and software tools and can have an impact on data processing and final results. The selection of centroid versus continuum/profile data for processing, selection of the optimal narrow mass extraction window, using theoretical versus measured accurate mass for the extraction of the ion chromatograms as well as differences in calculations and data handling residing in the different vendor software packages are tackled in the presented manuscript. These differences are illustrated on HRMS data acquired for the same plasma samples on three different platforms, i.e., a Sciex QToF, a Waters QToF, and a Thermo Orbitrap system, and processed in four different software packages, i.e., Sciex Analyst® TF, Waters Masslynx, Waters Unifi, and Thermo Xcalibur. The impact of these differences on quantitative HRMS performance was evaluated on calibration curves of eight small molecule compounds in plasma using four different ways of processing. Simple guidelines are provided for the selection of an optimal mass extraction window for continuum and centroided data. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing

Abstract We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we attempt to reprocess the data by running several thousand concurrent grid jobs, a rate we expect to be typical of current and future neutrino physics experiments. We process most of the dataset with the GPU version of our processing algorithm and the remainder with the CPU version for timing comparisons. We find that a 100-GPU cloud-based server is able to easily meet the processing demand, and that using the GPU version of the event processing algorithm is two times faster than processing these data with the CPU version when comparing to the newest CPUs in our sample. The amount of data transferred to the inference server during the GPU runs can overwhelm even the highest-bandwidth network switches, however, unless care is taken to observe network facility limits or otherwise distribute the jobs to multiple sites. We discuss the lessons learned from this processing campaign and several avenues for future improvements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Modeling powder spreadability in powder-based processes using the discrete element method

Powder-bed fusion (PBF) processes refer to a subset of Additive Manufacturing (AM) techniques where powder is spread on the build-plate before melting (by a laser or electron beam). While PBF processes are attractive due to their ability for realizing complex structures that are either difficult or impossible to create through conventional means, the parts fabricated with these techniques can exhibit defects such as pores, inclusions, and excessive surface roughness. To minimize these defects, much research has been dedicated towards process maturation by optimizing laser or electron beam parameters. However, these developmental efforts typically do not address the recoating process where achieving dense and uniform layers of powder is a necessity for ensuring process repeatability and part quality. While the recoating process can be studied through experimentation, the dynamics of particle movement are difficult to analyze experimentally. Therefore, here, in this study, powder spreading in PBF was simulated through the Discrete Element Method (DEM) to elucidate the mechanisms that control powder-bed quality. Utilizing the Buckingham Pi theorem, a dimensionless metric referred to as the spreading index is developed that combines powder-bed density, roughness, and particle size to assess the quality of powder layers. The formulated spreading index is then related to several dimensionless quantities that provide insight into the mechanisms dominating powder spreading in PBF. The DEM simulations conducted in this work focused on the scenario where powder is spread onto an existing powder bed and revealed that a reduction in the recoating velocity causes an increase in the spreading index while little to no impact on the spreading index was observed when varying layer thickness from 30 μm to 75 μm.Particle size effects on the powder-bed quality were also investigated.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Process design and economics for production of advanced biofuels from genetically modified lipid-producing sorghum

This study evaluates the potential for making advanced biofuels from genetically modified (GM) lipid–producing sorghum. A biodiesel coproduction process is developed to extract, purify, and upgrade lipids to diesel fuel while carbohydrates are utilized for making ethanol through acid thermal pretreatment, enzymatic hydrolysis, and fermentation. To assess the advantages of coproducing biodiesel from GM–sorghum, process economics are compared to a cellulosic ethanol biorefinery receiving non-GM sorghum. Minimum ethanol selling prices (MESP) that reach a breakeven point after 30 years of service life are calculated as an economic index to compare the two processes. Results indicate that biodiesel coproduction improves the economics by lowering the MESP from 3.08/gal for the ethanol-only process to 2.46/gal. Sensitivity analyses reveal that increasing sorghum’s lipid content, increasing the lipid extraction efficiency, and reducing the solvent-to-solids ratio in lipid extraction columns are the most important process parameters to further enhance technoeconomics. Analyses indicate that a lipid content above 13 wt% (dry basis) or a biomass price less than 65/Mg (dry basis) will result in a 2014 ethanol wholesale price of 2.25/gal for the coproduction process.

09 BIOMASS FUELS↗

DOE OSTI2022

Na + -gated nanochannel membrane for highly selective ammonia (NH 3 ) separation in the Haber-Bosch process

Currently, cryogenic condensation is the predominant process for recovering ammonia (NH 3 ) in the Haber-Bosch (HB) process, which is highly energy intensive. To be more compatible with the reaction conditions in the HB process and thus minimize the pressure and temperature swing during reactant recycling, energy-efficient technologies for NH 3 extraction at elevated temperature and pressure are greatly needed. In this work, the Na + -gated nanochannel membrane, shown exclusively for water conduction in our previous work, also exhibited highly NH 3 -selective performance, with NH 3 /H 2 selectivity as high as 4,280 and NH 3 /N 2 selectivity > 10,000 at temperature up to 250 °C and pressure up to 35 bar. Excellent stability of the Na + -gated nanochannel membrane was demonstrated during a 100-h run in ternary NH 3 /H 2 /N 2 gas mixture at 200 °C and 35 bar, consistent with structural characterization by X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and Fourier transfer infrared (FTIR) spectroscopy. A techno-economic analysis (TEA) was conducted for the HB process using the Na + -gated nanochannel membrane and the traditional HB process with condenser. Finally, under the optimized separation conditions, > 80% energy savings and approximately 20% reduction of the net NH 3 production cost can be achieved, demonstrating the great potential of the Na + -gated nanochannel membrane for NH 3 separation in the HB process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Screening green solvents for multilayer plastic film recycling processes

Multilayer (ML) plastic films are essential packaging materials that help protect products from diverse external factors; however, only 5% of all ML films are recycled in the United States. Solvent-based technologies are a promising alternative for recycling ML films because they enable recovery of constituent polymer resins. For example, the Solvent Targeted Recovery and Precipitation (STRAPTM) process sequentially dissolves and separates polymer components using a series of targeted solvent washes. A crucial design aspect of this process is the impact of selected solvents on human health and on the environment. Here, this work introduces a computational framework that integrates molecular modeling, process modeling, techno-economic analysis (TEA), and life-cycle analysis (LCA) to quickly screen green solvents for solvent-based ML recycling processes. Initial screening for solvents based on selectivity is performed by estimating temperature-dependent solubilities using molecular-scale models. Subsequent screening uses basic estimates of energy use and octanol-water partition coefficients (logP) as key measures of health, safety, and environmental hazards. Detailed process modeling, TEA, and LCA are used on a reduced set of promising solvents identified in early screening steps to more accurately determine how solvent selection and associated operating conditions impact overall economics and environmental impacts. The framework is used for the identification of green solvents (from a database of 1,000 solvents) that separate an industrial ML film composed of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET). Our analysis shows the effectiveness of the framework and reveals fundamental trade-offs between solvent greenness, solubility, and economics. Our work emphasizes the importance of taking a holistic systems view during solvent design and aims to inform the development of new processes for ML film recycling and the identification of new ML films that are easier to recycle.

economics↗

DOE OSTI2021

Enabling aqueous processing for LiNi 0.80 Co 0.15 Al 0.05 O 2 (NCA)-based lithium-ion battery cathodes using polyacrylic acid

Replacing N-methyl-2-pyrrolidone (NMP) with water in the production of lithium-ion battery cathodes is critical to realizing process cost savings and improved worker safety. LiNi 0.80 Co 0.15 Al 0.05 O 2 (NCA) is a poor fit for aqueous processing due to destructive Al current collector corrosion resulting from highly basic slurries and detrimental surface reconstruction reactions that occur in water. In this study, polyacrylic acid (PAA, MW = 450,000 g·mol -1 ) is examined as a corrosion-mitigating and surface-stabilizing agent. Adding PAA to an aqueous NCA slurry can provide a stable pH (4.0–8.5) for at least 4 h, since the carboxyl groups from PAA dissociate and increase the proton concentration in the slurry. Further, these groups can also adsorb to the surface of NCA particles and provide electrostatic stability from active material particle agglomeration, as revealed by zeta potential measurements. Minor cracking does occur at the electrode surface; this cracking likely caused poorer adhesion to the Al current collector in the aqueous-processed film when compared to the NMP-processed baseline. Electrochemically, the leached Li does cause a lower initial capacity for the aqueous-processed cathode, though the capacity retention of the aqueous-processed cathode is better than the baseline. The cracks in the coating led to a rise in charge transfer resistance that hindered rate capability above 1C.

25 ENERGY STORAGE↗