Results for “USe” — Page 31

DOE OSTI2021

Use Attainability Analysis for Upper Sandia Canyon

This document presents a use attainability analysis (UAA) for the perennial segment of Upper Sandia Canyon, which is located within the Los Alamos National Laboratory (LANL) property near Los Alamos, New Mexico.1 This UAA is consistent with 20.6.4.15 New Mexico Administrative Code (NMAC) (New Mexico Environment Department [NMED] 2011c), which describes the perennial segment as “Sandia Canyon from Sigma Canyon upstream to LANL [National Pollutant Discharge Elimination System] NPDES outfall 001.” The perennial segment’s designated uses are coldwater aquatic life, livestock watering, wildlife habitat, and secondary contact. 40 Code of Federal Regulations (CFR) § 131.10(g) permits a state to remove a designated use that is not an existing use (as defined in 40 CFR §131.3), if a UAA demonstrates that naturally occurring pollutant concentrations prevent the attainment of the use or if physical conditions related to the natural features of the water body preclude the attainment of the aquatic life protection use. This UAA considers whether natural physical conditions in Upper Sandia Canyon, specifically air and/or water temperatures, prevent the designated aquatic life use water temperature limits (i.e., coldwater) from being attained in the perennial segment. The weight of evidence presented in this UAA supports the conclusion that, based on air-water temperature modeling and instream thermograph data, the coolwater aquatic life designated use is currently the attainable use. Accordingly, it is recommended that the coolwater aquatic life designated use replace the coldwater aquatic life designated use in the Upper Sandia Canyon assessment unit (AU).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F 1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F 1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. In conclusion, these results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

3D neural networks↗

DOE OSTI2022

Strength of tantalum to 276 GPa determined by two x-ray diffraction techniques using diamond anvil cells

Tantalum (Ta) is a metal that has useful properties that make it useful in extreme environments. It is therefore important to understand how Ta performs in such extreme conditions by accurately measuring its properties. In this work, the yield strength of tantalum has been measured at pressures up to 276 GPa using axial and radial x-ray diffraction (XRD) methods in diamond anvil cells (DAC). We measured strength using XRD in a radial DAC to 50 GPa, in an axial DAC to 60 GPa using diamonds with standard flat culets, and in a final experiment to 276 GPa using toroidal diamond anvils. The radial XRD data were refined using the Material Analysis Using Diffraction (MAUD) Rietveld software package to extract lattice strain and the yield strength. The axial data were refined using the General Structure Analysis System (GSAS) II and a linewidth method was used to calculate the yield strength. The yield strength measured near ambient pressure was found to be 0.5 GPa and increased with pressure up to 50 GPa, where the yield strength plateaued at a value of 2.4 GPa. At pressures above 60 GPa, the strength increased again to a maximum value of 9 GPa at the highest pressure of 276 GPa. Here, the data from the three experiments show good agreement between the methods and previously reported experimental data. This agreement illustrates the value of axial diffraction data for material strength determination and allows for measurements at multihundreds of GPa using toroidal DACs.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Model Inputs, Outputs, and Scripts associated with: “Combined effects of stream hydrology and land use on basin-scale hyporheic zone denitrification in the Columbia River Basin”

This data package is associated with the publication “Combined effects of stream hydrology and land use on basin‐scale hyporheic zone denitrification in the Columbia River Basin”, published in Water Resource Research (Son et al.2022) available at https://doi.org/10.1029/2021WR031131. This data package includes the key model inputs/outputs of the river corridor model for the Columbia River Basin (CRB) and the model source codes used in the manuscript. The model is a carbon-nitrogen-coupled river corridor model (RCM), and the model is used to quantify hyporheic zone (HZ) denitrification at the NHDPLUS stream reach scales. The RCM used in this study combines empirical substrate models derived from observations and three microbially driven reactions, including two-step denitrification and aerobic respiration, are considered within the HZ. The key input data of the model are exchange flux, residence time, and stream solute (dissolved organic carbon (DOC), dissolved oxygen (DO), and nitrate concentrations). These inputs are constant over time and represent long-term averaged values. This study uses the RCM to explore the spatial patterns of HZ denitrification across reaches with different sizes and land use in the CRB. Our main objective is to use the RCM as a virtual reality model, and the machine-learning models as surrogates that encapsulate the complexities of the physics-based model while identifying the importance of different variables that are not evident in the model conceptualization. We do not include a direct comparison of the modeled HZ denitrification and measurements; however, the RCM can capture the overall spatial patterns of the HZ denitrification because the model inputs and its reaction networks are based on well-established theory and a physical-based model. The combination of the model-based predictions and a machine-learning approach (e.g., random forest) is used to improve our understanding of what variables of the model are associated with spatial patterns of the modeled denitrification across reaches with different sizes and land uses, and to develop a proxy model using measurable variables to reproduce the simulated patterns.This dataset contains five folders: (1) model_inputs, (2) model_outputs, (3) Rscripts, (4) figures, and (5) model_codes. It also contains a readme, file level metadata (FLMD), and data dictionary (dd). Please see the FLMD for a list of all the files contained in this data package and descriptions for each. The model_inputs folder contains the model inputs used to drive the model simulations. The model_outputs folder contains key model output files from the river corridor model. The Rscripts folder contains the Rscripts for pre- and post- processing model results. The figures folder contains the raw figures associated with the manuscript. The model_codes folder includes key model source codes/input files. All files are .jpg, .jpeg, .out, .e, .od, .dat, .sub, .F90, .0, .R, .sbx, .cpg, .sbn, .shx, .shp, .dbf, .prj, .tfw, .tif, .xml, .pdf, or .csv.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Model Data Archive for Manuscript Titled "Evaluation of a Coupled Surface–Subsurface Hydrologic Model Using Dense Water‑Level Sensors in a Mixed Urban–Rural Watershed"

This archive provides scripts, input files, and datasets used for the implementation and evaluation of a fully coupled surface–subsurface hydrologic model in the Neches River Basin, southeast Texas. The study uses the Advanced Terrestrial Simulator (ATS) to simulate coupled surface–subsurface hydrologic processes over a mixed urban–rural watershed and evaluates model performance using a dense network of 136 in situ water-level sensors, nine U.S. Geological Survey (USGS) stream gauges, and SSEBop-derived evapotranspiration estimates during the period October 2014–June 2024. The workflow is implemented primarily in Python 3 using the Watershed Workflow package. The Jupyter notebooks can be executed using open-source software such as Anaconda JupyterLab or Visual Studio Code. Other data files include TXT, CSV, XML, SHP, TIF, NetCDF, HDF5, and ExodusII files, which can be processed using the provided Python scripts. ATS input files are provided in XML format and can be edited using any commonly used text editor. This archive contains: *Scripts and input files used to generate the ATS model setup, including watershed discretization, mesh generation, parameter mapping, and model configuration. *Jupyter notebooks used for preprocessing observational data, evaluating streamflow, water levels, and evapotranspiration, computing performance metrics, and generating the figures presented in the manuscript. *ATS simulation outputs and processed observational datasets, including OneRain and DD6 water-level sensors, USGS streamflow observations, GIS data, and supporting spatial datasets used throughout the study.

Dense water-level sensor network↗

DOE OSTI2022

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

Energy & Fuels↗

DOE OSTI2023

Rapid AI-based Dissection of Ashes using Raman and XRF Spectroscopy (RADAR-X)

Waste-to-energy (WTE) facilities incinerate ~35 million tons of municipal solid waste annually in the United States. The incineration process reduces the mass and the volume of the waste fraction by over 75 and 95%, respectively. The fraction left after incineration remains as ash residues and is referred to as WTE ash, compromising of bottom and fly ash. In the United States, ~10 million tons of WTE ashes are generated annually and predominantly landfilled because of the lack of secondary end-use pathways. This incurs a significant financial burden (landfilling costs) on U.S. WTE facilities and also results in the loss of materials to the landfill. The primary objective of this research is to understand better the elemental and mineralogical composition of WTE ashes from diverse origins and find composition dependent upcycling pathways for diverting these ashes from landfills. This primary objective was addressed through three research tasks –(Task I) An AI-led Multi-Modal Approach for Compositional Analysis, (Task II) Developing a Dissolution-Based Test for Real Time Analysis, and (Task III) Establishing composition-dependent end uses. The chemical composition of WTE ash is dependent on two factors, i.e., the input waste composition and the operational parameters of a WTE facility (combustion conditions). Amongst these two factors, the input waste composition will likely show spatial and temporal variation. As a result, the chemical composition of WTE ash will also fluctuate. To understand the spatial and temporal variation in WTE ash composition, in Task I, we collected 128 ash samples (62 bottom ash and 66 fly ash samples) from 11 WTE facilities located in 11 U.S. states and characterized them via X-ray Fluorescence, powder X-ray Diffraction, and Raman Spectroscopy. The findings from this extensive characterization work indicated that the key elements in WTE fly ashes are Ca, Cl (greater than 10 wt. %), Si, S, K, Zn ( between 1 and 10 wt. %), Mg, Al, P, Ti, Fe, Cu, Br, and Pb (between 0.1 and 1 wt. %). Similarly, the key elements in WTE bottom ash fraction finer than 45μm are Ca (greater than 10 wt. %), Mg, Al, Si, S, Cl, K, Ti, Fe, Zn (between 1 and 10 wt. %), P, V, Cr, Mn, Cu, Br, and Pb (between 0.1 and 1 wt. %). Here, we note that the dominant fraction of WTE bottom ash is the coarse fraction. The coarse WTE bottom ash fraction (rich in silicon) was not characterized in this study because of excessive grinding requirements and their unsuitability as a supplementary cementitious material due to their coarse nature. The elements in WTE bottom ashes are present as calcite, anhydrite, vaterite, hydroxyapatite, quartz, bassanite, gehlenite, akermanite, hydrocalumite, and portlandite. Similarly, the mineralogical species present in WTE fly ashes are calcium chloride hydroxide, halite, calcite, anhydrite, sylvite, hydrocalumite, vaterite, hannebachite, quartz, and bassanite. Temporal variation in ash composition may also result in significant fluctuations in chemical compositions. Therefore, a WTE facility may need to monitor the ash composition (elemental and mineralogical composition) in real time. In Task I, we evaluated the possibility of using a portable X-ray fluorescence (XRF) spectrometer to monitor the elemental composition in real-time. Specifically, we collected XRF data on identical specimens via a portable XRF spectrometer (low-end) and a lab-based benchtop XRF spectrometer (high-end). The collected data was used to train an A.I. algorithm (portable XRF data as an input and benchtop XRF data as an output) to predict accurate elemental composition using portable XRF data. Finally, we developed a 2-minute photobleaching protocol to monitor the mineralogical characteristics of WTE ashes via Raman spectroscopy. Overall, the activities in Task I improved our understanding of ash composition and developed techniques to monitor elemental and mineralogical composition in near real-time. Based on the findings of Task I, we find that WTE ashes exhibit wide variability in mineralogy. For ICP-based elemental analysis, all the mineralogical species in WTE ashes must be brought into solution. This is traditionally accomplished with acid digestion using a combination of multiple acids. However, acid digestion with multiple acids is time-consuming and often fails to ensure complete digestion of the ash matrix. To address this limitation, in Task II, we developed an alkali-fusion-based digestion protocol using a combination of lithium tetraborate, lithium metaborate, and their combinations as possible alkali fluxes for digesting WTE ashes entirely and rapidly. The validity of the developed method was evaluated on two standard ash specimens, i.e., SRM 1633c coal fly ash and BCR-176R incineration fly ash specimen. The findings suggest that the developed protocol can ensure complete digestion of elements such as Al, Ba, Ca, Cr, Cu, Mg, Mn, P, Sr, V, Zn, Be, K, and rare earth elements. The recent changes in the energy market towards renewables and increased metal recycling have resulted in reduced supplies of supplementary cementitious materials (coal fly ash and slag). Therefore, in Task III, we evaluated the possibility of employing WTE ashes as SCMs. As the chemical composition of WTE ashes varies temporally (on an hourly basis), there was also a need to develop tests that can evaluate the suitability of material to act as supplementary cementitious material rapidly, i.e., in a few minutes. Therefore, in Task II, we also developed a rapid test to assess the suitability of a material to act as an SCM in 5 minutes. This represents a significant advance over the state-of-the-art R 3 test, which takes ~144 hours. This test was initially validated on amorphous aluminosilicates, such as calcined clays, and could be extended to evaluate other industrial by-products, such as WTE ashes. In Task III, we evaluated the possibility of employing WTE ashes for two applications, i.e., as an SCM and a lime substitute for clay stabilization. The findings from Task I indicated that WTE ashes are enriched in chlorine and, therefore, cannot be used directly as an SCM due to corrosion-related risks and altered hydration kinetics. Accordingly, we developed an ash treatment protocol to reduce the solubility of chlorine-containing species in WTE ashes. The developed treatment protocol also immobilized lead in certain mineral forms. As a result of the treatment, WTE ashes can be used as SCMs without any corrosion or heavy metal leaching concerns. The second application examined in this study was clay stabilization. WTE ashes are calcium-rich and can be an adequate lime replacement for clay stabilization. Our findings reveal that the sum of the concentrations of Ca(OH) 2 and CaClOH controls the clay stabilization capability of WTE ashes. In summary, in this work, we evaluated the elemental and mineralogical characteristics of U.S. WTE ashes from diverse origins and developed tests to evaluate the chemical characteristics of these ashes in real time through a portable XRF and a benchtop Raman spectrometer. Based on the chemical characteristics of these ashes, we developed an ash treatment process to enable the use of WTE ashes as an SCM and also evaluated the possibility of employing these ashes for clay stabilization. Overall, the findings from this work enables the diversion of WTE ashes from landfills for multiple end-uses, i.e., as an SCM or a lime substitute for clay stabilization.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Factors Impacting Range Hood Use in California Houses and Low-Income Apartments

Venting range hoods can control indoor air pollutants emitted during residential cooktop and oven cooking. To quantify their potential benefits, it is important to know how frequently and under what conditions range hoods are operated during cooking. We analyzed data from 54 single family houses and 17 low-income apartments in California in which cooking activities, range hood use, and fine particulate matter (PM 2.5 ) were monitored for one week per home. Range hoods were used for 36% of cooking events in houses and 28% in apartments. The frequency of hood use increased with cooking frequency across homes. In both houses and apartments, the likelihood of hood use during a cooking event increased with the duration of cooktop burner use, but not with the duration of oven use. Actual hood use rates were higher in the homes of participants who self-reported more frequent use in a pre-study survey, but actual use was far lower than self-reported frequency. Residents in single family houses used range hoods more often when cooking caused a discernible increase in PM 2.5 . In apartments, residents used their range hood more often only when high concentrations of PM 2.5 were generated during cooking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Pilot-Scale Testing of an Integrated Circuit for the Extraction of Rare Earth Minerals and Elements from Coal and Coal Byproducts Using Advanced Separation Technologies

The primary objective of this project was to develop and demonstrate an integrated pilot-scale circuitry for recovering high-value rare earth elements (REEs) from coal and coal byproducts. The target performance was to produce a mixed REE product with content of at least two percent by weight on a dry mass basis in a cost-effective and environmentally benign manner. During the first nine months of the project period (Phase 2 Budget Period 2), pilot plant construction was completed including all field site startup activities such as permitting, engineering design, procurement/bidding, unit fabrication, site construction, equipment installation, module assembly, safety training, and circuit shakedown. During the remaining 21 months of project period (Phase 2 Budget Period 3), detailed field-testing activities were performed including feedstock sample collection and preparation, exploratory testing, circuit modification, detailed parametric study, and performance optimization. A detailed techno-economic analysis was performed based on the pilot plant testing findings which provided various scenarios for REE production. The project successfully accomplished the proposed target performance by producing mixed rare earth oxide (REO) with greater than 90% purity by weight in a continuous pilot scale operation from two distinctly different coarse refuse materials (i.e., West Kentucky No. 13 and Fire Clay coal seams), and at least three secondary sources (i.e., heap leach process and naturally formed acid mine drainage system). Project partners included the University of Kentucky, Virginia Tech, West Virginia University, Alliance Coal, Blackhawk Mining, Mineral Refining Company, and Mineral Separation Technologies. The pilot scale test facility was constructed at a former mining complex owned by Alliance Natural Resource Partners (Alliance Coal). The site was rehabilitated to accommodate the equipment installation, construction and fabrication, electrical power requirement, water line management and containment. The process units constructed and installed included X-ray sorting unit, crushing and grinding unit, physical separation unit, acid leaching unit, solvent extraction unit, and wastewater management unit. A rare earth mineral concentration unit was constructed as a standalone unit for flexible operation. A detailed environmental assessment and control plan was carried out to identify and quantify any potential impacts of the pilot-scale processing circuitry on the human and eco-system health and well-being. Corresponding mitigation strategies and control measures were provided. A conceptual flowsheet was developed to effectively remove thorium and uranium from high purity rare earth oxide mix or any potential radionuclide enriched stream. The two distinct feedstock materials were secured from the Blackhawk Mining Complex in eastern Kentucky where the Fire Clay (Hazard No. 4) seam is processed. The West Kentucky No. 13 (Baker) coarse refuse material was collected from an active process stream at an Alliance coal preparation plant located in western Kentucky. Characterization analysis indicated that both of feed materials generated from the two sources contained >300 ppm of TREEs on a dry whole mass basis which met the requirements for a qualified feed stock. The two feedstocks were further upgraded using a dual x-ray sorter to prepare the feed material for hydrometallurgical circuit. Thermal treatment on feed material prior to leaching was found to: 1) improve the leaching recovery of REEs, 2) increase the leaching kinetics, and 3) allow the leaching reaction to occur at lower acidity. Roasting at 600°C was selected as the pre-treatment condition for both West Kentucky No. 13 and Fire Clay coarse refuse material. Over 40% of leaching recovery was achieved by roasting West Kentucky No. 13 material having a top particle size of 3 mm in the pilot scale operation using 1.2M sulfuric acid leaching at 75OC. Initial pilot scale testing involved continuous operation of the pilot plant for 94 hours. The leaching unit was operated at solid-to-liquid ratio of 1 to 10 (w/v) using 0.5M sulfuric acid solution at a temperature of 75°C. The continuous solvent extraction circuit utilized rougher and cleaner units with DEHPA and TBP as the extractants. An innovative stripping circuit was developed to accumulate the REE concentration in the stripping solution to a level above 600 ppm. A bleed stream from the recycled strip solution was treated using oxalic acid precipitation which produced a high grade rare earth oxalate. The oxalate product was roasted to remove the oxalate which produced a rare earth oxide product having a purity greater 90%. Due to high concentrations of contaminant ions in the pregnant leach solution (PLS), a modified flowsheet was developed that involved pre-concentration of the REEs using multiple stages of precipitation and redissolution. The advantage of this process was improved removal of contamination before the downstream purification process and a significant cost reduction relative to the circuit that utilized the solvent extraction process. The modified circuitry included processes involving leaching, multistage precipitation, redissolution, and oxalate precipitation followed by roasting of the oxalate product. The circuit produced a mixed REO that was 92.96% pure from the initial test. A detailed parametric test plan was carried out which involved varying key parameters including solids feed rate, acid flowrate, acid concentration, multistage precipitation pH, redissolution pH, oxalate precipitation dosage and pH. The response variables included REE recovery, contaminant recovery, REO product grade and overall chemical consumption. Test results indicated that the acid-to-solid ratio is the key parameter to leaching efficiency as performance deteriorated with an increase in solids concentration. The optimal pH determined for REE precipitation and redissolution was 6.5 and 2.5, respectively. Additional tests were conducted to further improve the flowsheet. Recirculating a portion of the PLS to the feed of the leach tanks improved the leaching performance by lowering the pH of the leaching system and reducing the contamination recovery by shortening the residence time. Moreover, the removal of Al prior to REE precipitation significantly reduced the oxalic acid consumption in the oxalate precipitation circuit. The modified circuit produced over 90% grade REO by weight from both West Kentucky No. 13 and Fire Clay coarse refuse material in pilot scale continuous test programs. A case analysis model was developed to project the REE and major contaminants concentration in each PLS stream based on the leaching condition, pH cut point, and oxalic acid dosage. A correlation was established using empirical and semi-empirical models. Using the models, chemical consumption required for each stage was predicted based on the projected performance of the hydrometallurgy circuit. After identifying the optimum conditions, validation tests were carried out for the treatment of both West Kentucky No. 13 and Fire Clay coarse refuse materials in the pilot plant. The actual circuit performance and chemical consumptions were very close to the model predictions. Other than the two coarse refuse sources, several secondary feedstocks were also tested in the pilot plant facility. A “heap leach” system was constructed using the coarse refuse material generated from cleaning the West Kentucky No. 13 seam coal. Using the two stage SX rougher and cleaner circuit, a concentrate with a grade >90% REO was produced while recovering >97% of the REEs from the heap leach PLS. Naturally generated acid mine drainage (AMD) from West Kentucky No.13 mine was processed using the multistage precipitation circuit in the pilot plant in a test conducted for a period of 32 hours. The final grade of the mix RE oxide produced from the AMD was 90.84% with an overall circuit recovery of 64%. The primary source of REE was the selective precipitation steps involving iron and aluminum rejection. The hydrophobic-hydrophilic separation (HHS) process was proven to effectively recover coal from fine waste materials. For REM recovery, the HHS process was able to produce concentrates at grades of approximately 1.8% REE on an ash basis; however, recovery values were typically low, <10%, under the optimal conditions determined in the laboratory-scale testing. Staged testing of the pilot-scale HHS process for coal recovery and semi-continuous laboratory testing for REM testing showed that a total concentration ratio of more than 15x was observed for the REM recovery process. A circuit simulation package was developed for REE extraction and purification using a spreadsheet-based platform (Microsoft Excel). The REESim circuit simulation package is configured to track the mass and volume flows of components passing through a series of unit operations specified and configured by the user. The mass rates can then be utilized by the user to determine important performance indicators such as product mass yields, concentrate purity levels, element-by-element recoveries, and so forth. The techno-economic analysis showed that the roasting and leaching operations were the most expensive capital items, each contributing approximately 30% to the total capital cost. One notable contributor to the high production costs was the low REE recovery observed in the pilot scale trials. The product basket price was shown to have a strong influence on the economic viability of the scenarios, with the scandium price being the most significant influencer. Operating cost was shown to be extremely sensitive to REE recovery, REE feed grade, and leaching acid consumption. An analysis of ten different scenarios for a 500 t/h commercial operation revealed that three were economically favorable, producing internal rates of return varying from 27.7% to 33.1% and payback periods of 4 to 5 years. The project successfully developed and demonstrated a process to recover REEs from coal and coal byproducts in a pilot-plant operation which consistently produced over 90% grade REO mix from varies types of feedstocks. Commercialization analysis showed that the technology readiness level successfully achieved TRL 6 at the end of the project and demonstrated the need and the potential for scaling the process to further advance the technologies toward the goal of providing a domestic supply of REEs at a commercial scale.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2026

Enhancing Sterile Neutrino Oscillation Sensitivities using SBND-PRISM at the Short-Baseline Neutrino Programme

The Short-Baseline Neutrino (SBN) Programme at Fermilab is comprised of two detectors, SBND and ICARUS, placed at $110$\,m and $600$\,m along the Booster Neutrino Beam at Fermilab. The key physics aim of the programme is to definitively test the sterile neutrino hypothesis, a proposed fourth flavour of neutrino that may explain certain experimental anomalies seen regarding standard model neutrino oscillations. To be able to detect the existence of sterile neutrinos, the uncertainties of the programme must be well constrained. To enable this, a robust analysis must be constructed that can consistently identify the correct values of systematic parameters and generate accurate predictions of what the neutrino energy spectrum at ICARUS should look like. SBND's design allows for the introduction of a technique called PRISM. In PRISM, the detector is divided into regions of different off axis angles from the beam, forming different samples where systematics impact each one in a distinct way. This allows any fits performed to obtain a better understanding of the correct value of the systematic parameters. The first analysis included in this thesis focuses on the improvements seen to the sensitivity of SBN to sterile oscillation parameters when using a PRISM configuration instead of treating SBND as a single whole. Focusing on the $5\sigma$ exclusion contour from the $\numu$ disappearance channel using three PRISM samples, an improvement on the order of $30$\,\% is seen, extending the parameter space for which the null hypothesis can be excluded. This thesis also presents a series of mock data studies comparing the abilities of SBND and PRISM analyses, concluding that in the case of simple changes between Monte Carlo (MC) and data, PRISM produces predictions of the ICARUS event rate spectrum that are more accurate and have smaller uncertainties. When moving to more realistic mock data, using different models to create the mock data than were used for the MC, the postfit predictions at ICARUS had a smaller difference between the postfit and mock data reconstructed energy spectra across all mock data samples tested. Finally, a covariance matrix defined by the maximum discrepancy between the postfit and mock data spectra at ICARUS from each of the SBND and PRISM fits across all the samples was constructed. The resultant $1\sigma$ fractional error induced by this bias systematic on the postfit spectrum has a smaller magnitude for PRISM than SBND, with the improvements ranging from $2.21$\,\% to $6.26$\,\% depending on the mock data samples. This summarises the reduction in systematic error when using PRISM instead of treating SBND as a single detector. *************************************** AUTHOR = Slater, Bethany University of Liverpool b.slater2@liverpool.ac.uk TITLE = Enhancing Sterile Neutrino Oscillation Sensitivities using SBND-PRISM at the Short-Baseline Neutrino Programme PAGES = 218 NOTE = Ph.D. University of Liverpool March 2026 ABSTRACT = The Short-Baseline Neutrino (SBN) Programme at Fermilab is comprised of two detectors, SBND and ICARUS, placed at $110$\,m and $600$\,m along the Booster Neutrino Beam at Fermilab. The key physics aim of the programme is to definitively test the sterile neutrino hypothesis, a proposed fourth flavour of neutrino that may explain certain experimental anomalies seen regarding standard model neutrino oscillations. To be able to detect the existence of sterile neutrinos, the uncertainties of the programme must be well constrained. To enable this, a robust analysis must be constructed that can consistently identify the correct values of systematic parameters and generate accurate predictions of what the neutrino energy spectrum at ICARUS should look like. SBND's design allows for the introduction of a technique called PRISM. In PRISM, the detector is divided into regions of different off axis angles from the beam, forming different samples where systematics impact each one in a distinct way. This allows any fits performed to obtain a better understanding of the correct value of the systematic parameters. The first analysis included in this thesis focuses on the improvements seen to the sensitivity of SBN to sterile oscillation parameters when using a PRISM configuration instead of treating SBND as a single whole. Focusing on the $5\sigma$ exclusion contour from the $\numu$ disappearance channel using three PRISM samples, an improvement on the order of $30$\,\% is seen, extending the parameter space for which the null hypothesis can be excluded. This thesis also presents a series of mock data studies comparing the abilities of SBND and PRISM analyses, concluding that in the case of simple changes between Monte Carlo (MC) and data, PRISM produces predictions of the ICARUS event rate spectrum that are more accurate and have smaller uncertainties. When moving to more realistic mock data, using different models to create the mock data than were used for the MC, the postfit predictions at ICARUS had a smaller difference between the postfit and mock data reconstructed energy spectra across all mock data samples tested. Finally, a covariance matrix defined by the maximum discrepancy between the postfit and mock data spectra at ICARUS from each of the SBND and PRISM fits across all the samples was constructed. The resultant $1\sigma$ fractional error induced by this bias systematic on the postfit spectrum has a smaller magnitude for PRISM than SBND, with the improvements ranging from $2.21$\,\% to $6.26$\,\% depending on the mock data samples. This summarises the reduction in systematic error when using PRISM instead of treating SBND as a single detector.

Slater, Bethany [Liverpool U.]↗

DOE OSTI2026

Enhancing Sterile Neutrino Oscillation Sensitivities using SBND-PRISM at the Short-Baseline Neutrino Programme

The Short-Baseline Neutrino (SBN) Programme at Fermilab is comprised of two detectors, SBND and ICARUS, placed at $110$\,m and $600$\,m along the Booster Neutrino Beam at Fermilab. The key physics aim of the programme is to definitively test the sterile neutrino hypothesis, a proposed fourth flavour of neutrino that may explain certain experimental anomalies seen regarding standard model neutrino oscillations. To be able to detect the existence of sterile neutrinos, the uncertainties of the programme must be well constrained. To enable this, a robust analysis must be constructed that can consistently identify the correct values of systematic parameters and generate accurate predictions of what the neutrino energy spectrum at ICARUS should look like. SBND's design allows for the introduction of a technique called PRISM. In PRISM, the detector is divided into regions of different off axis angles from the beam, forming different samples where systematics impact each one in a distinct way. This allows any fits performed to obtain a better understanding of the correct value of the systematic parameters. The first analysis included in this thesis focuses on the improvements seen to the sensitivity of SBN to sterile oscillation parameters when using a PRISM configuration instead of treating SBND as a single whole. Focusing on the $5\sigma$ exclusion contour from the $\numu$ disappearance channel using three PRISM samples, an improvement on the order of $30$\,\% is seen, extending the parameter space for which the null hypothesis can be excluded. This thesis also presents a series of mock data studies comparing the abilities of SBND and PRISM analyses, concluding that in the case of simple changes between Monte Carlo (MC) and data, PRISM produces predictions of the ICARUS event rate spectrum that are more accurate and have smaller uncertainties. When moving to more realistic mock data, using different models to create the mock data than were used for the MC, the postfit predictions at ICARUS had a smaller difference between the postfit and mock data reconstructed energy spectra across all mock data samples tested. Finally, a covariance matrix defined by the maximum discrepancy between the postfit and mock data spectra at ICARUS from each of the SBND and PRISM fits across all the samples was constructed. The resultant $1\sigma$ fractional error induced by this bias systematic on the postfit spectrum has a smaller magnitude for PRISM than SBND, with the improvements ranging from $2.21$\,\% to $6.26$\,\% depending on the mock data samples. This summarises the reduction in systematic error when using PRISM instead of treating SBND as a single detector. *************************************** AUTHOR = Slater, Bethany University of Liverpool b.slater2@liverpool.ac.uk TITLE = Enhancing Sterile Neutrino Oscillation Sensitivities using SBND-PRISM at the Short-Baseline Neutrino Programme PAGES = 218 NOTE = Ph.D. University of Liverpool March 2026 ABSTRACT = The Short-Baseline Neutrino (SBN) Programme at Fermilab is comprised of two detectors, SBND and ICARUS, placed at $110$\,m and $600$\,m along the Booster Neutrino Beam at Fermilab. The key physics aim of the programme is to definitively test the sterile neutrino hypothesis, a proposed fourth flavour of neutrino that may explain certain experimental anomalies seen regarding standard model neutrino oscillations. To be able to detect the existence of sterile neutrinos, the uncertainties of the programme must be well constrained. To enable this, a robust analysis must be constructed that can consistently identify the correct values of systematic parameters and generate accurate predictions of what the neutrino energy spectrum at ICARUS should look like. SBND's design allows for the introduction of a technique called PRISM. In PRISM, the detector is divided into regions of different off axis angles from the beam, forming different samples where systematics impact each one in a distinct way. This allows any fits performed to obtain a better understanding of the correct value of the systematic parameters. The first analysis included in this thesis focuses on the improvements seen to the sensitivity of SBN to sterile oscillation parameters when using a PRISM configuration instead of treating SBND as a single whole. Focusing on the $5\sigma$ exclusion contour from the $\numu$ disappearance channel using three PRISM samples, an improvement on the order of $30$\,\% is seen, extending the parameter space for which the null hypothesis can be excluded. This thesis also presents a series of mock data studies comparing the abilities of SBND and PRISM analyses, concluding that in the case of simple changes between Monte Carlo (MC) and data, PRISM produces predictions of the ICARUS event rate spectrum that are more accurate and have smaller uncertainties. When moving to more realistic mock data, using different models to create the mock data than were used for the MC, the postfit predictions at ICARUS had a smaller difference between the postfit and mock data reconstructed energy spectra across all mock data samples tested. Finally, a covariance matrix defined by the maximum discrepancy between the postfit and mock data spectra at ICARUS from each of the SBND and PRISM fits across all the samples was constructed. The resultant $1\sigma$ fractional error induced by this bias systematic on the postfit spectrum has a smaller magnitude for PRISM than SBND, with the improvements ranging from $2.21$\,\% to $6.26$\,\% depending on the mock data samples. This summarises the reduction in systematic error when using PRISM instead of treating SBND as a single detector.

Slater, Bethany [Liverpool U.]↗

DOE OSTI2026

Enhancing Sterile Neutrino Oscillation Sensitivities using SBND-PRISM at the Short-Baseline Neutrino Programme

The Short-Baseline Neutrino (SBN) Programme at Fermilab is comprised of two detectors, SBND and ICARUS, placed at $110$\,m and $600$\,m along the Booster Neutrino Beam at Fermilab. The key physics aim of the programme is to definitively test the sterile neutrino hypothesis, a proposed fourth flavour of neutrino that may explain certain experimental anomalies seen regarding standard model neutrino oscillations. To be able to detect the existence of sterile neutrinos, the uncertainties of the programme must be well constrained. To enable this, a robust analysis must be constructed that can consistently identify the correct values of systematic parameters and generate accurate predictions of what the neutrino energy spectrum at ICARUS should look like. SBND's design allows for the introduction of a technique called PRISM. In PRISM, the detector is divided into regions of different off axis angles from the beam, forming different samples where systematics impact each one in a distinct way. This allows any fits performed to obtain a better understanding of the correct value of the systematic parameters. The first analysis included in this thesis focuses on the improvements seen to the sensitivity of SBN to sterile oscillation parameters when using a PRISM configuration instead of treating SBND as a single whole. Focusing on the $5\sigma$ exclusion contour from the $\numu$ disappearance channel using three PRISM samples, an improvement on the order of $30$\,\% is seen, extending the parameter space for which the null hypothesis can be excluded. This thesis also presents a series of mock data studies comparing the abilities of SBND and PRISM analyses, concluding that in the case of simple changes between Monte Carlo (MC) and data, PRISM produces predictions of the ICARUS event rate spectrum that are more accurate and have smaller uncertainties. When moving to more realistic mock data, using different models to create the mock data than were used for the MC, the postfit predictions at ICARUS had a smaller difference between the postfit and mock data reconstructed energy spectra across all mock data samples tested. Finally, a covariance matrix defined by the maximum discrepancy between the postfit and mock data spectra at ICARUS from each of the SBND and PRISM fits across all the samples was constructed. The resultant $1\sigma$ fractional error induced by this bias systematic on the postfit spectrum has a smaller magnitude for PRISM than SBND, with the improvements ranging from $2.21$\,\% to $6.26$\,\% depending on the mock data samples. This summarises the reduction in systematic error when using PRISM instead of treating SBND as a single detector. *************************************** AUTHOR = Slater, Bethany University of Liverpool b.slater2@liverpool.ac.uk TITLE = Enhancing Sterile Neutrino Oscillation Sensitivities using SBND-PRISM at the Short-Baseline Neutrino Programme PAGES = 218 NOTE = Ph.D. University of Liverpool March 2026 ABSTRACT = The Short-Baseline Neutrino (SBN) Programme at Fermilab is comprised of two detectors, SBND and ICARUS, placed at $110$\,m and $600$\,m along the Booster Neutrino Beam at Fermilab. The key physics aim of the programme is to definitively test the sterile neutrino hypothesis, a proposed fourth flavour of neutrino that may explain certain experimental anomalies seen regarding standard model neutrino oscillations. To be able to detect the existence of sterile neutrinos, the uncertainties of the programme must be well constrained. To enable this, a robust analysis must be constructed that can consistently identify the correct values of systematic parameters and generate accurate predictions of what the neutrino energy spectrum at ICARUS should look like. SBND's design allows for the introduction of a technique called PRISM. In PRISM, the detector is divided into regions of different off axis angles from the beam, forming different samples where systematics impact each one in a distinct way. This allows any fits performed to obtain a better understanding of the correct value of the systematic parameters. The first analysis included in this thesis focuses on the improvements seen to the sensitivity of SBN to sterile oscillation parameters when using a PRISM configuration instead of treating SBND as a single whole. Focusing on the $5\sigma$ exclusion contour from the $\numu$ disappearance channel using three PRISM samples, an improvement on the order of $30$\,\% is seen, extending the parameter space for which the null hypothesis can be excluded. This thesis also presents a series of mock data studies comparing the abilities of SBND and PRISM analyses, concluding that in the case of simple changes between Monte Carlo (MC) and data, PRISM produces predictions of the ICARUS event rate spectrum that are more accurate and have smaller uncertainties. When moving to more realistic mock data, using different models to create the mock data than were used for the MC, the postfit predictions at ICARUS had a smaller difference between the postfit and mock data reconstructed energy spectra across all mock data samples tested. Finally, a covariance matrix defined by the maximum discrepancy between the postfit and mock data spectra at ICARUS from each of the SBND and PRISM fits across all the samples was constructed. The resultant $1\sigma$ fractional error induced by this bias systematic on the postfit spectrum has a smaller magnitude for PRISM than SBND, with the improvements ranging from $2.21$\,\% to $6.26$\,\% depending on the mock data samples. This summarises the reduction in systematic error when using PRISM instead of treating SBND as a single detector.

Slater, Bethany [Liverpool U.]↗

DOE OSTI2022

How will United States commercial building energy use be impacted by IPCC climate scenarios?

Climate change and anthropogenically-forced shift of weather in the future will impact energy use and resilience of both the built environment and the electric grid. The aim of this analysis is to understand how future climate scenarios will impact electricity and natural gas use of commercial buildings in the United States. Here, this study analyzes this impact for 2030, 2045, and 2100 using Representative Concentration Pathways (RCP) scenarios defined in Intergovernmental Panel on Climate Change (IPCC) Assessment Report 5. The large, gridded simulation of meteorological variables for RCPs 2.6, 4.5, 6.0, and 8.5 are selected and downscaled to make available hourly Future Meteorological Year (FMY) weather files for use and improvement in subsequent studies. High performance computing resources use these FMYs to simulate commercial prototype buildings in every American Society of Heating, Refrigeration, and Air Conditioning Engineers (ASHRAE) climate zone of the United States (US), and results are scaled to nation-wide energy use using conditioned floor area multipliers. The analysis is conducted without speculating the physical and performance traits of future buildings or the grid characteristics. This analysis quantifies the impact of climate change on source electrical and natural gas usage for commercial buildings in the United States over the next 80 years. If US commercial floorspace remained constant, total energy use by 2100 is predicted between an 1.75% decrease under the greatest emission scenario (8.5) and a 1.76% increase under the lowest emission scenario (2.6). When adjusted for anticipated urban growth by 2100, the predicted range is 65% increase (8.5) and 71% increase (2.6). Under a global temperature rise climate scenario, the warmest US climate zones will see a large increases in electricity use derived from space cooling while the coldest US climate zones will see significant decreases in natural gas use caused by the decrease in heating necessary. While climate change may ultimately require adaptations of the built environment to withstand its effects and because the United States is a country that requires more heating than cooling, from a building energy perspective, climate change (average temperature rise) is a net energy saver for the United States.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Large ecosystem-scale effects of restoration fail to mitigate impacts of land-use legacies in longleaf pine savannas

Ecological restoration is a global priority, with potential to reverse biodiversity declines and promote ecosystem functioning. Yet, successful restoration is challenged by lingering legacies of past land-use activities, which are pervasive on lands available for restoration. Although legacies can persist for centuries following cessation of human land uses such as agriculture, we currently lack understanding of how land-use legacies affect entire ecosystems, how they influence restoration outcomes, or whether restoration can mitigate legacy effects. Using a large-scale experiment, we evaluated how restoration by tree thinning and land-use legacies from prior cultivation and subsequent conversion to pine plantations affect fire-suppressed longleaf pine savannas. In this work, we evaluated 45 ecological properties across four categories: 1) abiotic attributes, 2) organism abundances, 3) species diversity, and 4) species interactions. The effects of restoration and land-use legacies were pervasive, shaping all categories of properties, with restoration effects roughly twice the magnitude of legacy effects. Restoration effects were of comparable magnitude in savannas with and without a history of intensive human land use; however, restoration did not mitigate numerous legacy effects present prior to restoration. As a result, savannas with a history of intensive human land use supported altered properties, especially related to soils, even after restoration. The signature of past human land-use activities can be remarkably persistent in the face of intensive restoration, influencing the outcome of restoration across diverse ecological properties. Understanding and mitigating land-use legacies will maximize the potential to restore degraded ecosystems.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Expanded analysis of machine learning models for nuclear transient identification using TPOT

Industries around the world are becoming more and more data driven. The nuclear field is no exception with several different applications being proposed. One popular area of research is the use of machine learning in transient detection. This paper seeks to build upon a previous study which made use of the AutoML package TPOT to train traditional machine learning models to classify transient events occurring with a reactor. Synthetic data was once again collected using a GPWR reactor simulator. Data on 12 different events was collected using 15 different initial conditions. Here, a dataset consisting of over 100,000 data points was compiled and used to train 7 different machine learning models using a pre-defined TPOT dictionary with 12 different preprocessing techniques. Three of the trained models were able to produce validation results in the 90s with the expanded dataset. Once the models were trained, it was possible to look into where during the simulation, misclassifications occurred. Using these three models, analysis was done to determine if TPOT could be used to train models that were effective if important features were missing. The results from this were positive with the newly trained models scoring close to the original models. Finally, to conclude this study, the three high performing models were retrained using different random states to see if there was any major variation when different states were used.

42 ENGINEERING↗

DOE OSTI2023

Combining Remotely Sensed Evapotranspiration and an Agroecosystem Model to Estimate Center‐Pivot Irrigation Water Use at High Spatio‐Temporal Resolution

Abstract Estimating irrigation water use accurately is critical for sustainable irrigation and studying terrestrial water cycle in irrigated croplands. However, irrigation is not monitored in most places, and current estimations of irrigation water use has coarse spatial and/or temporal resolutions. This study aims to estimate irrigation water use at the daily and field scale through the proposed model‐data fusion framework, which is achieved by particle filtering with two configurations (concurrent, CON, and sequential, SEQ) by assimilating satellite‐based evapotranspiration (ET) observations into an advanced agroecosystem model, ecosys . Two types of experiments using synthetic and real ET observations were conducted to study the efficacy of the proposed framework for estimating irrigation water use at the irrigated fields in eastern and western Nebraska, United States. The experiments using synthetic ET observations indicated that, for two major sources of uncertainties of ET difference between observations and model simulations, which are bias and noise, noise had larger impacts on degrading the estimation performance of irrigation water use than bias. For the experiments using real ET observations, monthly and annual estimations of irrigation water use matched well with farmer irrigation records, with Pearson correlation coefficient ( r ) around 0.80 and 0.50, respectively. Although detecting daily irrigation records was very challenging, our method still gave a good performance with RMSE, BIAS, and r around 2.90, 0.03, and 0.4 mm/d, respectively. Our proposed model‐data fusion framework for estimating irrigation water use at high spatio‐temporal resolution could contribute to regional water management, sustainable irrigation, and better tracking terrestrial water cycle.

Environmental Sciences & Ecology↗

DOE OSTI2023

The good, the bad, and the future: Systematic review identifies best use of biomass to meet air quality and climate policies in California

Abstract California has large and diverse biomass resources and provides a pertinent example of how biomass use is changing and needs to change, in the face of climate mitigation policies. As in other areas of the world, California needs to optimize its use of biomass and waste to meet environmental and socioeconomic objectives. We used a systematic review to assess biomass use pathways in California and the associated impacts on climate and air quality. Biomass uses included the production of renewable fuels, electricity, biochar, compost, and other marketable products. For those biomass use pathways recently developed, information is available on the effects—usually beneficial—on greenhouse gas (GHG) emissions, and there is some, but less, published information on the effects on criteria pollutants. Our review identifies 34 biomass use pathways with beneficial impacts on either GHG or pollutant emissions, or both—the “good.” These included combustion of forest biomass for power and conversion of livestock‐associated biomass to biogas by anaerobic digestion. The review identified 13 biomass use pathways with adverse impacts on GHG emissions, criteria pollutant emissions, or both—the “bad.” Wildfires are an example of one out of eight pathways which were found to be bad for both climate and air quality, while only two biomass use pathways reduced GHG emissions relative to an identified counterfactual but had adverse air quality impacts. Issues of high interest for the “future” included land management to reduce fire risk, future policies for the dairy industries, and full life‐cycle analysis of biomass production and use.

Agriculture↗

DOE OSTI2025

Robust measurement of microbial reduction of graphene oxide nanoparticles using image analysis

ABSTRACT Shewanella oneidensis ( S. oneidensis ) has the capacity to reduce electron acceptors within a medium and is thus used frequently in microbial fuel generation, pollutant breakdown, and nanoparticle fabrication. Microbial fuel setups, however, often require costly or labor-intensive components, thus making optimization of their performance onerous. For rapid optimization of setup conditions, a model reduction assay can be employed to allow simultaneous, large-scale experiments at lower cost and effort. Since S. oneidensis uses different extracellular electron transfer pathways depending on the electron acceptor, it is essential to use a reduction assay that mirrors the pathways employed in the microbial fuel system. For microbial fuel setups that use nanoparticles to stimulate electron transfer, reduction of graphene oxide provides a more accurate model than other commonly used assays as it is a bulk material that forms flocculates in solutions with a large ionic component. However, graphene oxide flocculates can interfere with traditional absorbance-based measurement techniques. This study introduces a novel image analysis method for quantifying graphene oxide reduction, showing improved performance and statistical accuracy over traditional methods. A comparative analysis shows that the image analysis method produces smaller errors between replicates and reveals more statistically significant differences between samples than traditional plate reader measurements under conditions causing graphene oxide flocculation. Image analysis can also detect reduction activity at earlier time points due to its use of larger solution volumes, enhancing color detection. These improvements in accuracy make image analysis a promising method for optimizing microbial fuel cells that use nanoparticles or bulk substrates. IMPORTANCE Shewanella oneidensis ( S. oneidensis ) is widely used in reduction processes such as microbial fuel generation due to its capacity to reduce electron acceptors. Often, these setups are labor-intensive to operate and require days to produce results, so use of a model assay would reduce the time and expenses needed for optimization. Our research developed a novel digital analysis method for analysis of graphene oxide flocculates that may be utilized as a model assay for reduction platforms featuring nanoparticles. Use of this model reduction assay will enable rapid optimization and drive improvements in the microbial fuel generation sector.

Bennett, Danielle T. (ORCID:0009000188748827)↗