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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Printable Fiber Reinforced Cement Composites – Feasibility Study

Additive manufacturing is enabling the manufacturability of structures with previously unattainable complexity or functionality, and there is growing interest in additive manufacturing of “printed” concrete structures. The focus of this Phase 1 Technical Collaboration (TC) project was to evaluate feasibility of printing hybrid cement composite structures reinforced with textile carbon fibers (tCF). This project leverages other (non-IACMI) projects on cement formulation and printing process development, as well as on the production process for tCF. This project’s primary focus was to explore cement composite mix design with textile carbon fibers to be manufactured by MonteFibre (TC partner) and evaluate suitable fiber-matrix interface or sizing for cement composites working with Michelman (TC Partner). This project supports IACMI’s goal of reducing the cost and embodied energy of carbon fiber composites. Cost is one of the fundamental challenges to carbon fiber reinforced cement composites. Cement is an extremely inexpensive material (approximately $\$$0.05/lb). Adding 1 wt% of conventional carbon fiber to cement quadruples its cost. Therefore, the need to use low-cost carbon fiber and ensure that the additional cost of the carbon fiber has a greater cost benefit to the final product. This was the first preliminary evaluation to integrate tCF reinforcement in cement composites, and such potential tCF utilization should significantly reduce materials cost. Cement composite production is energy and emissions intensive, thus by strengthening it less material will be required. Hence, the embodied energy and production time of the resulting structures will be reduced. Additionally, integrating these new materials into additive processes can enable selective use of the material in high load or stress areas. It is noteworthy that past work in this field of fiber reinforced cement composites did not consider the optimization of fiber-matrix interface using suitable sizing. Carbon fiber reinforcement offers potential added benefits of thermal conductivity (which affects cure rate) and flow behavior that could provide opportunities for site specific utilization of carbon fiber on hybrid cement structures (e.g. use the fiber reinforcement on outer surfaces to enhance strength and modulus and then infiltrating the internal structures with conventional concrete). MonteFibre was the industry lead and planned on supplying the tCF for this project. However, during the short Phase-1 duration of this project, MonteFibre was unable to produce tCF for this project due to manufacturing plant being off-line throughout the course of the project. The project team decided to pursue an alternate option which involved demonstrating printable concrete with steel fibers by the ORNL lead, Dr. Brian Post. The University of Tennessee collaboration team focused on evaluating the suitable chemical sizing for carbon fibers working with Michelman and also developed methods for material characterization of cement-based composites to evaluate the material response for compression, shear, flexure, and tension. The two milestones for University of Tennessee, Knoxville were realized related to identification of one sizing suitable for carbon fiber reinforced cement composite and developing data associated with mechanical behavior of unreinforced (neat) and carbon fiber reinforced cement composites. ORNL could not complete the task of carbon fiber reinforced printed cement composites due to the reasons mentioned earlier, but was able to replace tCF with steel fibers to demonstrate the feasibility of printing with fiber reinforced cement composites. The Project team reviewed possible sizing chemistry available in collaboration with Michelman for use on carbon fiber reinforcement in cement composites and concrete applications. Our initial goal was to identify a sizing most promising for formulation with textile carbon fibers (tCF) to deliver excellent mechanical properties in composite material state. Since tCF was not available for this project as originally envisioned, the team continued this task to identify a suitable sizing for carbon fiber applications by applying such sizing to lower cost carbon fibers currently available commercially from Zoltek called Panex fibers. At a future time this can be optimized for textile carbon fibers from Montefibre. The bulk of previous work on carbon fiber reinforced cement has neglected the importance of fiber-matrix adhesion on mechanical properties of the cement composite and identifying this missing link was an important accomplishment for future research. Tensile behavior of fiber reinforced concrete is important to evaluate in order to realize the dream of concrete products that do not need reinforcing steel. Important sample preparation and testing procedures were addressed in this study and it was concluded that substantial improvements in tensile behavior, without compromising compressive strength, and improved ductility can result from the use of carbon fiber reinforcement.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Toward efficient single-atom catalysts for renewable fuels and chemicals production from biomass and CO 2

Transformation of biomass and CO 2 into renewable value-added chemicals and fuels has been identified as a promising strategy to fulfill high energy demands, lower greenhouse gas emissions, and exploit under-utilized resources. Cost-effective and performance-efficient catalysts are of great importance to lowering the conversion cost of biomass and CO 2 . Significant progress has been made to advance the catalyst design for these processes, with metal catalysts playing a critical role in many involved catalytic reactions. Traditional nanoparticle-based metal catalysts still require improvement in metal utilization rates, stability, and selectivity tunability. Single-atom catalysts, which have maximum atomic efficiency and a uniform and tunable metal center, as well as an adjustable metal-support interaction, provide potential opportunities to boost catalyst efficiency and thermal stability. Their well-defined and uniform structure also provides advantages to fundamental studies for understanding of the intrinsic reaction mechanism and site requirement in biomass and CO 2 conversion. In this article, we summarize and highlight the recent advances in converting biomass and CO 2 to renewable fuels and chemicals using single-atom catalysts. We discuss the design principles of single-atom catalysts and their potential applications to biomass and CO 2 upgrading as well as the origins of catalytic activity. Moreover, we compare the catalytic efficiency of various catalysts reported thus to provide a fair assessment of these catalysts. Finally, perspectives are given on the interesting fields that may guide future studies.

09 BIOMASS FUELS↗
DOE OSTI2024

Application of deep learning methods for beam size control during user operation at the Advanced Light Source

Past research at the Advanced Light Source (ALS) provided a proof-of-principle demonstration that deep learning methods could be effectively employed to compensate for the significant perturbations to the transverse electron beam size induced by user-controlled adjustments of the insertion devices. However, incorporating these methods into the ALS’ daily operations has faced notable challenges. The complexity of the system’s operational requirements and the significant upkeep demands has restricted their sustained application during user operation. Here, we introduce the development of a more robust neural network (NN)-based algorithm that utilizes a novel online fine-tuning approach and its systematic integration into the day-to-day machine operations. Our analysis emphasizes the process of NN model selection, demonstrates the superior performance of the NN-based method over traditional feedback methods, and examines the effectiveness and resilience of the new algorithm during user-operation scenarios. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Influence of Process Parameters and Alloy Composition on Crack Mitigation in Selective Laser Melting

Microcracking and residual stresses are key limitations of laser powder bed fusion (LPBF), an additive manufacturing (AM) technique that uses a high power laser to consolidate successive layers of metal powder. For tungsten, these microcracks find their origin in the combination of a ductile-to-brittle transition (DBT) between 200°C and 400°C and high residual stresses. This work utilized in situ high speed video to capture the cracking mechanisms, and combined the experimental results with thermomechanical modeling to unveil correlations between crack network morphology and process parameter-related variables, such as the local temperature and residual stress distributions. Consequently, preheating and alloying with rare earth oxides were adopted as possible crack mitigation strategies, the efficacy of which was tested against the initial fundamental baseline results. Preheating temperatures above 500°C eliminated all cracking, though this threshold is expected to be higher when tungsten powder (higher oxygen content) is introduced. Alloying can serve as an active oxygen getter in the system, but the rare earth oxides in this work were too large to have significant contributions to crack-mitigation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Digitalization mapping and assessment process supporting ION strategic transformation activities

The existing fleet of commercial nuclear power plants (NPPs) are an important asset in the nation’s portfolio of electrical generating resources. Their continued safe and reliable operation are critical to providing a large source of carbon-free electricity to power the nation’s economy. The United States Department of Energy’s (DOE) Light Water Reactor Sustainability (LWRS) Program develops the scientific bases, methods, and tools, for the continued safe and economical operation of the nation's commercial NPPs. The Plant Modernization Pathway within LWRS Program focuses on providing guidance to industry on the full-scale implementation of modernization solutions for NPPs that significantly reduce the technical and financial risks associated with modernization. This research is focused on helping the nuclear industry understand how to digitize and digitalize their NPPs so that they can design their modernization solutions to be scalable, sustainable, and integrated both laterally and horizontally within their organization. That is, this research creates a digital transformation in NPPs by reshaping cultural mindsets and by identifying business efficiencies. In partnership with industry, and using four previously established guiding principles for digitalization, this research supported NPP modernization through assessing readiness for digitalization as a means to achieve integrated operations for nuclear. Specifically, this research created an assessment to review an entire organization’s work processes to gather information about the digitalization health of the plant. The assessment tools were administered to plant employees, and the results were used to develop a digitalization plan. The survey assessment identified the optimal candidate processes that would most benefit from a digitalization initiative which were revealed through analytical frameworks. One analysis calculated mean digitalization health indicator scores for all endorsed activities which allowed the researchers to rank and color code the results for easy identification. Individual health indicator scores are also provided, should our industry partner wish to understand these findings according to their own organizational priorities, business considerations and desired end-state. The results were also analyzed from the perspective that organizations are comprised of different types of innovators (e.g., generators, optimizers, conceptualizers, and implementers), which differentially affects the organization’s ability to comprehend and adapt to change (i.e., opportunities to innovate). Understanding the relative composition of innovator types at an NPP allows them to gather insights into the strengths and weaknesses they have in innovating how work is performed. For the utility that partnered with this research team, the results showed that implementers make up the largest portion of respondents and conceptualizers the smallest portion. Knowing the proportion of innovator types gave this organization insights on how they can effectively implement their innovation solutions. Additionally, the results were analyzed from a technical, economic, and risk perspective to identify and quantify work reduction opportunities (WROs). Recognizing that not all cost-saving opportunities are the same, a Technical, Economic and Risk Assessment (TERA) was performed to evaluate WROs to identify areas of greatest potential and lowest risk. The key results from TERA included a digitalization opportunity score for each activity, and a calculation of potential cost savings. These two outputs formed the bases for calculating a priority index and rank for the activities/processes assessed. From the prioritization calculations, TERA can then help the utility 1) decide what digitalization priorities to invest money in implementing and then 2) calculates how much should be invested in the digitalization initiatives selected to achieve cost savings and/or an acceptable return on investment. Last, onsite interviews revealed several inefficiencies in the standard work processes that occur cross-departmentally that are due to the absence of digitized and digitalized processes. Examples of these include time spent scanning paper documents and then uploading the documents electronically, obtaining signatures, and searching for desired information. This represents a digital but not digitalized process. Over 15 opportunities to improve work processes were identified through this multi-method digitalization assessment. The various analytical assessments used (e.g., TERA, digitalization health indicator scores), as well as discussions with the utility partner, corroborated that all the opportunities identified had a strong potential to make work processes more efficient and to improve overall performance of the NPP.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Future Intensity‐Duration‐Frequency Curves of Extreme Precipitation in the Midwest United States From Convection‐Permitting Modeling

Abstract During the last four decades, global warming has statistically significant intensified extreme precipitation events in the Midwestern United States (defined here as the region covering Illinois, Indiana, Ohio, and Kentucky), leading to increased risks to human life, property, and infrastructure. To enable climate change adaptation and resilience across various economic and social sectors in this region, updated information about future climate changes, specifically at finer spatial scales, is essential. Leveraging a new 150‐year dynamical downscaling data set at convection‐permitting resolution, this study introduces a framework to construct the projected future intensity‐duration‐frequency (IDF) curves of heavy precipitation, which are prominent tools for infrastructure design and water resources management. This framework generates IDF curves at both sub‐daily and multi‐day duration utilizing hourly in situ observations as well as quantile‐based statistical techniques in bias‐correction and return levels selection. The assumption of non‐stationarity in the distribution parameter fitting process is also implemented in this workflow. Compared to historical IDF curves for 1980–2022, future projected IDF curves for 2058–2100 under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios indicate an average intensity increase of approximately 15% and 25%, respectively, across 74 stations, considering both annual and seasonal timescales. Future projections suggest that extreme precipitation events may become more severe across six investigated return periods, with longer return periods showing a greater increase. The frequency of future extreme precipitation events in the Midwest region is also projected to double. Furthermore, current results reveal spatial heterogeneity of future trends across stations owing to the high‐resolution input data set. Plain Language Summary This study investigates the evolving nature of extreme precipitation events in the Midwestern United States under a changing climate. By leveraging a high‐resolution dynamical downscaling data set, we construct projected intensity‐duration‐frequency (IDF) curves for future extreme rainfall events. These curves serve as vital tools for infrastructure planning and water resource management. Our analysis reveals a significant increase in both the intensity and frequency of extreme precipitation events in the region. Future projected IDF curves for the late century indicate an average intensity increase of approximately 15%–25% compared to historical values. Moreover, the frequency of such events is expected to double. Spatial heterogeneity in future trends is observed across different stations within the Midwest, highlighting the importance of high‐resolution modeling in capturing localized climate variability. These findings underscore the urgent need for climate adaptation strategies to mitigate the increasing risks associated with extreme precipitation events in the region. Key Points This study introduces a workflow to construct future intensity‐duration‐frequency (IDF) curves over the Midwest United States using a new convection‐permitting modeling data set The current IDF construction workflow reproduces well the historical observed IDF 30 curves in summer months with median relative errors of 2.4% among 74 stations and 6 investigated durations The projected IDF curves show diverse future trends of extreme precipitation across stations, with intensity increases of approximately 15% and 25% under RCP4.5 and RCP8.5 climate scenarios, respectively, and a doubling of frequency on average

Nguyen, Trung↗
DOE OSTI2024

Physics-Based Machine Learning Methods for U-235 Forensics Signatures

Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collectedunder structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore,their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distanceto the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensembleof trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fittingobserved in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inversephysical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of thesource strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations ofunderlying regression methods.

Rao, Nageswara↗
DOE OSTI2021

Data processing pipeline for Tianlai experiment

The Tianlai project is a 21cm intensity mapping experiment for detecting dark energy by measuring the baryon acoustic oscillation (BAO) features in the large scale structure power spectrum. This experiment provides an opportunity to test the data processing methods for cosmological 21cm signal extraction, which is still a great challenge in current radio astronomy research. The 21cm signal is much weaker than the foregrounds and easily aected by the imperfections in the instrumental responses. Furthermore, processing the large volumes of interferometer data poses a practical challenge. We have developed a data processing pipeline called tlpipe to process the drift scan survey data from the Tianlai experiment. It performs oine data processing tasks such as radio frequency interference (RFI) agging, array calibration, binning, and map-making, etc. It also includes utility functions needed for the data analysis, such as data selection, transformation, visualization and others. A number of new algorithms are implemented, for example the eigenvector decomposition method for array calibration and the Tikhnov regularization for m-mode analysis. In this paper we describe the design and implementation of the pipeline and illustrate its functions with some analysis of real data. Finally, we outline directions for future development of this publicly code.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Research and Test Reactor Fuels

PRO-RR is the research reactor focused program element of the broader Proliferation Resistance Optimization program (PRO-X) under the National Nuclear Safety Administration (NNSA) in the U.S. Department of Energy (DOE). PRO-X provides a framework for integrating proliferation resistance in nuclear system designs to minimize weapons usable nuclear materials (WUNM) production and diversion pathways while optimizing systems performance for peaceful use missions. PRO-RR applies the PRO-X mission objectives to research reactor system design. This document serves as one of the foundational documents for the PRO-RR-Fuel System Design technical team by documenting current research reactor fuels usage. The PRO-RR-Fuel System Design technical team consists of subject matter experts from Argonne National Laboratory (Argonne) and Savannah River National Laboratory (SRNL). In order to determine the preferred fuel of use in upcoming research and test reactors to optimize proliferation resistance, performance, and safety, it is useful to assess the fuels that have been used in the past, or are currently in use. This report reviews the historical and current fuels used in research and test reactors to inform future fuel selection. Chapter 2 discusses the low-enriched uranium (LEU) fuels currently in use in terms of thermal power level and utilization of the reactor. Chapter 3 summarizes the fabrication processes for common fuel types. Chapter 4 discusses in detail the fuel types in use in research and test reactors. A review of the cladding types in use is presented in Chapter 5, and a historical review of research and test reactor fuel fabricators is presented in Chapter 6. The data collection strategy used the International Atomic Energy Agency (IAEA) research reactor database [1] as a starting point. Information on the fuel used was gathered on research reactors (other than critical assemblies) that were listed as operational, planned, or in temporary shutdown in the IAEA database. Data on the fuel type, geometry, enrichment, uranium loading, cladding type, and fabricator were collected for each of the reactors available in the public domain. Sources of data included conference papers, journal articles, and facility and fabricator websites. Data on research reactors operating on LEU fuels are presented in Appendix A, while Appendix B presents data collected on all reactors at the time of publication of this report. Appendix C presents data collected on reactors that were part of the M3 research and test reactor conversion program.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Physics-Based Machine Learning Methods for U-235 Forensics Signatures

Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collected under structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore, their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distance to the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensemble of trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fitting observed in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inverse physical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of the source strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations of underlying regression methods.

Rao, Nageswara↗
DOE OSTI2025

Sulfur Conversion to Donor‐Acceptor Ladder Polymer Networks through Mechanochemical Nucleophilic Aromatic Substitution for Efficient CO 2 Photoreduction

The development of synthetic methods capable of converting elemental sulfur into conjugated porous sulfur‐rich polymers remains a great challenge, although direct utilization of this readily available feedstock can significantly enrich its uses and circumvent environmental problems during sulfur storage. Here, we report herein mechanochemical (MC) nucleophilic aromatic substitution (S N Ar) that enables sulfur conversion into thianthrene‐bridged porous ladder polymer networks with dense donor‐acceptor (D−A) molecular junctions. We demonstrate that the key lies in the generation of bent thianthrene units through a solid‐state ball‐milling condensation reaction between 1,2‐dihaloarenes and elemental sulfur. We also show that the assembling of D−A structural motifs into porous networks affords efficient visible‐light‐driven photocatalytic reduction of carbon dioxide (CO 2 ) with water (H 2 O) vapor, in the absence of any additional photosensitizer, sacrificial agents or cocatalysts. Exceptional photoinduced charge separation along with boosted exciton dissociation results in a high‐performance of carbon monoxide (CO) production rate of 306.1 μmol g −1 h −1 with near 100 % CO selectivity, which is accompanied by H 2 O oxidation to O 2 , as confirmed by both experimental and theoretical results. We anticipate this novel MC S N Ar approach will advance processing techniques for direct sulfur utilization and facilitate new possibilities for the synthesis of D−A ladder polymer networks with promising potential in photocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Intensified Catalytic Conversion of CO2 into High Value Chemicals

The utilization of CO2 produced from power generation plants as a feedstock for creating new valuable products offers a strategy to reduce GHG emissions, offset carbon capture costs, and facilitate the rebalancing of the carbon cycle. After considering the thermodynamic requirement of potential products from CO2 utilization and their market size, formic acid is a desirable target that offers widespread utility in industrial chemical production and chemical energy storage as a liquid fuel. To effectively utilize CO2 from power generation plants, UK proposes an enhanced bimetallic oxide carbon utilization process (EBOCU) that enables electrochemical CO2 conversion to formic acid. The UK intensified electrocatalytic process combines three main components: 1) a novel bimetallic oxide electrocatalyst; 2) a stable electrochemical reactor using robust electrodes; and 3) a pressurized electrochemical reactor. The output from this project showed the economic viability of producing high-value formic acid from CO2 to both offset the cost associated with post-combustion CCS and reduce GHG emissions. This project developed and screened a series engineered catalysts to selectively reduce CO2 directly to formic acid. The best performing catalyst based on formic acid production, stability, and Faradaic efficiency, was immobilized on carbon electrodes, and tested inside a flow through reactor. After lab-scale testing was completed, the experimental data was used to perform a Life-Cycle Analysis (LCA) and conduct an Initial Technical and Economic Feasibility Study. The LCA showed a pathway to a net negative CO2 utilization process with the incorporation of renewable energy, while the TEA showed the potential to produce formic acid below the current commercial price. The UK EBOCU process was successfully demonstrated at the TRL3 level and has a clear pathway to further development and contribution to the nation’s ambitious decarbonization goals.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Rare Earth Extraction and Concentration at Pilot-Scale from North Dakota Coal-Related Feedstocks (Final Technical Report)

The objectives of this project were to design, construct, commission, and operate a pilot-scale system utilizing UND's REE extraction technology from lignite, and complete saleability and economic evaluations of products. The process includes a dilute-acid extraction process from low-rank-coals, followed by selective precipitations and further processing to produce mixed rare earth oxide materials. The pilot was successfully constructed to a 1,000 lb/hr nameplate capacity and tested with over 100 tons of >300-ppm lignite-based feedstocks and produced saleable-quality products during operation. The team successfully attained a TRL status of 6 with the completion and testing of the pilot system, and the technology is poised for demonstration at a commercial scale.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Where’s Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data

Abstract We present the Swimmy (Subaru WIde-field Machine-learning anoMalY) survey program, a deep-learning-based search for unique sources using multicolored (grizy) imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). This program aims to detect unexpected, novel, and rare populations and phenomena, by utilizing the deep imaging data acquired from the wide-field coverage of the HSC-SSP. This article, as the first paper in the Swimmy series, describes an anomaly detection technique to select unique populations as “outliers” from the data-set. The model was tested with known extreme emission-line galaxies (XELGs) and quasars, which consequently confirmed that the proposed method successfully selected $\sim\!\! 60\%$–$70\%$ of the quasars and $60\%$ of the XELGs without labeled training data. In reference to the spectral information of local galaxies at z = 0.05–0.2 obtained from the Sloan Digital Sky Survey, we investigated the physical properties of the selected anomalies and compared them based on the significance of their outlier values. The results revealed that XELGs constitute notable fractions of the most anomalous galaxies, and certain galaxies manifest unique morphological features. In summary, deep anomaly detection is an effective tool that can search rare objects, and, ultimately, unknown unknowns with large data-sets. Further development of the proposed model and selection process can promote the practical applications required to achieve specific scientific goals.

Astronomy & Astrophysics↗
DOE OSTI2021

The Cell Utilized Partitioning Model as a Predictive Tool for Optimizing Counter-Current Chromatography Processes

Counter-current chromatography (CCC) is capable of unique elution modes that isolate analytes using the movement of the stationary phase in addition to moving the mobile phase. These modes include elution-extrusion CCC (EECCC) and dual-mode CCC (DM CCC) that are not possible in traditional solid-liquid chromatography systems. Although EECCC and DM CCC are widely used to recover highly retained components, to our knowledge, optimizing the elution process in these modes with predictive models has not been reported. To address this gap, we developed a predictive model for CCC dubbed the Cell Utilized Partitioning (CUP) model. The CUP model accurately predicts the effluents of multicomponent separations in EECCC and DM CCC modes when compared to experimental data. Furthermore, CUP model simulations were extended to investigate the influence of operating and intrinsic parameters on the yield and productivity, and to compare the separation performances of EECCC and DM CCC in various conditions. The results demonstrate that low distribution constants, usually a KD less than 1, and a selectivity > 1.3, under specific flowrate ranges, increase both productivity and yield. From these results, generalized optimization and scaleup guidelines are proposed that can apply to research settings and to industrial processes to maximize preparative CCC performance.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2021

The hybrid topological longitudinal transmon qubit

We introduce a new hybrid qubit consisting of a Majorana qubit interacting with a transmon longitudinally coupled to a resonator. To do so, we equip the longitudinal transmon qubit with topological quasiparticles, supported by an array of heterostructure nanowires, and derive charge- and phase-based interactions between the Majorana qubit and the resonator and transmon degrees of freedom. Inspecting the charge coupling, we demonstrate that the Majorana self-charging can be eliminated by a judicious choice of charge offset, thereby maintaining the Majorana degeneracy regardless of the quasiparticles spatial arrangement and parity configuration. We perform analytic and numerical calculations to derive the effective qubit-qubit interaction elements and discuss their potential utility for state readout and quantum error correction. Further, we find that select interactions depend strongly on the overall superconducting parity, which may provide a direct mechanism to characterize deleterious quasiparticle poisoning processes.

36 MATERIALS SCIENCE↗
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↗