Multi-Agent Based Attack-Resilient System Integrity Protection for Smart Grid
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Concentrating solar power (CSP) technologies can utilize heat from concentrated sunlight from a field of tracking mirrors to generate electricity, reform fuel, provide process heat, or augment fossil plant heat sources. Electricity-generating power tower systems focus light from thousands of independent heliostats onto a thermal receiver, which uses the focused light to warm a heat transfer fluid (HTF), typically, a molten nitrate salt. The HTF is then sent to a power generation cycle or diverted into thermal energy storage (TES) for later use. Thermal storage is – in principle – a straightforward proposition. However, optimal utilization of a TES resource is complex and multi-faceted: thermal energy may be dispatched to produce electricity immediately upon first availability, or thermal energy may be reserved for next-day peak periods at risk of filling storage and dumping energy, or a portion of the thermal energy can be reserved to maintain equipment temperatures, reducing power cycle startup time, etc. Many possible dispatch permutations variously emphasize producing peak power, operating through transients, expediting daily startup, etc. The best operation strategy can change day-to-day throughout the year, depending on the weather and market pricing forecasts. The project we describe in this report develops a software package that allows users to explore design optimization, operations decisions, and performance characterization of concentrating solar power tower plants. Users interface with the tool through a scripting language, and results are reported in time series tables, plots, runtime logs, and design outputs. Users choose from a list of variables such as tower height, solar multiple, design-point irradiance, thermal storage size, etc., and specify information about the system using a list of parameters. The software can then optimize the specified variables to reduce the cost of energy produced by the system while meeting certain production requirements, accounting for uncertain weather and electricity price forecasts, and correcting for equipment failures or repair time. The software we develop is the first comprehensive design tool of its kind to incorporate all of these aspects while being deployed as open source.
The production of hydrogen fuels, via water splitting, is of practical relevance for meeting global energy needs and mitigating the environmental consequences of fossil-fuel-based transportation. Water photoelectrolysis has been proposed as a viable approach for generating hydrogen, provided that stable and inexpensive photocatalysts with conversion efficiencies over 10% can be discovered, synthesized at scale, and successfully deployed. While a number of first-principles studies have focused on the data-driven discovery of photocatalysts, in the absence of systematic experimental validation, the success rate of these predictions may be limited. We address this problem by developing a screening procedure with co-validation between experiment and theory to expedite the synthesis, characterization, and testing of the computationally predicted, most desirable materials. Here, starting with 70 150 compounds in the Materials Project database, the proposed protocol yielded 71 candidate photocatalysts, 11 of which were synthesized as single-phase materials. Experiments confirmed hydrogen generation and favorable band alignment for 6 of the 11 compounds, with the most promising ones belonging to the families of alkali and alkaline-earth indates and orthoplumbates. This study shows the accuracy of a nonempirical, Hubbard-corrected density-functional theory method to predict band gaps and band offsets at a fraction of the computational cost of hybrid functionals, and outlines an effective strategy to identify photocatalysts for solar hydrogen generation.
One of the key new features of connected lighting systems (CLS) is their ability to collect data from various types of integral sensors and share that data with other lighting or building systems. Occupancy and vacancy sensors have been widely adopted as an energy-saving strategy in buildings, yet published test methods for reproducibly characterizing their performance remain few and limited in their sophistication. As a result, it has been difficult to predict the performance of such sensors in a specific application, and in practice, they frequently do not meet energy-savings expectations. Occupants at times remove or otherwise bypass occupancy sensors that hinder their work or otherwise do not perform as expected, thereby compromising the sensors’ potential to reduce energy consumption. Poor performance can result from multiple causes – ranging from fundamental limitations of the sensor technology, to misconfiguration, to poor placement in the room or space. Innovative occupancy sensors, some of them combining multiple sensing technologies (i.e., multimodal), have come on the market over the years, with claims of improved performance compared to their predecessors. However, in practice, their performance has neither differed enough from the performance of previous products to necessitate a test method that facilitated comparison between them, nor has it led to high deployment or high user satisfaction in human-occupied spaces with persistent presence. While the performance of both common and novel occupancy sensors has been the subject of many published research articles, the test methods that have been employed for them typically have been loosely described and have incorporated custom equipment or techniques that render them difficult to reproduce, or have been limited in their ability to fairly characterize devices that utilize varying sensor technology. The lack of a fully described, technology-agnostic test method that yields reproducible results across different implementations has been a barrier to the commercial success of new occupancy-sensor products, as users and specifiers who have been disappointed with previous products are often unwilling to take a chance with new ones. Motivated by a desire to fairly characterize new technologies that continue to enter the market and claim not only improved occupancy detection but, in some cases, additional capabilities (e.g., the ability to measure traffic or discern between different object types), this report presents the results of a literature review of recently published fully described test methods for characterizing occupancy-sensor performance, as well as research articles containing ad-hoc test methods. The review also identifies and consolidates test conditions for characterizing sensor performance in indoor spaces and identifies apparent test method gaps that need to be filled in order to evaluate emerging technologies and products. The identified test-method conditions are intended to enable the development of a future technology-agnostic test method that facilitates occupancy-sensor performance characterization more-accurately representing performance in buildings.
We present a global explainability method to characterize sources of errors in a real-world multitask deep abstaining classifier (DAC), in the context of cancer histology prediction. Our multitask classifier, currently deployed for automated annotation of cancer pathology reports from NCI-SEER registries, was trained and evaluated on 1.04 million hand-annotated samples and makes simultaneous predictions of cancer site, subsite, histology, laterality, and behavior for each report. The DAC framework enables the model to abstain on ambiguous reports and confusing classes to achieve the target accuracy on the retained (non-abstained) samples, but at the cost of decreased coverage. Requiring 97% accuracy on the histology task caused our model to retain only 22% of all samples, mostly the less ambiguous and common classes. Local explainability with the GradInp technique provided a computationally efficient way of obtaining contextual reasoning for hundreds of thousands of individual predictions. Our method, involving dimensionality reduction of approximately 13000 aggregated local explanations (ALE), offers a tractable path to true global explainability. It enabled identification of sources of errors in histology classification, globally, as hierarchical complexity among classes, label noise, insufficient information, and conflicting evidence. This suggests several strategies for iterative improvement of our DAC, including well-designed exclusion criteria, focused annotation, and reduced penalties for errors involving hierarchically related classes.
Abstract Gas hydrates, waxes and asphaltenes represent some of the most significant flow assurance challenges, especially in subsea lines, where treatment options can be limited. Currently, complete avoidance is the primary strategy for hydrate management, while chemical or mechanical flushing/pigging may be utilized for other solids. Each system must also be treated with individualized solutions, as there have been no proved one-size-fits-all technologies demonstrated to date. As an alternative to constant chemical injection or thermodynamic controls such as insulation or heating, a robust omniphobic surface treatment material has been developed which has been shown in previous studies to significantly reduce the adhesion of flow assurance solids, resulting in lower risk for deposition and plugging of gas hydrates, waxes, and asphaltenes. As part of a Department of Energy study, laboratory scale tests were performed on a variety of apparatuses ranging from micromechanical force (micron scale) to laboratory flow loop testing (meter scale). While the results from these tests have been promising, there can be some disconnects between lab-scale observations and field-scale testing. Several factors can result in discrepancies between small-scale bench-top studies and deployment-scale efforts. These include flow conditions (Reynolds number, flow regime), chemistry (synthetic vs. crude oils, gas composition, and additives), physical differences (pipeline material, size, and geometry), etc. This work will describe the actions taken to better understand the deployment and performance of a material such as this omniphobic surface treatment in field scenarios, and to build confidence for deployment in a production scenario. These efforts include modeling field production scenarios using models developed from lab observations, application of the material to field-scale equipment to better understand application challenges, and expanded survivability and longevity testing. Understanding the bridge between laboratory testing and full-scale deployment allows for better technique and risk mitigation for field scale testing.
Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-core environment. The most critical tests are equipped with instrumentation leads, which allow real-time data collection. However, because of the very harsh environment inside high-power MTR experiments, there are very few sensors that can survive and maintain their calibrated readings for the time periods required to obtain the high neutron doses needed for new fuels and materials qualification. As a result, sometimes sponsoring programs are forced to accept low reliability of sensors, collecting useful data for only part of the experiment duration. The work described herein is based on the observation that MTRs normally run at constant power and the corresponding conditions within reactor experiments typically evolve relatively slowly. Therefore, even one or two measurements per day would provide a complete and representative data set. With this in mind, INL has embarked on a program to develop a mechanism capable of pushing a very small-diameter sensor (typically a thermocouple or optical fiber) into the location to be measured, leave the sensor for roughly 60 seconds to allow it to reach equilibrium and transmit the signal, then pull it up and away from the high neutron flux and high-temperature region. Small-diameter capillary tubes, up to 8 m long, are used to guide the sensors to the appropriate locations. These capillary tubes serve as essentially very deep, thin-walled thermowells. The distance a thermocouple or optical fiber would need to traverse is on the order of 40 - 80 cm. By adopting this infrequent cycling strategy, the thermocouple or optical fiber would spend only a few hours in the high-neutron flux/high-temperature environment over the duration of even the longest irradiation experiment. To date, INL has developed two styles of drive mechanisms. The first is based on friction drive wheels which drive the sensors in a manner similar to a small MIG welder. This has the advantage of being able to accommodate a very long insertion length. The second is based on a ball screw drive and has the advantages of positive attachment and being able to move more than one sensor at a time. Both drive mechanisms have been fabricated and tested in a laboratory setting. Both systems can handle hard mineral insulated cable (such as thermocouples) or optical fibers encased in small diameter tube. The sizes tested to date are 1 - 1.6 mm diameter. Work in this area is ongoing with an eye toward demonstration in the Massachusetts Institute of Technology's MITR reactor, followed by deployment in an ATR irradiation experiment.
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Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-core environment. The most critical tests are equipped with instrumentation leads, which allow real-time data collection. However, because of the very harsh environment inside high-power MTR experiments, there are very few sensors that can survive and maintain their calibrated readings for the time periods required to obtain the high neutron doses needed for new fuels and materials qualification. As a result, sometimes sponsoring programs are forced to accept low reliability of sensors, collecting useful data for only part of the experiment duration. The work described herein is based on the observation that MTRs normally run at constant power and the corresponding conditions within reactor experiments typically evolve relatively slowly. Therefore, even one or two measurements per day would provide a complete and representative data set. With this in mind, INL has embarked on a program to develop a mechanism capable of pushing a very small-diameter sensor (typically a thermocouple or optical fiber) into the location to be measured, leave the sensor for roughly 60 seconds to allow it to reach equilibrium and transmit the signal, then pull it up and away from the high neutron flux and high-temperature region. Small-diameter capillary tubes, up to 8 m long, are used to guide the sensors to the appropriate locations. These capillary tubes serve as essentially very deep, thin-walled thermowells. The distance a thermocouple or optical fiber would need to traverse is on the order of 40 - 80 cm. By adopting this infrequent cycling strategy, the thermocouple or optical fiber would spend only a few hours in the high-neutron flux/high-temperature environment over the duration of even the longest irradiation experiment. To date, INL has developed two styles of drive mechanisms. The first is based on counter-rotating wheels which drive the sensors in a manner similar to a small MIG welder. This has the advantage of being able to accommodate a very long insertion length. The second is based on a ball screw drive and has the advantages of positive attachment and being able to move more than one sensor at a time. Both drive mechanisms have been fabricated and tested in a laboratory setting. Both systems can handle hard mineral insulated cable (such as thermocouples) or optical fibers encased in small diameter tube. The sizes tested to date are 1 - 1.6 mm diameter. Work in this area is ongoing with an eye toward demonstration in the Massachusetts Institute of Technology's MITR reactor, followed by deployment in an ATR irradiation experiment.
Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-core environment. The most critical tests are equipped with instrumentation leads, which allow real-time data collection. However, because of the very harsh environment inside high-power MTR experiments, there are very few sensors that can survive and maintain their calibrated readings for the time periods required to obtain the high neutron doses needed for new fuels and materials qualification. As a result, sometimes sponsoring programs are forced to accept low reliability of sensors, collecting useful data for only part of the experiment duration. The work described herein is based on the observation that MTRs normally run at constant power and the corresponding conditions within reactor experiments typically evolve relatively slowly. Therefore, even one or two measurements per day would provide a complete and representative data set. With this in mind, INL has embarked on a program to develop a mechanism capable of pushing a very small-diameter sensor (typically a thermocouple or optical fiber) into the location to be measured, leave the sensor for roughly 60 seconds to allow it to reach equilibrium and transmit the signal, then pull it up and away from the high neutron flux and high-temperature region. Small-diameter capillary tubes, up to 8 m long, are used to guide the sensors to the appropriate locations. These capillary tubes serve as essentially very deep, thin-walled thermowells. The distance a thermocouple or optical fiber would need to traverse is on the order of 40 – 80 cm. By adopting this infrequent cycling strategy, the thermocouple or optical fiber would spend only a few hours in the high-neutron flux/high-temperature environment over the duration of even the longest irradiation experiment. To date, INL has developed two styles of drive mechanisms. The first is based on counter-rotating wheels which drive the sensors in a manner similar to a small MIG welder. This has the advantage of being able to accommodate a very long insertion length. The second is based on a ball screw drive and has the advantages of positive attachment and being able to move more than one sensor at a time. Both drive mechanisms have been fabricated and tested in a laboratory setting. Both systems can handle hard mineral insulated cable (such as thermocouples) or optical fibers encased in small diameter tube. The sizes tested to date are 1 – 1.6 mm diameter. Work in this area is ongoing with an eye toward demonstration in the Massachusetts Institute of Technology’s MITR reactor, followed by deployment in an ATR irradiation experiment.
This report outlines strategies for Weatherization and Intergovernmental Programs Office (WIP) investment aimed at improving state energy office programs. The project was funded by WIP within the U.S. Department of Energy (DOE) office of Energy Efficiency and Renewable Energy (EERE). WIP aims "to enable strategic investments in energy efficiency and renewable energy technologies through the use of innovative practices across the United States in partnership with a wide range of stakeholders, including state and local organizations and community-based nonprofits." Across its programs, WIP has created thousands of jobs, maintains an investment ratio of 4.1 to 1, and increases U.S. economic output by 1.2 billion a year. This project was funded in the FY21 budget cycle and originally focused on improving WIP energy efficiency funding and programs. The project gathered information from various state energy offices to understand how WIP funding and processes can be optimized to improve state-level energy efficiency programs. We propose that the conclusions are also broadly applicable to weatherization and renewable energy deployment programs.
The term “ocean platform” is used to reference everything from stationary, typically moored, buoys to mobile water vehicles, whether they operate on the ocean’s surface or underwater. For certain applications for which relatively stationary station-keeping conditions are desired, the use of mooring systems is not always a viable alternative either for economic, environmental, regulatory, or otherwise practical reasons, or a combination thereof, (e.g., short deployments, sensitive ecosystems, very deep project sites). Maintaining a platform at a single waypoint or reference location without being moored would require additional control systems and a power source to counteract the drift forces that would naturally displace it. Mobile platforms, which are usually untethered except for remotely operated vehicles, typically require energy input to power their station-keeping capabilities so that they hold or control their location in the ocean. Currently, most of these platforms use combustion engines or batteries for this purpose, which, depending on the specific systems, may be costly, pollute the environment, or create limitations on the length of the deployment. However, powering this kind of platforms with surrounding renewable resources (waves, currents, winds, or sun) has been identified as a promising solution to expand their application. The intent of this report is to investigate station-keeping methods for various ocean platforms that are not moored or otherwise anchored to the ocean floor, or another platform or vessel, paying particular interest to technologies that use marine renewable resources to power their operation, because that is of particular interest to the U.S. Department of Energy’s Powering the Blue Economy (PBE) initiative. As a first step, 72 articles and technical reports related to mooringless station-keeping methods were collected for review. The preliminary literature review provided a broad overview of common themes across the literature from which a descriptive methodology for analyzing various platforms was developed. That is, station-keeping methods were categorized based on their predominant energy source and consumption (renewable, nonrenewable, or hybrid if the platform uses renewable and nonrenewable resources equally), and their localization strategy (drift reduction, “path-planning or “waypoint-holding”). In addition, platform types were segregated into the following groups: buoys, surface drifters, and unoccupied surface vehicles (USVs); offshore renewable energy systems; and unoccupied underwater vehicles (UUVs). The main types of station-keeping methods encountered in this report achieve their intended localization strategy by means of drift mitigation, steering, and/or propulsion. Drift mitigation is commonly accomplished via drogues and sea anchors. Stand-along steering subsystems use control surfaces (e.g., ship rudder, wing sail, etc.) that react to ocean currents, waves, or winds to provide varying-degrees of course adjustments. Combined steering and propulsion subsystems include differential thrusters, directional thrusters separate from a primary thruster that cause the platform to pitch up/down or yaw clockwise/counterclockwise, or vectored thrusters that direct the propulsion in a range of directions relative to the platform’s local coordinate system. Propulsion is often achieved by running a motor and applying active control strategies but can also involve buoyancy shifts and using sails to generate lifting forces that propel a platform in a desired direction. Future research is primarily expected to take place in the form of a technoeconomic analysis that would aim to determine the technological viability, cost, and added value of mooringless station-keeping use cases identified through this research, including docking for UUV recharging or for georeferencing drifter buoys, deep-sea floating wind farms, U.S. Navy sonar arrays, and a Pacific Ocean wave buoy network.
Model predictive control (MPC) has been proven in simulations and pilot case studies to be a superior control strategy for large buildings. MPC can utilize the weather and occupancy schedule forecasts, together with the system model, to predict the future thermal behavior of the building and minimize the overall energy use and maximize thermal comfort. However, these advantages come with the cost of increased modeling effort, computational demands, communication infrastructure, and commissioning efforts. Thus a typical approach is to, often rapidly, simplify the building modeling and MPC optimization problem while paying a price of not reaching the full performance potential. It has been shown that by employing accurate physics-based models, MPC performance can be notably increased closer to its theoretical performance bound. However, implementation of such high-fidelity MPC in real buildings remains a challenge, resulting in a lack of successful field test studies. This work presents the methodology and field test demonstration of a computationally efficient implementation of the white-box MPC in an office building in Belgium. The detailed model of the building is based on first-principle physical equations. The deployment and supervision of MPC operation in a practical setting are supported by an automated cloud-based communication infrastructure. The motivating factor behind the cloud-based architecture is its compatibility with a commercially appealing control as a service concept. The building is equipped with a ground source heat pump (GSHP) and thermally activated building structures (TABS), where the combination of both is also known as GEOTABS. From a control perspective, GEOTABS buildings are particularly challenging systems due to large scale, complex heating, ventilation and air conditioning (HVAC) system, and slow dynamics with time delays. On the other hand, there is an increased potential for energy savings due to the high thermal mass, which acts as thermal storage. The MPC operation is demonstrated during the challenging transient seasons (switching between heating and cooling), and its performance is compared to a traditional rule-based controller (RBC). We provide a proof of concept of real MPC operation for the most difficult seasons with notable GSHP energy use savings equal to 53.5% and thermal comfort improvement by 36.9%. Other MPC applications found in the literature describe tests for only cooling or only heating, and up to now only for a black-box or a grey-box approach.
This talk will highlight recent advances at the SQMS Center in understanding and mitigating decoherence in superconducting quantum systems, focusing on both transmon qubits and 3D superconducting cavities. I will present results from systematic studies of materials and devices, aimed at identifying key sources of loss: including two-level systems (TLS), quasiparticles, and other noise mechanisms. These studies span microwave loss characterization in materials such as niobium, tantalum, aluminum, and their native oxides, as well as substrate losses in silicon and sapphire. Using both qubits and cavities, we disentangle subsystem contributions to loss and develop a hierarchy of mitigation strategies. Through these efforts, we have achieved transmon coherence times exceeding one millisecond. I will also present investigations into quasiparticle dynamics, including bursts observed in qubits located above ground and in the Gran Sasso underground laboratory. Additional studies explore temperature dependence to distinguish between TLS and quasiparticle losses, and reveal that applying a magnetic field can reduce temporal T1 fluctuations in transmons. Building on these results, we demonstrate a record-coherence two-cell cavity-qudit system with coherence times exceeding 20 milliseconds. By leveraging sideband interactions and error-resilient protocols including measurement-based correction and post-selection, we achieve high-fidelity quantum state control. This includes preparation of Fock states up to N = 20 with fidelities over 95%, and two-mode entanglement with coherence-limited fidelities reaching 99.9% after post-selection. These achievements position the SQMS platform as a powerful foundation for scalable quantum information processing and high-dimensional qudit encodings. Finally, I will discuss how these ultra-coherent systems are being deployed in emerging quantum sensing applications, including dark matter searches and gravitational wave detection.
Abstract Industrial microbes and bio-derived products have emerged as an integral component of the bioeconomy, with an array of agricultural, bioenergy, and biomedical applications. However, the rapid development of microbial biotechnology raises concerns related to environmental escape of laboratory microbes, detection and tracking thereof, and resultant impact upon native ecosystems. Indeed, though wild-type and genetically modified microbes are actively deployed in industrial bioprocesses, an understanding of microbial interactivity and impact upon the environment is severely lacking. In particular, the persistence and sustained ecosystem impact of industrial microbes following laboratory release or unintentional laboratory escape remains largely unexplored. Herein, we investigate the applicability of soil-sorghum mesocosms for the ecological risk assessment of the industrial microbe, Saccharomyces cerevisiae . We developed and applied a suite of diagnostic and bioinformatic analyses, including digital droplet PCR, microscopy, and phylogenomic analyses to assess the impacts of a terrestrial ecosystem perturbation event over a 30-day time course. The platform enables reproducible, high-sensitivity tracking of S. cerevisiae in a complex soil microbiome and analysis of the impact upon abiotic soil characteristics and soil microbiome population dynamics and diversity. The resultant data indicate that even though S. cerevisiae is relatively short-lived in the soil, a single perturbation event can have sustained impact upon mesocosm soil composition and underlying microbial populations in our system, underscoring the necessity for more comprehensive risk assessment and development of mitigation and biocontainment strategies in industrial bioprocesses.
Implementing accessible, authentic research experiences in introductory courses is challenging, particularly at institutions serving diverse student populations. To address this gap, we developed and deployed a Course-based Undergraduate Research Experience (CURE) focused on plant-microbe interactions in General Biology II at Northeastern Illinois University (NEIU), a minority-serving institution with a diverse student body. Students grew sugar beets (Beta vulgaris), extracted DNA from the rhizoplane, and used the Department of Energy Systems Biology Knowledgebase (KBase) for bioinformatic analysis to compare microbial relative abundance in fertilized versus unfertilized soil. Over five semesters, the CURE engaged 103 students and leveraged the intuitive KBase platform to make complex sequencing data accessible. Pre/post-course survey data revealed significant increases in student self-assessed research skills, including the ability to explain results and determine the types of data to collect. Furthermore, students reported significant gains in confidence related to experimental design and hypothesis development, alongside a strong increase in familiarity with KBase. Informal faculty feedback indicated high student engagement and appreciation for the real-world connections (e.g. food systems, agriculture, and health). This scalable, low-cost model effectively integrates data science tools into the foundational curriculum, demonstrating a potent strategy for boosting research skills and broadening participation in authentic scientific inquiry among diverse undergraduate students.
Optimization of the food supply chain (FSC) depends on reducing food waste, especially at the consumer stage, where a substantial portion of food is not eaten, but instead disposed of via landfill, incineration, or in-sink disposals. One key strategy is to increase the time that consumers have before food goes bad or expires. This study developed a framework to assess the efficacy of shelf-life extension (SLE) technologies for mitigating food loss and waste (FLW), such as packaging improvements. The impact flows through the entire FSC, reducing FLW, energy use, and other inputs at each stage. The framework and resulting calculator can be used to evaluate the impact of FLW reduction at any stage for any food commodity. As shown by two SLE cases, the calculator is valuable for policy-makers, government entities, and professionals, specifically those in marketing, business development, and capital projects teams, to comprehensively evaluate the impacts of FLW reduction technologies and practices. The framework and calculator are sensitive to the shape of the consumption curve, the fraction of inedible waste, and the current shelf life. The calculator was used to assess the impacts of the United States goal of reducing food waste by consumers through various SLE lengths. It was found that uptake of several near-ready-to-deploy SLE technologies would reduce annual food production demand by about 10–19 MMT and supply chain energy consumption by 240–410 PJ in the United States.
The only medication available currently to prevent and treat opioid overdose (naloxone) was approved by the US Food and Drug Administration (FDA) nearly 50 years ago. Due to its pharmacokinetic and pharmacodynamic properties, naloxone has limited utility under some conditions and would not be effective to counteract mass casualties involving large-scale deployment of weaponized synthetic opioids. To address shortcomings of current medical countermeasures for opioid toxicity, a trans-agency scientific meeting was convened by the US National Institute of Allergy and Infectious Diseases/National Institutes of Health (NIAID/NIH) on August 6 and 7, 2019, to explore emerging alternative approaches for treating opioid overdose in the event of weaponization of synthetic opioids. The meeting was initiated by the Chemical Countermeasures Research Program (CCRP), was organized by NIAID, and was a collaboration with the National Institute on Drug Abuse/NIH (NIDA/NIH), the FDA, the Defense Threat Reduction Agency (DTRA), and the Biomedical Advanced Research and Development Authority (BARDA). This paper provides an overview of several presentations at that meeting that discussed emerging new approaches for treating opioid overdose, including the following: (1) intranasal nalmefene, a competitive, reversible opioid receptor antagonist with a longer duration of action than naloxone; (2) methocinnamox, a novel opioid receptor antagonist; (3) covalent naloxone nanoparticles; (4) serotonin (5-HT)1A receptor agonists; (5) fentanyl-binding cyclodextrin scaffolds; (6) detoxifying biomimetic “nanosponge” decoy receptors; and (7) antibody-based strategies. These approaches could also be applied to treat opioid use disorder.