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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An Intelligent Garbage Sorting System Based on Edge Computing and Visual Understanding of Social Internet of Vehicles

In order to enable Social Internet of Vehicles devices to achieve the purpose of intelligent and autonomous garbage classification in a public environment, while avoiding network congestion caused by a large amount of data accessing the cloud at the same time, it is therefore considered to combine mobile edge computing with Social Internet of Vehicles to give full play to mobile edge computing features of high bandwidth and low latency. At the same time, based on cutting-edge technologies such as deep learning, knowledge graph, and 5G transmission, the paper builds an intelligent garbage sorting system based on edge computing and visual understanding of Social Internet of Vehicles. First of all, for the massive multisource heterogeneous Social Internet of Vehicles big data in the public environment, different item modal data adopts different processing methods, aiming to obtain a visual understanding model. Secondly, using the 5G network, the model is deployed on the edge device and the cloud for cloud-side collaborative management, aiming to avoid the waste of edge node resources, while ensuring the data privacy of the edge node. Finally, the Social Internet of Vehicles devices is used to make intelligent decision-making on the big data of the items. First, the items are judged as garbage, and then the category is judged, and finally the task of grabbing and sorting is realized. The experimental results show that the system proposed in this paper can efficiently process the big data of Social Internet of Vehicles and make valuable intelligent decisions. At the same time, it also has a certain role in promoting the promotion of Social Internet of Vehicles devices.

Shen, Xuehao↗
DOE OSTI2015

Sol–gel method as a way of carbonyl iron powder surface modification for interaction improvement

This article presents a method for modification of carbonyl iron particles’ surface (CIP), (d{sub 50}=4–9 µm) by silica coatings obtained using the sol–gel method. Reaction parameters were determined to obtain dry magnetic powder with homogeneous silica coatings without further processing and without any by-product in the solid or liquid phase. This approach is new among the commonly used methods of silica coating of iron particles. No attempt has been made to cover a carbonyl iron surface by silica in a waste-free method, up to date. In the current work two different silica core/shell structures were made by the sol–gel process, based on different silica precursors: tetraethoxy-silane (TEOS) and tetramethoxy-silane (TMOS). The dependence between the synthesis procedure and thickness of silica shell covering carbonyl iron particles has been described. Surface morphology of the modified magnetic particles and the coating thickness were characterized with the use of scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Determination of the physicochemical structure of the obtained materials was performed by the energy-dispersive X-ray spectroscope (EDS), and the infrared technique (IR). The surface composition was analyzed using X-ray photoelectron spectroscopy (XPS). Additionally, distribution of particle size was measured using light microscopy. The new, efficient process of covering micro-size CIP with a nanometric silica layer was shown. Results of a performed analysis confirm the effectiveness of the presented method. - Highlights: • Proper covering CIP by sol–gel silica layer avoids agglomeration. • A new solid waste-free method of CIP coating is proposed. • Examination of the properties of modified CIP in depends on washing process. • Coatings on CIP particles doesn’t change the magnetic properties of particles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Low-Cost Bio-Based Carbon Fibers for High Temperature Processing

GrafTech International Holdings Inc. (GTI), under Award No. DE-EE0005779, worked with Oak Ridge National Laboratory (ORNL) under CRADA No. NFE-15-05807 to develop lignin-based carbon fiber (LBCF) technology and to demonstrate LBCF performance in high-temperature products and applications. This work was unique and different from other reported LBCF work in that this study was application-focused and scalability-focused. Accordingly, the executed work was based on meeting criteria based on technology development, cost, and application suitability. High-temperature carbon fiber based insulation is used in energy intensive industries, such as metal heat treating and ceramic and semiconductor material production. Insulation plays a critical role in achieving high thermal and process efficiency, which is directly related to energy usage, cost, and product competitiveness. Current high temperature insulation is made with petroleum based carbon fibers, and one goal of this protect was to develop and demonstrate an alternative lignin (biomass) based carbon fiber that would achieve lower cost, CO2 emissions, and energy consumption and result in insulation that met or exceeded the thermal efficiency of current commercial insulation. In addition, other products were targeted to be evaluated with LBCF. As the project was designed to proceed in stages, the initial focus of this work was to demonstrate lab-scale LBCF from at least 4 different lignin precursor feedstock sources that could meet the estimated production cost of $5.00/pound and have ash level of less than 500 ppm in the carbonized insulation-grade fiber. Accordingly, a preliminary cost model was developed based on publicly available information. The team demonstrated that 4 lignin samples met the cost criteria. In addition, the ash level for the 4 carbonized lignin samples was below 500 ppm. Processing as-received lignin to produce a high purity lignin fiber was a significant accomplishment in that most industrial lignin, prior to purification, had greater than 4X the ash level needed for this project, and prior to this work there was not a clear path of how to achieve the purity target. The lab scale development of LBCF was performed with a specific functional application in mind, specifically for high temperature rigid insulation. GTI is a consumer of foreign-sourced pitch and rayon based carbon fibers for use in its high temperature insulation products, and the motivation was that LBCF had potential to decrease costs and increase product competitiveness in the marketplace through lowered raw material costs, lowered energy costs, and decreased environmental footprint. At the end of this project, the Technology Readiness Level (TRL) remained at 5 for LBCF in high temperature insulation.

09 BIOMASS FUELS↗
DOE OSTI2019

Photocatalytic pure water splitting with high efficiency and value by Pt/porous brookite TiO 2 nanoflutes

We report the remarkable production of H 2 and H 2 O 2 from pure water using porous brookite TiO 2 nanoflutes decorated with Pt nanoparticles through two-electron photocatalytic intermediate water splitting (PIWS). The H 2 production rate from deionized water (pH similar to 7.0) was 9.8 +/- 0.6 $\mu$mol mg -1 h -1 , more than 12-fold and 230-fold higher than the reported TiO 2 -based photocatalytic overall water splitting (POWS) and H 2 /H 2 O 2 production systems, respectively. The apparent quantum yields (AQY) value can achieve 43.4%, which is the highest value compared with the reported TiO 2 -based POWS systems and the commercial value for PIWS is similar to 9-fold higher than that for POWS even ignoring the separation cost. The mechanism of the distinct performance was confirmed by DFT and experimental measurements. The present work provides a feasible strategy to significantly improve the process efficiency and value of photocatalytic water splitting in both pure water and seawater using natural sunlight.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Ultrafast processing of pixel detector data with machine learning frameworks

Modern photon science performed at high repetition rate free-electron laser (FEL) facilities and beyond relies on 2D pixel detectors operating at increasing frequencies (towards 100 kHz at LCLS-II) and producing rapidly increasing amounts of data (towards TB/s). This data must be rapidly stored for offline analysis and summarized in real time for online feedback to the scientists. While at LCLS all raw data has been stored, at LCLS-II this would lead to a prohibitive cost; instead, enabling real time processing of pixel detector data (dark, gain, common mode, background, charge summing, subpixel position, photon counting, data summarization) allows reducing the size and cost of online processing, offline processing and storage by orders of magnitude while preserving full photon information. This could be achieved by taking advantage of the compressibility of sparse data typical for LCLS-II applications. Faced with a similar big data challenge a decade ago, computer vision stimulated revolutionary advances in machine learning hardware and software. We investigated if these developments are useful in the field of data processing for high speed pixel detectors and found that typical deep learning models and autoencoder architectures failed to yield useful noise reduction while preserving full photon information, presumably because of the very different statistics and feature sets in computer vision and radiation imaging. However, the raw performance of modern frameworks like Tensorflow inspired us to redesign in Tensorflow mathematically equivalent versions of the state-of-the-art, “classical” algorithms used at LCLS. The novel Tensorflow models resulted in elegant, compact and hardware agnostic code, gaining 1 to 2 orders of magnitude faster processing on an inexpensive consumer GPU, reducing by 3 orders of magnitude the projected cost of online analysis and compression without photon loss at LCLS-II. The novel Tensorflow models also enabled ongoing development of a pipelined hardware system expected to yield an additional 3 to 4 orders of magnitude speedup, necessary for meeting the data acquisition and storage requirements at LCLS-II, potentially enabling acquiring every single FEL pulse at full speed. Computer vision a decade ago was dominated by hand-crafted filters; their structure inspired the deep learning revolution resulting in modern deep convolutional networks; similarly, our novel Tensorflow filters provide inspiration for designing future deep learning models for ultrafast and efficient processing and classification of pixel detector images at FEL facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Process parameter translation strategies for variable directed energy deposition spot size using 316L, copper, and Inconel 625

Directed energy deposition (DED) is a form of additive manufacturing available across a variety of laser spot diameter values, often referred to as spot sizes. However, there is no method to easily transfer process parameters across discrete spot sizes, leading to DED process parameters that are equipment specific and not widely applicable. In this study, a strategy is proposed and investigated for five spot sizes that keep the areal energy density constant while varying power, feed rate, and powder flow during the deposition of 316L stainless steel. An assessment of trends in hardness and microstructure is possible due to the novel production of components of a single material across several spot sizes using a single nozzle on a single DED system. The proposed strategy was used to nullify the hardness drop during functional grading of Inconel 625 and pure copper, enabling fabrication of multi-material sample that does not compromise desirable properties. This application shows the value in establishing more efficient process parameter development and understanding spot size influences on geometric and material property flexibility, to enable a more diverse powder-based DED design space and to increase the industry adoption of DED systems.

316L↗
DOE OSTI2013

Measures of the environmental footprint of the front end of the nuclear fuel cycle

Previous estimates of environmental impacts associated with the front end of the nuclear fuel cycle (FEFC) have focused primarily on energy consumption and CO2 emissions. Results have varied widely. This work builds upon reports from operating facilities and other primary data sources to build a database of front end environmental impacts. This work also addresses land transformation and water withdrawals associated with the processes of the FEFC. These processes include uranium extraction, conversion, enrichment, fuel fabrication, depleted uranium disposition, and transportation. To allow summing the impacts across processes, all impacts were normalized per tonne of natural uranium mined as well as per MWh(e) of electricity produced, a more conventional unit for measuring environmental impacts that facilitates comparison with other studies. This conversion was based on mass balances and process efficiencies associated with the current once-through LWR fuel cycle. Total energy input is calculated at 8.7 x 10- 3 GJ(e)/MWh(e) of electricity and 5.9 x 10- 3 GJ(t)/MWh(e) of thermal energy. It is dominated by the energy required for uranium extraction, conversion to fluoride compound for subsequent enrichment, and enrichment. An estimate of the carbon footprint is made from the direct energy consumption at 1.7 kg CO2/MWh(e). Water use is likewise dominated by requirements of uranium extraction, totaling 154 L/MWh(e). Land use is calculated at 8 x 10- 3 m2/MWh(e), over 90% of which is due to uranium extraction. Quantified impacts are limited to those resulting from activities performed within the FEFC process facilities (i.e. within the plant gates). Energy embodied in material inputs such as process chemicals and fuel cladding is identified but not explicitly quantified in this study. Inclusion of indirect energy associated with embodied energy as well as construction and decommissioning of facilities could increase the FEFC energy intensity estimate by a factor of up to 2.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Water Sorption Study of Various Polymers and Polymeric Composites

In the formulations of many composite materials, polymeric materials are often used as binder materials to enhance processability, functionality, and safety during manufacturing. For instance, poly(ester urethane), such as Estane® 5703, is used in the formulation of PBX 9501, and vinyl copolymer elastomer (VCE) is used in the formulation of VCE/filler compos ites. The stability of these binders directly impacts the overall stability and performance of their composites. Estane and VCE are prone to hydrolytic degradation, with water sorption behavior playing a significant role in their long-term stability and aging behavior. Hence, it is critical to study their water sorption behavior under various humidity and tempera ture conditions. To improve the accuracy and reduce labor-intensive work involved in water sorption experiments, an automated Dynamic Vapor Sorption (DVS) system was employed, which can generate large datasets. To efficiently process and analyze these datasets, Python scripts were developed. These scripts not only streamlined data processing, but also accu rately derived the Arrhenius parameters related to water diffusion and sorption properties for the tested materials. This approach offers a robust framework for evaluating water trans port and sorption processes in polymeric materials and adds valuable capabilities to future water sorption testing efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Potential of hybrid functionalized meso-porous materials for the separation and immobilization of radionuclides

Functionalized meso-porous materials are a class of hybrid organic-inorganic material in which a meso-porous metal oxide framework is functionalized with multifunctional organic molecules. These molecules may contain one or more anchor groups that form strong bonds to the pore surfaces of the metal oxide framework and free functional groups that can impart and or modify the functionality of the material such as for binding metal ions in solution. Such materials have been extensively studied over the past decade and are of particular interest in absorption applications because of the tremendous versatility in choosing the composition and architecture of the metal oxide framework and the nature of the functional organic molecule as well as the efficient mass transfer that can occur through a well-designed hierarchically porous network. A sorbent for nuclear applications would have to be highly selective for particular radio nuclides, it would need to be hydrolytically and radiolytically stable, and it would have to possess reasonable capacity and fast kinetics. The sorbent would also have to be available in a form suitable for use in a column. Finally, it would also be desirable if once saturated with radio nuclides, the sorbent could be recycled or converted directly into a ceramic or glass waste form suitable for direct repository disposal or even converted directly into a material that could be used as a transmutation target. Such a cradle-to- grave strategy could have many benefits in so far as process efficiency and the generation of secondary wastes are concerned.This paper will provide an overview of work done on all of the above mentioned aspects of the development of functionalized meso-porous adsorbent materials for the selective separation of lanthanides and actinides and discuss the prospects for future implementation of a cradle-to-grave strategy with such materials. (author)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Graph-Based Representations and Applications to Process Simulation

Rapid and robust convergence of a process flowsheet is critical to enable large-scale simulations that address core scientific questions related to process design, optimization, and sustainability. However, due to the highly coupled and nonlinear nature of chemical processes, efficiently solving a flowsheet remains a challenge. In this work, we show that graph representations of the underlying physical phenomena in unit operations may help identify potential avenues to systematically reformulate the network of equations and enable more robust topology-based convergence of flowsheets. To this end, we developed graph abstractions of the governing equations of vapor-liquid and liquid-liquid equilibrium separation equipment. These graph abstractions consist of a mesh of interconnected variable nodes and equation nodes that are systematically generated through PhenomeNode, a new open-source library in Python developed in this study. We show that partitioning the graph into separate mass, energy, and equilibrium subgraphs can help decouple nonlinearities and guide decomposition algorithms. By employing the graph abstraction on an industrial separation process for separating glacial acetic acid from water, we implemented a new block decomposition scheme in BioSTEAM and demonstrated that this can accelerate convergence over a traditional sequential modular approach.

Distillation↗
DOE OSTI2018

Molecular-scale Understanding of Selective Oxidative Transformations of Alcohols Promoted by Au and Au-based Alloys

This project developed a molecular-scale mechanistic framework for complex selective oxidation of alcohols and amines on O-covered Au and Ag-Au alloys that guides the design of selective and energy efficient processes. Our work has led to the discovery of new classes of reactions. We have also bridged a wide range of pressures and materials complexity by demonstrating catalytic activity using nanoporous Au catalysts. The selective oxidation of alcohols with each other and with other reactants, such as CO or amines, were focused on in this work because it is a key technology for large-scale chemical synthesis. Theory and experiment were combined to understand selective oxidation reactions on Au. Significant accomplishments are: (i) development of molecular-scale mechanisms for selective alcohol oxidation over Au surfaces; (ii) discovery of two new classes of reactions on O/Au—amide synthesis from amines and aldehydes and carbonylation of alcohols; and, (iii) demonstration that the mechanisms derived from our fundamental work predict the conditions for efficient catalysis using nanoporous Au catalysts under flow conditions at atmospheric pressure. This work has made an unprecedented connection between fundamental surface science studies and their implementation under catalytic conditions. This research fits squarely in the DOE-BES mission articulated in several BES Basic Research Needs (BRN) reports. “Catalysis for Energy” emphasizes the need to fabricate, design, characterize, and assemble new structures, including nanoscale alloys, and to understand and model their chemical behavior for energy-related applications. Our studies of nanoporous materials also exploit unique mesoscale phenomena to address complex energy-related issues (http://www.meso2012.com/).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Scalable Graph Analytics and HPC Operational Enhancement: Parallel Computing and ML/DL Innovations

Parallel computing plays a pivotal role in the efficient processing of large-scale graphs. Complex network analysis stands as a capti- vating research frontier, holding promise across diverse scientific domains such as sociology, biology, online media, and recommenda- tion systems. In this era, Machine Learning (ML) and Deep Learning (DL) have emerged as indispensable tools, underpinning remarkable technological achievements. Within this dynamic landscape, my research revolves around advancing parallel algorithms tailored for large-scale graph operations. To achieve this, I harness the power of cutting-edge technologies including OpenMP, MPI, HIP, and CUDA, on the High-Performance Computing (HPC) platforms to unlock optimal performance. I also apply ML/DL techniques to HPC operational data, to streamline the monitoring and maintenance of supercomputers, alleviating the complexities associated with their upkeep and enhancing user support. My research echoes the syn- ergy between parallel computing, large-scale graph analysis, and ML/DL, improving computational efficiency and user experience.

Sattar, Naw Safrin↗
DOE OSTI2012

A Practical Approach to a Closed Nuclear Fuel Cycle and Sustained Nuclear Energy - 12383

Recent systems analysis studies at Oak Ridge National Laboratory (ORNL) have shown that sufficient information is available from previous research and development (R and D), industrial experience, and current studies to make rational decisions on a practical approach to a closed nuclear fuel cycle in the United States. These studies show that a near-term decision is needed to recycle used nuclear fuel (UNF) in the United States, to encourage public recognition that a practical solution to disposal of nuclear energy wastes, primarily UNF, is achievable, and to ensure a focus on essential near-term actions and future R and D. Recognition of the importance of time factors is essential, including the multi-decade time period required to implement industrial-scale fuel recycle at the capacity needed, and the effects of radioactive decay on proliferation resistance, recycling complexity, radioactive emissions, and high-level-waste storage, disposal form development, and eventual emplacement in a geologic repository. Analysis of time factors led to identification of the benefits of processing older fuel and an 'optimum decay storage time'. Further benefits of focused R and D can ensure more complete recycling of UNF components and minimize wastes requiring disposal. Analysis of recycling costs and nonproliferation requirements, which are often cited as reasons for delaying a decision to recycle, shows that (1) the differences in costs of nuclear energy with open or closed fuel cycles are insignificant and (2) nonproliferation requirements can be met by a combination of 'safeguards-by-design' co-location of back-end fuel cycle facilities, and applied engineered safeguards and monitoring. The study shows why different methods of separating and recycling used fuel components do not have a significant effect on nonproliferation requirements and can be selected on other bases, such as process efficiency, maturity, and cost-effectiveness. Finally, the study concludes that continued storage of UNF without a decision to recycle is not a solution to the problem of nuclear waste disposal, but can be a deterrent to public confidence in nuclear energy. In summary, our studies have shown, in contrast to findings of the more prominent studies, that today we do have sufficient knowledge to make informed choices for the values and essential methods of UNF recycling, based on previous research, industrial experience, and current analyses. We have shown the significant importance of time factors, including the benefits of an optimum decay storage time on deploying effective nonproliferation safeguards, enabling reduced recycling complexity and environmental emissions, and optimizing waste management and disposal. Together with the multi-decade time required to implement industrial-scale UNF recycle at the capacity needed to match generation rate, our conclusion is that a near-term decision to recycle as many UNF components as possible is vitally needed. Further indecision and procrastination can lead to a loss of public confidence and favorable perception of nuclear energy. With no near-term decision, the path forward for UNF disposal will remain uncertain, with many diverse technologies being considered and no possible focus on a practical solution to the problem. However, a near-term decision to recycle UNF fuel and to take advantage of processing UNF and surface storing HLW, together with development and incorporation of more-complete recycling of UNF components, can provide the focus needed for a practical solution to the problem of nuclear waste disposal. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Co-synthesis of Hydrogen and High-Value Carbon Products from Methane Pyrolysis

The ARPA-E Methane Pyrolysis Project successfully developed a scalable technology for hydrogen production with low-CO x emission through methane pyrolysis, co-producing high-value carbon nanotubes (CNTs). The project focused on optimizing reactor design, enhancing catalyst performance, and assessing techno-economic feasibility to create a commercially viable and environmentally sustainable process. The fluidized bed reactor achieved over 90% methane (CH 4 ) conversion by using a 5% CO 2 co-feed, which stabilizes carbon yields and minimizes catalyst deactivation. This setup allowed for continuous operation across ten cycles, each consisting of a 14-minute pyrolysis phase followed by a 10-minute dislodging phase to remove a fraction of the accumulated carbon, resulting in stable performance and high-quality CNT production. In parallel, monolith reactors coated with Fe demonstrated a sustained methane conversion of 73% while producing CNTs with high crystallinity. Although promising for continuous operation, monolith reactors face challenges in coating durability and scalability, highlighting areas for further optimization in commercial applications. Catalyst formulation played a key role in enhancing process efficiency. The core catalyst used was 5%Fe/Al 2 O 3 (wt%), optimized through wet impregnation, which improved CNT morphology, yielding longer and more uniform CNTs. The catalyst's performance was further enhanced by adding promoters: 2.5 wt% Ni increased methane conversion close to the thermodynamic limit, while 2.5 wt% Mn improved CNT alignment and crystallinity, and 1.5 wt% NaCl boosted CNT morphology but slightly lowered methane conversion. These adjustments allowed the reactor to maintain high methane conversion while producing high-quality CNTs, enabling stable performance over multiple cycles. To address carbon buildup and ensure uninterrupted operation, a pneumatic conveying tube was implemented for effective carbon dislodging in the fluidized bed configuration. CO 2 and H 2 O co-feeds were also introduced to enhance carbon removal, with CO 2 boosting CNT yield by approximately 15%. This setup enabled stable reactor operation across multiple cycles, preventing clogging and minimizing catalyst wear, making the process suitable for industrial scaling. Techno-economic analysis (TEA) projected hydrogen production costs between $\$$1.00 and $\$$1.64 per kilogram, with CNT values assumed at $\$$375/ton and $\$$100/ton. The life cycle assessment showed that CO 2 emissions could be as low as 0.64 kg CO 2 e/kg H 2 at 95% methane conversion assuming an electricity input of 50 kg CO 2 e/MWh. Even at 50% methane conversion, emissions remained below 1 kg CO 2 e/kg H 2 , demonstrating the process's low-emission potential and making it a viable alternative to traditional steam methane reforming. Overall, the results from this project demonstrate the feasibility of a pyrolysis process where carbon is continuously removed from the catalyst surface and hydrogen is continuously produced until a catalyst regeneration step is required to fully clean the catalyst surface and renew catalyst performance. Major open challenges are related to avoiding the loss of catalyst material in the dislodged carbon during fluidized bed conditions, since our best result demonstrated a carbon purity of ~70 wt. % (rest being iron and alumina). A monolith reactor was used to favor dislodgement of carbon compared to fluidized bed conditions but our results do not demonstrate an advantage of the monolith configuration. Catalyst performance was similar to fluidized bed conditions with slower deactivation rates overall, but we could not observe carbon dislodging in any of the tens of experiments that were run at Stanford. Our results show that the most relevant areas of improvement are related to the fundamental understanding of the iron-carbon interface for dislodging, and the development of catalyst that can produce CNTs via a base-growth mechanism such that catalyst is not lost in the dislodgement steps. The final report documents all findings and methodologies in detail, providing a valuable resource for the scientific community. By building on these results, researchers can further advance methane pyrolysis technology, moving toward a more sustainable, scalable pathway for hydrogen production. This work lays the foundation for future research and commercial efforts to reduce emissions in hydrogen production while generating valuable carbon products.

08 HYDROGEN↗
DOE OSTI2026

Development of Industrial Scale Rare Earth Master Alloys from Their Native Oxides for Magnet Production

The objective of the project is to develop an energy-efficient, reduced cost, and single-step critical metal oxide reduction and alloying methodology for the production of NdFeB and SmCo magnets to facilitate the establishment of a sustainable domestic critical materials supply chain. The method consists of an immiscible molten salt flux layer and a higher density molten metal alloy pool (FeB or Co). The rare earth (RE) oxide and a reductant are added to the molten salt layer, where the reductant first strips the oxygen from the rare earth oxide. Subsequently, the separated RE metal diffuses into the molten metal pool below, creating a RE-saturated master alloy. Towards this end, thermodynamic calculations of the reactions between RE oxides, metallic reducing agent, and molten salt bath chemistry have been performed, and the ideal feeds and conditions for extraction and diffusion to produce master alloys were established. Small-scale and scaled experimentation was performed to validate and optimize the feasibility of viable reactions, temperatures, and process conditions with regards to yield, composition, and process efficiency. Finally, full-scale experiments for the production of NdFeB and SmCo magnets were performed, and their performance characteristics were established.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Defining the Minimal Set of Microbial Genes Required for Valorization of Lignin Biomass (Final Report)

Project Goals: Lignin is the second most abundant biopolymer on earth and represents a critically underutilized biomass resource for hydrocarbon feedstocks. Despite substantial effort, there is still no efficient process to convert lignin to useable carbon-based platform chemicals and materials. The goal of this project is identify a minimal set of microbial enzymes necessary for lignin breakdown and sufficient for the synthesis of valuable chemical intermediates from lignin isolated as a byproduct of lignocellulosic ethanol production. These genes will be then used to engineer functional whole cell biocatalysts for tunable lignin metabolism. To date, although a number of enzymes have been associated with lignin degradation, most have been tested in isolation (as individual enzymes) and on drastically different substrates -- often dyes that are not related to lignin. In contrast, lignin utilization in nature likely occurs by microbial consortia with multiple enzymes acting synergistically. We propose to examine two separate stages of lignin breakdown carried out by the microbes that do it best: (1) early breakdown of native polymeric lignin into soluble fragments by a set of sequenced wood-rotting fungal species, and (2) downstream metabolism of these soluble lignin fragments to useful chemical intermediates by a panel of sequenced soil saprophytes. Our approach involves testing sets of genes that will be assayed combinatorially in the context of a heterologous expression host. The resulting engineered strains will be systematically assayed using soluble lignin fragments, synthetic defined polymeric lignin, and finally lignin directly sourced from lignocellulosic processing streams. In addition to resulting in a functional whole cell biocatalyst for lignin utilization, we anticipate that this approach will allow us to address key unanswered questions about lignin metabolism in nature, including: (1) Why does the Trametes versicolor genome contain 25 different class II peroxidases? (2) What is the role of laccases in lignin metabolism? Why do some aggressive lignin degraders have many laccases (e.g. >7 in T. versicolor) while others have none (e.g. P. chrysosporium)? (3) How is peroxide provided in a controlled manner to drive peroxidase activity without causing the enzyme inhibition that is so often observed in vitro? (4) What strategies do microbial lignin degraders use to avoid the problem of repolymerization during active lignin degradation? and (5) Can microbial lignin metabolism be diverted for high level production of defined aromatics? A final critical question is whether combining key minimal sets of enzymes from a wide range of organisms will result in engineered strains capable of highly efficient, streamlined pathways for lignin utilization that can be tuned for a specific carbon output. This effort will leverage DOE investments in microbial genome sequencing, and secure a critical channel for lignin biomass utilization that will also help to render lignocellulosic a viable feedstock for the production of renewable liquid biofuels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Process intensification education contributes to sustainable development goals. Part 1

In 2015 all the United Nations (UN) member states adopted 17 sustainable development goals (UN-SDG) as part of the 2030 Agenda, which is a 15-year plan to meet ambitious targets to eradicate poverty, protect the environment, and improve the quality of life around the world. Although the global community has progressed, the pace of implementation must accelerate to reach the UN-SDG time-line. For this to happen, professionals, institutions, companies, governments and the general public must become cognizant of the challenges that our world faces and the potential technological solutions at hand, including those provided by chemical engineering. Process intensification (PI) is a recent engineering approach with demonstrated potential to significantly improve process efficiency and safety while reducing cost. It offers opportunities for attaining the UN-SDG goals in a cost-effective and timely manner. However, the pedagogical tools to educate undergraduate, graduate students, and professionals active in the field of PI lack clarity and focus. This paper sets out the state-of-the-art, main discussion points and guidelines for enhanced PI teaching, deliberated by experts in PI with either an academic or industrial background, as well as representatives from government and specialists in pedagogy gathered at the Lorentz Center (Leiden, The Netherlands) in June 2019 with the aim of uniting the efforts on education in PI and produce guidelines. In this Part 1, we discuss the societal and industrial needs for an educational strategy in the framework of PI. The terminology and background information on PI, as it relates to educational implementation in industry and academia, are provided as a preamble to Part 2, which presents practical examples that will help educating on Process Intensification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗