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DOE OSTI2026

Technology pathways for energy- and water-efficient controlled environment agriculture: A review of technologies, implementation pathways, and regional use cases

Controlled Environment Agriculture (CEA) offers high-yield, climate-resilient food production, but high energy and resource demands challenge its sustainability. This paper synthesizes technologies that can improve outcomes across six categories—energy, CO 2 utilization, building envelope, hardware, water, and process—plus colocation strategies. We evaluate 80 technologies and define ten implementation pathways bundling complementary technologies to reduce energy use, optimize water consumption, and minimize emissions. Regional application is demonstrated through five U.S. case studies spanning different climates. A logic framework guides pathway selection for case studies based on climate, infrastructure, and regulatory context, informing context-sensitive technology deployment. Results show energy intensity reductions of 3–55 %, ranging from energy management programs to comprehensive lighting retrofits; water savings of 20–40 % through closed-loop recirculation; and emissions reductions of 3–100 %, with strategic energy management achieving 3–5 % and renewable electricity paired with electrified heating achieving up to 100 %. Text mining revealed that energy, hardware, and process technologies account for 91 % of literature coverage. Water, building envelope, and CO 2 utilization remain underexplored, indicating priorities for future research. This integrative approach to technology assessment supports growers, developers, and policymakers in aligning CEA system design with local conditions, improving resource efficiency and addressing gaps in cross-domain technology coverage.

Controlled environment agriculture↗

DOE OSTI2021

Bacterial xylan utilization regulons: systems for coupling depolymerization of methylglucuronoxylans with assimilation and metabolism

Abstract Bioconversion of lignocellulosic resources offers an economically promising path to renewable energy. Technological challenges to achieving bioconversion include the development of cost-effective processes that render the cellulose and hemicellulose components of these resources to fermentable hexoses and pentoses. Natural bioprocessing of the hemicellulose fraction of lignocellulosic biomass requires depolymerization of methylglucuronoxylans. This requires secretion of endoxylanases that release xylooligosaccharides and aldouronates. Physiological, biochemical, and genetic studies with selected bacteria support a process in which a cell-anchored multimodular GH10 endoxylanase catalyzes release of the hydrolysis products, aldotetrauronate, xylotriose, and xylobiose, which are directly assimilated and metabolized. Gene clusters encoding intracellular enzymes, including α-glucuronidase, endoxylanase, β-xylosidase, ABC transporter proteins, and transcriptional regulators, are coordinately responsive to substrate induction or repression. The rapid rates of glucuronoxylan utilization and microbial growth, along with the absence of detectable products of depolymerization in the medium, indicate that assimilation and depolymerization are coupled processes. Genomic comparisons provide evidence that such systems occur in xylanolytic species in several genera, including Clostridium, Geobacillus, Paenibacillus, and Thermotoga. These systems offer promise, either in their native configurations or through gene transfer to other organisms, to develop biocatalysts for efficient production of fuels and chemicals from the hemicellulose fractions of lignocellulosic resources.

Biotechnology & Applied Microbiology↗

DOE OSTI2023

Incentivizing Cold-Climate Efficiency in Juneau (Final Report)

This is the final technical report for the DOE EERE BTO project Incentivizing Cold-Climate Efficiency in Juneau. The project implemented a community energy campaign to deploy heat pumps and efficiency for residents of Juneau, in Southeast Alaska. The project helped Juneau make progress towards its renewable energy goal of reaching 80% renewable energy for space heating by 2045. The Incentivizing Cold-Climate Efficiency in Juneau (ICE-Juneau) project began in 2020 and concluded three years later in 2023. During that time, a group of implementation partners and a research advisory team instituted a beneficial electrification campaign to promote energy savings and carbon reduction in residences of Juneau, Alaska. The campaign, Thermalize Juneau, focused on the installation of single head ductless mini-split heat pumps along with other efficiency upgrades. Thermalize Juneau was the first campaign of its kind in Alaska. After several months of planning, registration opened to the public in early 2021, and over the course of six months 164 participants enrolled. Campaign staff provided education to homeowners on heat pumps and efficiency, and each participant received a one-on-one heat pump assessment using a custom Microsoft Excel-based calculator that estimated energy savings for their residence based on building characteristics and past utility bills. Participants could also obtain a free energy audit from one of the two local energy auditors to further inform their decision. A heat pump installer, electrician, and builder were selected via a competitive RFP process. Participants who felt ready to install a heat pump or other efficiency upgrades received a site visit and custom quote from each of these contractors free of charge, and if they still felt energy upgrades were right for them, could move forward with an individual contract. Participants received a $400 heat pump installation rebate, offered by the installer if 40 heat pump installations occurred through the campaign. Overall, the campaign facilitated 75 heat pump installations (including participants that went with another contractor or heat pump model) and 30 efficiency upgrades (including participants that went with another builder or did DIY upgrades). The campaign created 3 new jobs as the heat pump installer hired an administrative assistant and two apprentices over the course of the campaign. It also helped Juneau work toward achieving its renewable energy goal of 80% renewable energy for space heating by 2045 by upgrading houses from fuel oil to heat pumps powered by the hydropower electric grid. Researchers conducted four surveys to inform Thermalize Juneau and future energy campaigns. The first surveyed existing and prospective heat pump owners in Juneau to identify barriers the campaign could address and inform recruitment efforts. Entry and exit surveys provided information on participant demographics, goals, outcomes, and suggestions for improving future campaigns. And a final survey of community members who had not participated in Thermalize Juneau gave insight on ways future campaigns could include a greater diversity of participants so they could realize similar benefits. Researchers also used pre-campaign energy modeling to predict energy savings, which was then compared to the savings estimated through aggregation of heat pump assessments and energy audits, and later to actual energy savings of 10 participants who installed a heat pump and were able to provide complete energy use data sets. In addition to energy savings, and to assist the electric utility in future planning efforts, researchers analyzed the overall change in electric use across participants with energy data and heat pump installations. They also completed a life cycle cost analysis, showing positive net present values for those displacing a fuel oil appliance, and a more mixed case for those switching from electric baseboard. This project proved the feasibility of energy campaigns, with a goal of beneficial electrification, in cold, remote locations. The Thermalize Juneau team compiled a Guidebook to Thermalize Campaigns, available online. In addition to documenting what occurred in Alaska’s first thermalize campaign, it provides tips and resources for other communities wishing to implement a similar program.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Multi-Sourced Collaboration for the Production and Refining of Rare Elements and Critical Metals (Final Technical Report)

The project objective was to develop a feasible and cost-effective method for recovering rare earth elements (REEs) and critical materials (CMs) from coal and coal byproducts, resulting in high-purity individually separated REEs and CMs. The targeted REEs included Y, Pr, Nd, Gd, Dy, and Sm, with a purity of over 99.5%, while the CMs included Co, Mn, Ga, Sr, Li, Ni, Zn, and Ge, with a purity of over 90%. The project aimed to design a prototype facility capable of producing 1-3 tonnes/day of high-purity REO mixes. The work was divided into four designated circuits: 1) REE extraction and concentration, 2) REE separation and purification, 3) RE metal production, and 4) CM production. To achieve these goals, the project involved 11 tasks, including technology reviews, research, process flow diagram development, mass balance estimation, and preliminary technical-economic analysis. The project team included researchers from the University of Kentucky, University of Alabama and Virginia Tech as well as process specialists from Argonne National Laboratory. MP Materials provided technical support regarding rare earth markets and processing while Alliance Coal performed resource assessment. The project included a market analysis for Nd/Pr, Tb, Dy, Gd, Y, Co, Mn, Li, Sr, Ga, Ni, Zn, and Ge. These analyses provided insights into the supply and demand trends as well as historic and future projections of market price relative to purity requirements for these elements. Two coal resources were selected for the project: the West Kentucky No. 13 (Baker) Seam and an undisclosed lignite resource in the Illinois coal basin. The estimated quantities of REEs in these resources were calculated based on production samples and drilling data. It was estimated that there is adequate supply for an operation producing one metric ton daily of higher purity mixed rare earth oxides (MREO) for approximately 20 years at a site located in western Kentucky. In Circuit 1, project data was obtained from a pilot heap leach and REE concentration facility. It was concluded that the existing circuit, which generated a MREO concentrate, two types of CM mixed products, and Li- and Sr-containing waters, would be suitable feed for circuits 2-4. Data from the first-of-its-kind coal coarse refuse heap leach pilot pad played a crucial role in estimating reliable elemental concentrations of the pregnant leaching solution (PLS). The average total REE concentration in the PLS was found to be 28.6 ppm. In Circuit 2, several concepts were explored including a novel process referred to as solvent-assisted chromatography (SAC). This concept involved a novel columnar reactor that incorporated multiple mixer/settlers, thereby enabling the operation of counter-flowing aqueous and organic phases. Unfortunately, due to project time constraints, a complete fundamental modeling analysis could not be completed to fully evaluate the technology. Molten salt electrowinning was considered as an alternative for circuit 3 following circuit 2 purification circuit utilizing the novel SAC process. A mass and energy balance of Nd reduction to metal in a fluoride containing molten salt electrolyte was conducted. Comparisons were made with the current state of Asian molten salt electrorefining, and potential improvements in siphoning rare earth metals (REM) from the reactor were presented. A cost estimate was performed for the production of 1 tonne per day, which yielded a total of $2.29 million for the nine electrowinning (EW) cells required. The selected option for circuits 2 and 3 was a plasma distillation process, which initially separates rare earth elements (REEs) from other elements. This is followed by selective electrowinning in various ionic liquids. The selection was made on the basis of thermodynamic modeling and experimental data previously published by a project partner. The combination offers an innovative approach to integrated refining and RE metal production. For Circuit 4, an extensive literature review was conducted for the processing of the CMs. The ultimate decision was to utilize a combined plasma and ionic liquid process as well to produce individual high-purity concentrates of Zn, Ni, Co, Mn, and Mg. A separate flowsheet for Li and Sr was recommended, which would yield carbonates of these elements. Due to the lack of suitable experimental data at this time, a process recommendation could not be provided but several methods have been proposed for consideration. Lastly, a techno-economic analysis (TEA) was conducted to assess the effectiveness of the proposed process for further investigation. The TEA results revealed a capital expense (CapEx) of $737 million and an annual operational expense (OpEx) of $220 million. Due to the selected elements, the hypothetical heap leach pad can produce 1 metric tonne per day of REO equivalent, but a conscious decision was made to only treat targeted REEs, resulting in the production of 0.4 metric tonne of REM. An estimated annual revenue of $90.87 million was projected based on standard market pricing information provided by the funding agency. During the TEA, ten different modules were evaluated for costing purposes. The precipitation circuit was identified as the largest single operational expense, followed by the Mg/Mn process due to the amount of treated metal. In terms of capital expenditures, the heap leach process incurred the highest cost, followed by the Mg/Mn process. The scalability of the plasma process is a crucial consideration since the reactors cannot be scaled beyond the largest demonstrated size due to their reliance on surface area of the slag and vapor phase. The purity estimate for the REEs are generally 98%±2% to produce a metal. The purity level being lower than the project objective was due to the lack of specific experimental data needed to tighten the tolerance of the estimates. Based on literature and previous experience, the CMs are estimated as follows; Ga (95%+, metal), Sr (95%+, carbonate), Li (95%+, carbonate), Ni (98%±2%, metal), Zn (95%+, metal sponge), Ge (95%+, metal), Co (98%±2%, metal), and Mn (98%±2%, metal).

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation

Proximal remote sensing has the potential to provide critical information on vegetation biophysical factors that can predict land-atmosphere exchange of water and energy. Latent energy (LE) flux is traditionally estimated using process-based models which rely on vegetation parameters that change during the growing season. Data-driven models have the potential to address these issues by offering flexible predictor selection and more efficient utilization of the information in predictor sets. These models require careful choice of predictors to avoid redundancy and allow robust cross-validation. In this study we present a systematic and comprehensive evaluation of machine learning (ML) models to assess the capability of meteorological and proximal sensing data for predicting LE at a half-hourly temporal resolution across multiple growing seasons for an agricultural system. The results presented here demonstrate that a model using four environmental predictors in combination with two proximal sensing variables can capture 88 % of the variability in LE. ML models using only three predictors (one meteorological and two proximal remote sensing) captured 81 % of LE variability, offering the best trade-off between performance and complexity. An ML model utilizing only two predictors, one proximal remote sensing variable and downwelling radiation, captured 77 % of LE variability. These results demonstrate the power of proximal remote sensing and meteorological observations to estimate land-atmosphere water vapor exchange, providing a solution where more direct methods such as eddy covariance are not available and for evaluations of agronomic management and genotypic variations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

One-Step Solution Patterning for Two-Dimensional Perovskite Nanoplate Arrays

Two-dimensional perovskite crystals have attracted significant attention for their diverse optoelectronic characteristics, owing to their superior semiconducting properties. However, the majority of studies to date have focused on single crystals, which pose challenges for integration into device arrays due to their incompatibility with selective growth or conventional lithography techniques. Here, a facile one-step solution process for synthesizing 2D perovskite crystal arrays is proposed through meniscus-guided coating on patterned substrates. Here we further utilized this method for the synthesis of lateral heterostructure nanoplate arrays. Six different 2D perovskite nanoplate arrays, including epitaxial heterostructures, are successfully realized. Optical and crystallographic characterizations show the high optical performance and crystallinity of the nanoplates. Moreover, this method is further employed to prepare high-performance 2D perovskite nanoplate photosensor arrays. This strategy can be utilized as a guideline for the fundamental investigation of optical properties and the development of high-performance optoelectronics of perovskite materials including photosensors and displays.

2D perovskite nanoplates↗

DOE OSTI2024

Non-averaged single-molecule tertiary structures reveal RNA self-folding through individual-particle cryo-electron tomography

Large-scale and continuous conformational changes in the RNA self-folding process present significant challenges for structural studies, often requiring trade-offs between resolution and observational scope. Here, we utilize individual-particle cryo-electron tomography (IPET) to examine the post-transcriptional self-folding process of designed RNA origami 6-helix bundle with a clasp helix (6HBC). By avoiding selection, classification, averaging, or chemical fixation and optimizing cryo-ET data acquisition parameters, we reconstruct 120 three-dimensional (3D) density maps from 120 individual particles at an electron dose of no more than 168 e - Å -2 , achieving averaged resolutions ranging from 23 to 35 Å, as estimated by Fourier shell correlation (FSC) at 0.5. Each map allows us to identify distinct RNA helices and determine a unique tertiary structure. Statistical analysis of these 120 structures confirms two reported conformations and reveals a range of kinetically trapped, intermediate, and highly compacted states, demonstrating a maturation folding landscape likely driven by helix-helix compaction interactions.

36 MATERIALS SCIENCE↗

DOE OSTI2024

MOF compositions for selective separation of hydrocarbons

The present disclosure relates to novel metal-organic frameworks (MOFs) comprising tetratopic ligands with small pore apertures. The present disclosure further relates to methods of utilizing the MOFs of the disclosure to separate hydrocarbons through adsorptive processes.

Li, Jing↗

DOE OSTI2025

A Synthetic Pathway for Producing Carbon Dots for Detecting Iron Ions Using a Fiber Optic Spectrometer

Iron detection is of growing importance in the critical minerals sector, where unwanted iron ions are typically removed during the processing of target critical metals. The ideal sensor should utilize inexpensive, scalable materials along with a low-cost, robust, and easy-to-use analysis platform. Here, we demonstrate a simple acid–base synthesis of luminescent iron-responsive carbon dots by reacting ethanolamine, phosphoric acid, and m-phenylenediamine. The carbon dots exhibit selective, iron-specific emission quenching, with the ability to detect part-per-billion levels of iron ions even in 0.1 M HCl. After benchmarking the purified materials using a commercial spectrometer, a “low-cost” process is demonstrated in which carbon dots with minimal purification are coupled with a portable fiber-optic spectrometer for analyzing iron content. Carbon dot-coated paper strips are also evaluated as another convenient platform for iron analysis. Taken together, the sensing material and platforms demonstrated here are well-suited for detecting trace quantities of iron in environmentally relevant conditions, with potential applications in tracking iron removal processes during critical mineral production as one exciting area of interest.

acid mine draining↗

DOE OSTI2022

Data for Catalyst and Reactor Design Considerations for Selective Production of Acids by Oxidative Cleavage of Alkenes with H2O2

Oxidative cleavage of alkenes and unsaturated fatty acids with hydrogen peroxide gives an efficient and sustainable process to obtain mono- and di-acids for polymers and lubricants with fewer safety risks and less environmental impact than processes that utilize ozone or other inorganic oxidizers (e.g., permanganate, dichromate, etc.). Guided by insight into the mechanisms for competing reaction pathways (i.e., epoxidation of alkene on W–(η2-O2) complexes vs. H2O2 decomposition) and the apparent kinetics derived from kinetic experiments, here, we postulate that W-based heterogeneous catalysts can provide high performance and stable operations at low H2O2 concentrations. Semi-batch reactors with continuous introduction of H2O2 solutions offer the means to maintain low H2O2 concentrations while providing sufficient quantities of H2O2 to satisfy the reaction stoichiometry. We derived simple kinetic model equations for the epoxidation, ring-opening, oxidative cleavage, and oxidation steps and fit theses equations to batch experimental data to obtain kinetic parameters. This kinetic model describes the concentration profiles of reactant, oxidant, and products well as shown by agreement with experimental data. Further predictions of the optimal H2O2 feed rate for semi-batch operation utilized by the proposed rate expressions and the reactor design equations suggest that low H2O2 feed rate increases selectivity towards oxidative cleavage products and selective use of H2O2 for oxidative cleavage pathway. Comparisons of oxidative cleavage of 4-octene in batch and semi-batch reactors show that semi-batch reactors with optimized molar feed rates of H2O2 increased oxidative cleavage product selectivities (76% to 99%; with an increase in butyric acid selectivity from 1% to 55%) and H2O2 selectivity (3% to 30%). In addition, semi-batch reaction conditions used avoid H2O2-mediated dissolution of W-atoms from the catalyst. Analysis of these findings suggest that solid oxide catalysts will be effective for continuous oxidative cleavage reactions if deployed within fixed-bed reactors that allow for distributed introduction of reactants and therefore low in situ concentrations of H2O2.

Catalysis↗

DOE OSTI2024

Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-Based Battery Enclosures for Crashworthiness

Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that should be carefully chosen. Here, in this work, we implemented high throughput finite element analysis (FEA) based thermoforming simulation to virtually manufacture the battery enclosure using different design and processing parameters. Subsequently, we performed virtual crash simulations to mimic a side pole crash to evaluate the crashworthiness of the battery enclosures. This high throughput crash simulation dataset was utilized to build predictive models to understand the crashworthiness of an unknown set. Our machine learning (ML) models showed excellent performance (R 2 > 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Development of Remote Cutting Tools for use at ATR - 20438

As a result of years of operations of the Advanced Test Reactor (ATR) at the Idaho National Lab (INL), the ATR canal has become congested due to the storage of materials including waste consisting of highly irradiated remote handled (RH) metals. In order to support its continuing nuclear mission the ATR requires canal space to support fuel storage and future core internal change out (CIC) components. INL staff determined that the canal must be cleaned out and waste removed to efficiently utilize the valuable canal floor space. It was determined that a methodology for processing, packaging, characterizing, and removing current and future irradiated hardware from the ATR canal is needed. Orano Federal Services (OFS) and its subcontractor Babcock Services Inc. (BSI) were selected to provide remote tooling in order to safely and efficiently size reduce the material currently stored in the ATR canal. INL developed detailed requirements that established the design and performance requirements for the tooling. Orano TN and BSI had recently successfully completed similar tasks in clearing highly activated materials from spent fuel pools and canals at commercial nuclear plants. That experience directly translated into a proven approach that could be used at the ATR thereby saving time and money and lowering risks. Based on BSI's demonstrated proficiency in the design and deployment of remote size reduction specialty tooling at comparable commercial facilities, Orano Federal Services was confident that a solution to INL's requirements could be delivered. The tooling had to be designed to be compatible with the operational requirements of the ATR canal as well as the functionality needed by the facility operators that will use the equipment. Some of the major design requirements and functions of the equipment included remote operations, capability to shear highly irradiated sections of aluminum and steel tubing, pipe, and components ranging in size from 0.6 cm to 16.5 cm diameter into 1.5 m -1.8 m lengths, utilizing buoyancy compensation to assist with underwater operations, and use commercially available existing technology and components as much as practical. The design team evaluated and selected commercially available equipment that could be modified to meet the design and performance specifications. In addition, a waste sizing table (WST) was designed and fabricated to further ensure the size reduction equipment would perform as expected and give the facility operators a stable and usable remote, underwater work platform. After successful development of the tooling, a mock up test and operator training was conducted to ensure the tooling performed as expected and met the requirements of INL. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Glass Design Using Machine Learning Property Models with Prediction Uncertainties: Nuclear Waste Glass Formulation

The United States Department of Energy is responsible for managing the legacy nuclear waste stored in underground tanks at the Hanford Site. The waste will be separately vitrified as low-activity waste and high-level waste fractions. Waste glass formulation algorithms have been traditionally developed using partial quadratic mixture property-composition models. Recently, machine learning (ML) techniques have been used to predict glass properties and discover new glass materials for nuclear waste vitrification, and these advancements can be utilized to improve waste glass composition design. In this proof-of-principle study, ML algorithms such as Gaussian process regression (GPR) were used to interpolate glass properties (e.g., viscosity, electrical conductivity, chemical durability). After selecting appropriate sets of GPR hyper-parameters for each property, an optimization program was developed to formulate glass compositions to maximize waste loading while simultaneously satisfying property within constraints. The results of the ML-based waste loadings and glass compositions were compared to those obtained using the traditional methods. Comparing to the previous glass design framework, the ML-based optimization methods offer improved glass designs and a streamlined approach to generation of optimally designed data and near real-time updates.

glass formulation, machine learning, constraints, ↗

DOE OSTI2021

Reinforcement Learning Configuration Interaction

Selected configuration interaction (sCI) methods exploit the sparsity of the full configuration interaction (FCI) wave function, yielding significant computational savings and wave function compression without sacrificing the accuracy. Despite recent advances in sCI methods, the selection of important determinants remains an open problem. Furthermore, we explore the possibility of utilizing reinforcement learning approaches to solve the sCI problem. By mapping the configuration interaction problem onto a sequential decision-making process, the agent learns on-the-fly which determinants to include and which to ignore, yielding a compressed wave function at near-FCI accuracy. This method, which we call reinforcement-learned configuration interaction, adds another weapon to the sCI arsenal and highlights how reinforcement learning approaches can potentially help solve challenging problems in electronic structure theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Integration of phage and yeast display platforms: A reliable and cost effective approach for binning of peptides as displayed on-phage

Hundreds of target specific peptides are routinely discovered by peptide display platforms. However, due to the high cost of peptide synthesis only a limited number of peptides are chemically made for further analysis. Here we describe an accurate and cost effective method to bin peptides on-phage based on binding region(s), without any requirement for peptide or protein synthesis. This approach, which integrates phage and yeast display platforms, requires display of target and its alanine variants on yeast. Flow cytometry was used to detect binding of peptides on-phage to the target on yeast. Once hits were identified, they were synthesized to confirm their binding region(s) by HDX (Hydrogen deuterium exchange) and crystallography. Moreover, we have successfully shown that this approach can be implemented as part of a panning process to deplete non-functional peptides. This technique can be applied to any target that can be successfully displayed on yeast; it narrows down the number of peptides requiring synthesis; and its utilization during selection results in enrichment of peptide population against defined binding regions on the target.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Predictive Tools for Customizing Heat Treatment of Additively Manufactured Aerospace Components

Laser-bed powder fusion (LBPF) additive manufacturing is increasingly being used to produce components of complex geometries using the Ni-base superalloy Inconel 718. The composition and the microstructure of the alloy are currently well optimized for wrought components made using conventional manufacturing processes such as rolling, forging, extrusion, etc. The attractive mechanical properties of the alloy result from the underlying austenitic matrix with fine equiaxed grains, and a high density and uniform distribution of the precipitation hardening phase, γ". Heat treatment steps such as homogenization, solutioning and aging are well documented for the wrought alloy. However, when the same wrought alloy compositions are used for the additive manufacturing (AM) processes, the asprocessed microstructure is significantly different, because of the different thermal history associated with LBPF, including rapid solidification and multiple temperature excursions that lead to multiple re-melting and reheating in the solid state. Rapid solidification introduces potential non-equilibrium effects at the moving solid-liquid interfaces that impact the extent of solute segregation, as well as the morphology of the dendritic grains that form. In order to recover the target mechanical properties, AM components have to undergo post-process heat treatments. However, such heat treatments have to be custom designed for the AM process and the component geometry because of the expected vast differences in the microstructure at various locations of a component with complex geometry. The homogenization and precipitation steps should be optimized for the component so that target mechanical properties can be obtained throughout the part. The objective of this research is to utilize High Performance Computing in phase field simulations of microstructure evolution during post-processing of AM components. The physics-based modeling will be beneficial in reducing the experimental effort required for heat treatment process selection, optimization, and certification, thus leading to a significant reduction in energy consumption for AM and post-processing heat treatment. The optimization study will help identify heat treatments steps that are critical for development of a final desired microstructure with the minimum energy input. This combined with shortening of the production cycle (time-to-market) by reducing the number of failed parts (property targets), and reduction in the number of iterations for process optimization, will enable 30-40% savings in the energy costs. Phase field simulations of the degree of homogenization and the effect of local matrix composition on the nucleation and growth of competing precipitating phases were performed using the Microstructure Evolution Using Massively Parallel Phase Field Simulations code developed in-house at the Oak Ridge National Laboratory. The simulations were able to successfully capture the kinetics of nucleation and growth, and morphologies of various precipitating phases as a function of local matrix compositions and composition gradients characteristic of local microstructures arising from location-dependent variations in the thermal conditions. Future work will involve extending the simulations to a length scale consisting of multiple dendrites, so that the effect of homogenization on the coarsening of the dendrites can be simulated and used as an additional input to the optimization of the heat treatment process.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Recycling of Titanium Scrap by Shear Assisted Processing and Extrusion (ShAPE)

Titanium and its alloys are used in the aviation and automobile industries due to their remarkable strength to weight ratio, but, commonly, machining loss is high with ~90 wt.% of the material being converted to scrap. Recycling post-consumer Ti scrap directly into solid bulk products is a potential solution for repurposing valuable material. Further, reducing or even eliminating fresh Ti sponge during recycling might lead to lower energy and greenhouse gas emissions. In this study, a solid-phase process known as friction extrusion was utilized to recycle Ti-6Al-4V machining chips into solid wires which could be used as feedstock in additive manufacturing. The friction consolidation technique was first used to convert chips with varying degrees of oxygen content into solid billets for its use as feedstock material in subsequent friction extrusion. The extrudates were fabricated above the beta transition temperature, which was achieved by selecting rotation rate and feed rate, to process the billets near 1000°C using a tungsten-lanthana extrusion die. This work presents the first occurrence of friction extruded titanium alloy wires. The effect of friction extrusion on microstructural features, tensile properties, and texture are reported. Overall, the friction extrusion method is capable of recycling Ti-6Al-4V scrap directly into extruded wire.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Synthesis of Carbohydrates from Methanol Using Electrochemical Partial Oxidation over Palladium with the Integrated Formose Reaction

Electrochemically derived multicarbon products are a golden target for valorization of captured carbon dioxide due to the potential of turning a waste product into useful commodity chemicals with renewable energy sources. As a tantalizing approach toward their synthesis, the formose reaction utilizes catalytic condensation of formaldehyde to generate carbohydrates. Furthermore, while a sustainable approach to artificial carbohydrate production through electrochemical generation of formaldehyde is desirable, to date, it has not been fully realized. Here, we study the electrocatalytic conversion of methanol to formaldehyde on palladium with faradaic efficiency of over 90% at 0.9 V vs Ag/AgCl and with the partial current density of nearly 3 mA cm -2 at 1.6 V vs Ag/AgCl. We observe the concurrent generation of palladium oxides as a consequence of the high overpotentials employed, which may partially explain the higher selectivity toward the partial oxidation product. Moreover, we demonstrate that formaldehyde produced electrochemically from methanol is feasible for formose reactions without the need for further purification, achieving 21-28% carbon conversion to carbohydrates. This process, therefore, represents a potential avenue for the electrochemical generation of formaldehyde and its utilization in generating multicarbon products inaccessible by other electrocatalytic means.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗