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DOE OSTI2023

AmeriFlux FLUXNET-1F US-xJR NEON Jornada LTER (JORN)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xJR NEON Jornada LTER (JORN). This is the FLUXNET version of the carbon flux data for the site US-xJR NEON Jornada LTER (JORN) produced by applying the standard ONEFlux (1F) software. Site Description - Land use at Jornada has contrasting livestock grazing histories (including ungrazed lands), dating back to 1902, which are representative of the pervasive land use in desert-southwest wildlands. Historical accounts of the region report significant changes in vegetation starting in the late 1800s coincident with the expansion of livestock grazing. By 1912, sufficiently dramatic changes lead area scientists and private landowners to convince the U.S. government to set aside Public Domain Lands for the Jornada Range Reserve for the purpose of scientific investigation on shrub invasion and subsequent loss of forage grasses. Much of this early research focused on quantifying utilization levels for forage species, developing livestock production strategies to deal with drought, and developing methods for shrub control and grass recovery. Exclosures were constructed and long-term plots were established throughout the range to monitor the continued expansion of shrubs across the landscape. Over the decades, numerous trials of various remediation approaches were implemented, from manual and mechanical shrub removal to herbicide application to construction of terraces or other means of redirecting surface flow of runoff.

Network), NEON (National Ecological Observatory↗

DOE OSTI2020

Assessment of Enhanced Oil Recovery Using Carbon Dioxide in Michigan Basin Silurian Pinnacle Reefs

The Midwest Regional Carbon Sequestration Partnership (MRCSP) has incorporated the work of geologic research teams (Geoteams) in its regional characterization, project planning and carbon dioxide (CO 2 ) injection implementation work since the partnership was established by the U.S. Department of Energy (DOE) in 2003. Over this 16-year period, the cohort of Geoteams has grown from five to ten states and has contributed to the characterization of geologic sequestration opportunities, refinement of reservoir and seal data, and supported injection efforts through both predictive and post-injection assessments. The Michigan team at Western Michigan University has provided technical analysis and support of the enhanced oil recovery (EOR) using carbon dioxide (CO 2 ) in the Northern Michigan Silurian Pinnacle reefs. With Core Energy, LLC as the industry partner, this project has assessed the potential for EOR in known fields in the Michigan Pinnacle reefs and the capacity for CO2 storage in the reef reservoirs as they are depleted and after reservoir abandonment. This project managed by Core Energy, LLC, involves the only commercial CO 2 Enhanced Oil Recovery floods within the MRCSP region. The Silurian pinnacle reef trend shows significant potential for CO 2 -EOR: this play is relatively new, with approximately 60% of the wells in the largest Niagaran fields still accessible. Most wells have modern logs and there are many cores, core analyses, pressure tests and produced water analyses available for study from these fields. These carbonate reservoirs in Michigan have produced sufficient oil to warrant evaluation for additional recovery efforts. The role of the Michigan team was to collect and analyze all relevant data about reservoir properties, including lithologic and depositional characteristics of the reservoir formations and porosity and fluid flow properties through different compartments in the reservoir. The team has also developed models that represent the geological and physical characteristics of the reservoir and seal system.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

Artificial Intelligence for Accelerating Nuclear Applications, Science, and Technology

Artificial intelligence (AI) and machine learning (ML) methods have had significant impacts in science and technology in recent years. These methods for generating models from datasets or logic-based algorithms that emulate aspects of human performance can similarly accelerate the fields of nuclear applications, science, and technology toward the IAEA goals of contributing to peace, health, and prosperity. In order to accomplish advances with AI in general and ML in particular across these fields, IAEA can play a significant role by establishing, hosting and curating centralised resources, including databases, adhering to FAIR (findable, accessible, interoperable and reusable) principles and Open Science best practices, providing stewardship of data sharing, supporting training efforts and development of relevant workforces, as well as enabling connections among the scientific, technology, mathematics, AI and ethics communities. Many areas can benefit from the use of AI in the realm of nuclear applications. In human health, these areas include clinical research, epidemiology, nutrition, medical imaging, radiotherapy and education of health professionals. AI-based tools are also being used to facilitate different clinical tasks in imaging, computer-assisted diagnosis in mammography and lung cancer screening programmes, and dose prediction in nuclear medicine procedures. ML methods in particular may also increase the efficiency and accuracy of the analysis of computerised tomography and dual-energy absorptiometry scans for body composition and bone analysis. The application of AI methods to nuclear and related technologies in food and agriculture can lead to significant advances and improved efficiency in the optimisation of agricultural production, food product development, management of supply chains, food safety and food authenticity control. In the water and environmental sector, AI can help inform policies to mitigate the world’s water problems. The application of AI techniques to hydrology and environmental sciences is expected to improve patterns identification and enable model predictions under a changing climate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Cloud-Control of Legacy Building Automation System: A case study

As Internet of Things devices and cloud-based platforms become more mature, Energy Management and Information Systems (EMIS) are increasingly gaining momentum in the building industry. In large commercial buildings, Fault-Detection and Diagnostic (FDD) and energy information systems (EIS) are now established technologies with tens of providers and thousands of deployment sites across North America. The new frontier for the EMIS technology is now represented by control systems that use advanced system optimization (ASO) methods to improve the operations of the HVAC system. Given the complexity of the integration of such systems with the existing building automation systems (BAS) and the higher risk involved with direct control of the HVAC, these systems are still emerging in the market. This paper presents the results of a project in which a start-up company partnered with a research institution to develop a cloud-based software EMIS solution and deployed it in a university campus in California. The software system included advanced sensing, data acquisition, storage and advanced control and analytics applications developed on top of the native BAS. The new platform controls ten buildings on the campus and the FDD and the ASO applications deployed on this platform were able to generate energy savings of up to 35% and 25% in certain buildings for each functionality respectively. Where the platform did not save energy, it improved building service (air quality). Lessons learned include the importance of collaborating with and training the building operators and evaluating whether the legacy system can work reliably with the new technology.

Prakash, Anand Krishnan↗

DOE OSTI2022

A Comprehensive Simulation Study to Evaluate Future Vehicle Energy and Cost Reduction Potential

Under the umbrella of EERE’s Office of Sustainable Transportation, the U.S. Department of Energy’s (DOE) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) seek to develop sustainable, affordable, and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate the potential for vehicle-level fuel savings is critical to understanding DOE’s impact and success in this mission For this study, Argonne National Laboratory (Argonne) simulated technologies funded by VTO and HFTO for light-duty vehicles across the following: Powertrain configurations (conventional, power-split hybrid electric vehicle, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles); Vehicle classes (compact car, mid-size car, small sport utility vehicle [SUV], mid-size SUV, and pickup truck); Fuels (gasoline, diesel, natural gas, hydrogen, and battery electricity). We assessed each technology for five different timeframes: laboratory years 2015 (reference), 2020, 2025, 2030, and 2045. We assumed a delay of 5 years between laboratory year and model year (i.e., the year the technology is introduced into production). Finally, we included uncertainties for both technology performance and cost by considering two cases (note that these cases are not intended as predictions of future performance): Low case , aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on business as usual regulatory and market environments; High case , aligned with aggressive technology advancements based on research and development (R&D) targets developed through support by VTO and HFTO. We estimated the energy and cost impact of different technologies using Autonomie (Argonne undated), a state-of-the-art vehicle system simulation tool developed by Argonne and used to assess the energy consumption, performance, and cost of multiple advanced vehicle technologies. The tool comprises a complete set of vehicle models to assess impacts across a wide range of classes (from light- to heavy-duty), powertrain configurations (from conventional to hybrid electric vehicles [HEVs], fuel cell electric vehicles [FCEVs], plug-in hybrid electric vehicles [PHEVs], and battery electric vehicles [BEVs]), components, and control strategies, including vehicle-level and component-level controls developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies: analyzing various component technologies, sizing powertrain components to meet different vehicle requirements, comparing the benefits of powertrain configurations, optimizing both heuristic and route-based vehicle energy control, and predicting transportation energy use when paired with a traffic modeling tool such as POLARIS. This report documents the assumptions made and the vehicle-level energy consumption benefits and associated technology costs estimated for various types of light-duty vehicles. Details regarding vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.

08 HYDROGEN↗

DOE OSTI2020

Measurement of free chlorine levels in water using potentiometric responses of biofilms and applications for monitoring and managing the quality of potable water

We report residual free chlorine is not monitored continuously at scale in drinking water distribution systems because existing real-time sensor technologies require frequent maintenance, cleaning, and calibration, which makes these products too costly to be used throughout a distribution system. As a result, current measurement approaches require manual sampling, which is not feasible for the consistent monitoring of free chlorine because chlorine concentrations vary significantly throughout pipeline distribution and over time and space. This research presents an alternative and cost-effective method of predicting free chlorine levels in drinking water using graphite electrodes coated with naturally grown microbial biofilms. This Microbial Potentiometric Sensor (MPS) array was installed in a Continuously Mixed Batch Reactor (CMBR), and drinking water containing variable free chlorine concentrations. The chlorine concentrations were introduced in a controlled manner, and the MPS signals were monitored over time. MPS signals were measured from the change in Open Circuit Potential (OCP) across the MPS array in real-time. An empirically derived relationship between the normalized change in OCP and free chlorine was established by fitting individual and average MPS data to a decaying exponential growth function in order to predict free chlorine levels. The results show that free chlorine can be predicted with reasonable accuracy, with model validation showing an average absolute error of ±0.09 ppm below 1.1 ppm and ±0.30 ppm between 1.1 and 2.7 ppm. However, the accuracy of predictions was reduced at higher free chlorine levels. The researchers conclude that MPS systems may benefit drinking water distribution systems by measuring free chlorine. These advantages of the MPS are especially pronounced in the developing world because this system is inexpensive and does not require routine maintenance or cleaning. The system relies on a naturally forming and regenerating biofilm and an inexpensive potentiometric meter to produce stable measurements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Daily evapotranspiration changes during heatwaves at 32 NEON sites, 2019-2021

This dataset provides partitioned evapotranspiration (ET, the combined loss of water from soil and plant surfaces) anomalies during heatwave events—soil evaporation (E) and transpiration (T)—for 268 heatwave events across 32 National Ecological Observatory Network (NEON) flux sites in the contiguous United States from 2019–2021. Using an ensemble of four high-frequency turbulence methods (Flux-variance Similarity, Conditional Eddy Covariance [CEC], CEC with Water-Use Efficiency, and Conditional Eddy Accumulation; see Zahn and Bou-Zeid 2024), half-hourly transpiration-to-evapotranspiration (T/ET) ratios were derived from 20 hertz (Hz, cycles per second) eddy covariance measurements of carbon dioxide (CO₂) and water vapor (H₂O) concentrations. The dataset spans six vegetation types including evergreen and deciduous forests, grasslands, cultivated crops, shrublands, and emergent herbaceous wetlands. Data Package Contents: The dataset includes a single CSV (comma-separated values) file containing daily anomalies (deviations from baseline conditions) for transpiration (Delta_T), evaporation (Delta_E), total evapotranspiration (Delta_ET), and T/ET ratio (Delta_T_ET) during each day of identified heatwave events. The file also includes site codes, dates, heatwave event identifiers, and day-of-heatwave indicators. The CSV file can be opened with spreadsheet software (Microsoft Excel, Google Sheets) or programming environments (Python, R, MATLAB). This resource enables researchers to investigate ecosystem-specific responses to thermal extremes, validate land surface model partitioning of ET fluxes, and examine feedbacks between water cycling and surface energy balance during heatwaves. The dataset is particularly valuable for studies linking vegetation hydraulic strategies to climate resilience, as it captures the divergent responses of shallow-rooted versus deep-rooted ecosystems. Potential applications include improving drought early warning systems, informing irrigation management strategies, and advancing our mechanistic understanding of land-atmosphere interactions under extreme heat conditions.

Day of Heatwave↗

DOE OSTI2021

Science Breakthroughs 2030. Final report

Agriculture is a fundamental societal activity, characterized by many different landscapes, crops, markets, and participants. Food, agricultural, and biofuels products are central to the daily life of all citizens, though most do not recognize the fragility of the environment that brings forth this abundance. As is noted in the 2012 report from the President's Council of Advisors on Science and Technology, Agricultural Preparedness and the United States Agricultural Research Enterprise (PCAST, 2012) the food and agricultural system faces constant challenges in: Managing new pests, pathogens, and invasive plants. Increasing the efficiency of water use. Growing food in a changing climate. Reducing the environmental footprint of agriculture. Managing the production of bioenergy. Producing safe and nutritious food. Assisting with global food security and maintaining abundant yields. Science Breakthroughs 2030 was organized to identify the most compelling research directions in food and agriculture, in particular those empowered by the application of insights and tools from disciplines of science and engineering not typically associated with food and agricultural research. A committee appointed by the Chairman of the National Research Council explored ideas for research directions with input from the scientific community, with the objective of producing a report describing ambitious and achievable scientific pathways to address major problems and create new opportunities in food and agriculture. Following numerous meetings, a jamboree, and town hall, the appointed committee prepared a report that has subsequently become a reference for federal agencies supporting research in the food and agricultural space. It highlights five key areas for research investment with broad application across food and agriculture: integrated systems research; sensor development; data mining and information sciences, genomics; and the microbiome.

09 BIOMASS FUELS↗

DOE OSTI2021

Wind Energy Forecasting with the Weather Research and Forecasting Model

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Siemens Energy, Inc. (Siemens) to develop a wind resource forecasting tool. LLNL was to develop an independent high-resolution mesoscale modeling capability forecasting tool that could be implemented in conjunction with existing wind farm control and monitoring software to provide forecasting of wind resources using local observations of winds and temperature. Research with LLNL’s state-of-the-art large-eddy simulation meteorological prediction model, based on the community WRF model and innovative turbulence parameterizations, would improve that model’s applicability to large wind farms offshore and in complex terrain. The modeling capability would include uncertainty quantification. Finally, the application of the modeling tool and existing global climate change predictions would enable the delineation of the likely effects of climate change on wind resources. Siemens was to provide high time resolution hub-height wind speed and other meteorological data streams, including temperature profiles from wind farms, for LLNL to incorporate into the modeling system, to validate and tune this forecasting model for their locations of interest. These data streams would also be used for longer-term studies of correlations of wind resources to climate oscillations to indicate how long-term climate change trends may affect the available wind resource. Siemens would also provide information and observations of turbine wakes for incorporation into the modeling tool. By implementing state-of-the-art turbulence parameterizations into a simulation model and/or ensembles of simulation models, and by integrating real-time hub height wind speed and other meteorological datastreams from wind farms into that model or ensemble of models, LLNL would develop a forecasting tool that could be implemented by Siemens as an add-on to existing wind farm control and monitoring software to provide owners with useful resource forecasting. The desired outcome was that the accuracy level of the output would be sufficient to substantiate power output commitments. The final deliverable for this work would consist of a document outlining the algorithms and software tools that could be integrated into Siemens Wind Park Supervisor.

17 WIND ENERGY↗

DOE OSTI2021

Strategic Energy Management Program Persistence and Cost Effectiveness An Analysis of the SEM Program Landscape

This study examines the relationship between strategic energy management (SEM) programs and their persistence and cost effectiveness, with analysis based on interview data from 24 SEM program administrators, SEM program evaluations, and other reports. The 80 interview questions focused on the topics of program design, energy savings, energy savings persistence, cost effectiveness, and customer SEM persistence. The generosity of interview respondents provided a wealth of data, resulting in a report of sufficient length to warrant inclusion of this brief guide of the report structure. The major sections are listed below with brief descriptions. Individual sections of this report are mainly stand-alone and do not require reading of other sections. As a result, there is some duplication between sections, but with differing levels of detail. Executive Summary: Presents three key conclusions of this work with a short description of potential actions to advance the understanding of each key finding. Brief Observations: Lists a large number of bulleted observations resulting from this research, arranged by the five major topic areas included in the interviews. Analysis details are provided in the Analysis of Interview Results section. Foundations for this Research: Provides an overview of SEM, SEM frameworks, SEM programs, the topics of persistence and cost effectiveness, and the focus of this research. Methodology: Details the approach and strategy of this research, providing background information relevant to the formulation of interview questions and the identification of which SEM programs to interview. Observations from Compiled Evaluations and Other Reports: Reports observations from the collection and analysis of program evaluations, annual reports, utility planning documents, and SEM-related white papers. This section, presented in bullet form, highlights challenges in data collection and ultimately a comparison of program practices as they pertain to persistence and cost effectiveness. Analysis of Interview Results: Presents detailed analysis of responses from SEM program administrators, arranged by the five major categories examined: program design, energy savings, energy savings persistence, cost effectiveness, and customer SEM persistence. Interview questions are generally grouped together into subsections when it makes sense to examine them together. SEM Programs Challenge Traditional Cost-Effectiveness Metrics: Details an analysis based on five key factors showing that applying traditional cost-effectiveness metrics to SEM programs is not straightforward. This invites the opportunity to consider whether traditional cost-effectiveness metrics are applicable to SEM programs, either individually or at large. Resolution of Research Hypotheses: Tabulates a set of hypotheses that were developed to address the fundamental nature of the research at hand. Analysis of responses to multiple questions informs an understanding of each hypothesis and can be used to better understand the SEM program environment at large.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Carbon Utilization and Storage Partnership of the Western United States

This technical report documents research conducted under DOE Award No. DE-FE0031837 focused on evaluating the feasibility of carbon capture, utilization, and storage (CCUS) systems in the central and western United States. The project integrated geologic characterization, reservoir simulation, infrastructure modeling, and economic analysis to assess CO₂ storage potential near industrial sources and develop strategies for transport and sequestration. The work included subsurface modeling, risk assessment, monitoring and verification (MRV) planning, and evaluation of regulatory pathways such as EPA Underground Injection Control (UIC) Class VI permitting and IRS 45Q tax credit eligibility. Results demonstrate the viability of multiple storage approaches, including saline formations, enhanced coalbed methane recovery, and basalt mineralization, supported by data-driven workflows and regional analyses. The project also produced permitting templates, technology transfer activities, and stakeholder engagement efforts to support deployment readiness. These findings contribute to the development of scalable, economically viable CCUS systems and provide a repeatable framework for future carbon management projects.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Ibis Networks/WattIQ (IN2 Final Report)

Ibis Networks is a full-stack cleantech company that provides plug-level energy monitoring and control to solve energy and asset management problems for the enterprise. During DATES – DATES, an NREL research team worked with the Ibis team to validate their product, the InteliSocket. The Ibis InteliSocket is a pass-through plug-load energy monitor and controller that is designed to reduce energy wasted by common 120 V plug-in devices in commercial office buildings, such as computer peripherals, conference room AV equipment, and break-room appliances. The system can shut off supply power to these end uses via remote control, manual switches, pre-set schedules, or automated control algorithms. The scope of this IN2 project was the development and refinement of “smart” learning behavior algorithms (LBAs), which could help installation processes and dramatically expand the sockets’ capabilities and energy-saving potential by suggesting suitable control schedules that are based on monitored use patterns. While Ibis has the analytical and software expertise for algorithm development, the lack of test data, both in a controlled laboratory setting and in real-life deployment scenarios, represented a key barrier toward commercialization of the product. Assistance through the IN2 program provided an opportunity to conduct the needed “trial and error” algorithm development. The project included baseline field-data collection, laboratory testing, and field validation components, all of which were conducted at the NREL campus between April 2017 and July 2019.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Advanced Characterization to Inform Sustainable Recovery of Critical Minerals from Fossil Energy Waste Feedstocks

Rare earth elements (REE) and other critical minerals (CM, e.g., Co, Li) have important uses in green energy and modern technologies, yet are vulnerable to potential supply chain disruptions. One potential domestic source of CM is fossil energy wastes, such as acid mine drainage (AMD) and treatment solids (AMD solids), coal combustion ash, and Oil and Gas (O&G) drilling wastes (drill cuttings and produced waters). CM recovery from these waste feedstocks is promising due to their abundances and fast availability as waste products. On the other hand, the CM occurrence in these wastes is in small quantities compared to traditional ore bodies. Thus, novel and strategic separation and extraction processes are under exploration. Researchers at DOE’s National Energy Technology Laboratory (NETL) are collecting and analyzing CM data for aforementioned fossil energy wastes, and utilizing advanced geochemical characterization (e.g., synchrotron microprobe and sequential extraction) to understand the CM speciation and binding environments in these materials to better inform sustainable and effective recovery. Successful examples discussed in this talk include: (1) the discovery of easily mobile REE phases in Ca-rich coal combustion ash and developing a patented REE recovery process from Ca-rich Powder River Basin coal ash; (2) the successful identification of REE/Co/Ni/Zn hosting phases in acid mine drainage treatment solids (AMD solids) with diverse chemical composition (Al, Mn, or Fe-rich) informing the sequential recovery of different REE/CMs from AMD solids; (3) the recovery potential of Li and other CMs in O&G produced waters and drill cuttings. The innovations driven by characterization have the potential to offset the cost of waste management and wastewater treatments while reducing the cost and environmental footprint of CM extraction.

characterization and extraction↗

DOE OSTI2024

Metagenome-assembled genomes provide insight into the metabolic potential during early production of Hydraulic Fracturing Test Site 2 in the Delaware Basin

Demand for natural gas continues to climb in the United States, having reached a record monthly high of 104.9 billion cubic feet per day (Bcf/d) in November 2023. Hydraulic fracturing, a technique used to extract natural gas and oil from deep underground reservoirs, involves injecting large volumes of fluid, proppant, and chemical additives into shale units. This is followed by a “shut-in” period, during which the fracture fluid remains pressurized in the well for several weeks. The microbial processes that occur within the reservoir during this shut-in period are not well understood; yet, these reactions may significantly impact the structural integrity and overall recovery of oil and gas from the well. To shed light on this critical phase, we conducted an analysis of both pre-shut-in material alongside production fluid collected throughout the initial production phase at the Hydraulic Fracturing Test Site 2 (HFTS 2) located in the prolific Wolfcamp formation within the Permian Delaware Basin of west Texas, USA. Specifically, we aimed to assess the microbial ecology and functional potential of the microbial community during this crucial time frame. Prior analysis of 16S rRNA sequencing data through the first 35 days of production revealed a strong selection for a Clostridia species corresponding to a significant decrease in microbial diversity. Here, we performed a metagenomic analysis of produced water sampled on Day 33 of production. This analysis yielded three high-quality metagenome-assembled genomes (MAGs), one of which was a Clostridia draft genome closely related to the recently classified Petromonas tenebris. This draft genome likely represents the dominant Clostridia species observed in our 16S rRNA profile. Annotation of the MAGs revealed the presence of genes involved in critical metabolic processes, including thiosulfate reduction, mixed acid fermentation, and biofilm formation. These findings suggest that this microbial community has the potential to contribute to well souring, biocorrosion, and biofouling within the reservoir. Our research provides unique insights into the early stages of production in one of the most prolific unconventional plays in the United States, with important implications for well management and energy recovery.

natural gas↗

DOE OSTI2020

Are scenario projections overly optimistic about future yield progress?

Historical increases in agricultural production were achieved predominantly by large increases in agricultural productivity. Intensification of crop and livestock production also plays a key role in future projections of agricultural land use. Here, we assess and discuss projections of crop yields by global agricultural land-use and integrated assessment models. To evaluate these crop yield projections, we compare them to empirical data on attainable yields by employing a linear and plateauing continuation of observed attainable yield trends. While keeping in mind the uncertainties of attainable yields projections and future climate change impacts, we find that, on average for all cereals on the global level, global projected yields by 2050 remain below the attainable yields. Additionally, this is also true for future pathways with high technological progress and mitigation efforts, indicating that projected yield increases are not overly optimistic, even under systemic transformations. Furthermore, on a regional scale, we find that for developing regions, specifically for sub-Saharan Africa, projected yields stay well below attainable yields, indicating that the large yield gaps which could be closed through improved crop management, may also persist in the future. In OECD countries, in contrast, current yields are already close to attainable yields, and the projections approach or, for some models, even exceed attainable yields by 2050. This observation parallels research suggesting that future progress in attainable yields in developed regions will mainly have to be achieved through new crop varieties or genetic improvements. The models included in this study vary widely in their implementation of yield progress, which are often split into endogenous (crop management) improvements and exogenous (technological) trends. More detail and transparency are needed in these important elements of global yields and land use projections, and this paper discusses possibilities of better aligning agronomic understanding of yield gaps and yield potentials with modelling approaches.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Proton Therapy for Breast Cancer: A Consensus Statement From the Particle Therapy Cooperative Group Breast Cancer Subcommittee

Radiation therapy plays an important role in the multidisciplinary management of breast cancer. Recent years have seen improvements in breast cancer survival and a greater appreciation of potential long-term morbidity associated with the dose and volume of irradiated organs. Proton therapy reduces the dose to nontarget structures while optimizing target coverage. However, there remain additional financial costs associated with proton therapy, despite reductions over time, and studies have yet to demonstrate that protons improve upon the treatment outcomes achieved with photon radiation therapy. There remains considerable heterogeneity in proton patient selection and techniques, and the rapid technological advances in the field have the potential to affect evidence evaluation, given the long latency period for breast cancer radiation therapy recurrence and late effects. In this consensus statement, we assess the data available to the radiation oncology community of proton therapy for breast cancer, provide expert consensus recommendations on indications and technique, and highlight ongoing trials’ cost-effectiveness analyses and key areas for future research.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2025

Capacitated p -hub approach for park-and-ride facility location problem under nested logit demand function: polyhedral approaches

By generalizing the unconstrained p-hub approach for the park-and-ride (P&R) facility location problem under the multinomial logit demand function, the capacitated p-hub approach for the problem under the nested logit demand function captures a broader range of real-world cases. To solve this problem optimally, we introduce a mixed-integer linear program and accelerate its solution by enhancing the branch-and-cut procedure. To address the problem at a large scale, we introduce two other polyhedral approaches: variable neighborhood search (VNS) and adaptive randomized rounding (ARR). Downtown areas in Seoul have a high modal share of public transportation and congested road traffic, yet P&R has not been widely implemented. Therefore, we apply the ARR procedure to solve a real-world problem using traffic and geographic data from the Seoul metropolitan area. ARR performs better than VNS and addresses real-world cases. The solutions obtained by ARR present a phased expansion plan that encourages policymakers to start installing a small number of P&Rs immediately.

Capacitated p-hub approach↗