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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Advances in Solutions to Improve the Energy Performance of Agricultural Greenhouses: A Comprehensive Review

The increasing global population and the challenges faced by the food production sector, including urbanization, reduction of arable land, and climatic extremes, necessitate innovative solutions for sustainable agriculture. This comprehensive review examines advancements in improving the energy performance of agricultural greenhouses, highlighting innovations in thermal and energy efficiency, particularly in heating and cooling systems. The methods include a systematic analysis of current technologies and their applications in optimizing greenhouse design and functionality. Key findings reveal significant progress in materials and techniques that enhance energy efficiency and operational sustainability. The review identifies gaps in the current knowledge, such as the need for more research on the economic viability of new materials and the development of predictive models for various climatic conditions. The conclusions emphasize the importance of integrating renewable energy technologies and advanced control systems to achieve energy-efficient and sustainable agricultural practices

Castro, Rodrigues Pascoal↗
DOE OSTI2026

Radiation-front position control with scenario adjustments during the first tungsten divertor operation on KSTAR

This study reports the first experimental demonstration of a radiation front-based detachment control system during the initial tungsten divertor campaign in KSTAR. The system employs real-time infrared video bolometry with tomographic reconstruction to track the inboard radiation-front position, which served as a feedback variable for impurity seeding. In a baseline scenario without pre-D 2 fueling, tungsten accumulation triggered core radiation surges and repetitive H–L back transitions. In contrast, with pre-D 2 fueling, detachment was stabilized by maintaining higher edge-localized mode frequency, suppressing core tungsten buildup, and reducing sputtering, thereby enabling sustained feedback control. The suitability of the inboard front position as a detachment control metric was confirmed by its correlation with the degree of detachment: with D 2 fueling the relation exhibited a broad hysteresis loop characteristic of momentum-loss-dominated detachment, whereas with N 2 fueling, it became narrow and nearly vertical, consistent with radiation-loss-dominated pinning. These results provide a proof-of-principle for radiation-front-based detachment control, demonstrating its viability as a control approach. At the same time, these results clarify that the detachment outcomes were further conditioned by impurity transport characteristics of tungsten divertor operation in KSTAR, providing guidance for future scenario optimization and controller development in tungsten devices.

KSTAR↗
DOE OSTI2026

Lightfall v0.0.1

Lightfall is a desktop application for synchrotron beamline instrument control, data acquisition, and live analysis at the Advanced Light Source (ALS). Built on Python and Qt, it provides a native graphical interface for operating beamline hardware, configuring and executing experimental scans, and visualizing results in real time. Key features include direct integration with EPICS control systems, a built-in electronic logbook, remote beamline access over secure tunnels, and an interprocess communication (IPC) architecture that coordinates with external analysis applications via ZMQ and EPICS process variables. This IPC approach allows Lightfall to orchestrate specialized analysis tools—including GPU-accelerated streaming correlators—without embedding them, avoiding the dependency conflicts common in monolithic scientific software platforms. Compared to prior approaches such as Xi-CAM's plugin-based architecture, Lightfall's design cleanly separates instrument control from domain-specific analysis, enabling feedback-driven acquisition where live analysis results can adjust scan parameters during an experiment. Its native Qt interface provides responsive performance for real-time data visualization that web-based alternatives struggle to match. Lightfall is designed for use by beamline scientists and staff operating synchrotron instruments at national user facilities.

Pandolfi, Ronald [Lawrence Berkeley National Labor↗
DOE OSTI2025

Entropy of the Quantum–Classical Interface: A Potential Metric for Security

Hybrid quantum–classical systems are emerging as key platforms in quantum computing, sensing, and communication technologies, but the quantum–classical interface (QCI)—the boundary enabling these systems—introduces unique and largely unexplored security vulnerabilities. This position paper proposes using entropy-based metrics to monitor and enhance security, specifically at the QCI. We present a theoretical security outline that leverages well-established information-theoretic entropy measures, such as Shannon entropy, von Neumann entropy, and quantum relative entropy, to detect anomalous behaviors and potential breaches at the QCI. By linking entropy fluctuations to scenarios of practical relevance—including quantum key distribution, quantum sensing, and hybrid control systems—we promote the potential value and applicability of entropy-based security monitoring. While explicitly acknowledging practical limitations and theoretical assumptions, we argue that entropy-based metrics provide a complementary approach to existing security methods, inviting further empirical studies and theoretical refinements that can strengthen future quantum technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine Learning for Optimized Polarization at Jefferson Lab

Polarized cryo-targets and polarized photon beams are widely used in experiments at Jefferson Lab. Traditional methods for maintaining the optimal polarization involve manual adjustments throughout data taking by human shift takers. This may introduce some level of inconsistency simply due to the wide variety of experience and expertise of the shift takers themselves. Implementing machine learning-based control systems can improve the stability of the polarization without relying on human intervention. The cryo-target polarization is influenced by temperature, microwave energy, the distribution of paramagnetic radicals, as well as operational conditions including the radiation dose. Diamond radiators are used to generate linearly polarized photons from a primary electron beam. The energy spectrum of these photons can drift over time due to changes in the primary electron beam conditions and diamond degradation. As a first step towards automating the continuous optimization and control processes, uncertainty aware surrogate models have been developed to predict the polarization based on historical data. This talk will provide an overview of the use cases and models developed, highlighting the collaboration between data scientists and physicists at Jefferson Lab.

Jeske, Torri [Thomas Jefferson National Accelerato↗
DOE OSTI2026

AI-Ready Semantic Infrastructure for CEBAF: From CED to PALS Knowledge Graphs

JLab and PNNL are jointly developing an AI-ready data ecosystem that exposes the Continuous Electron Beam Acceleration Facility’s (CEBAF’s) operational configuration, lattice description, and control-system channels to agentic optimization frameworks through a standards-based semantic layer. The effort integrates the existing facility-specific CEBAF Element Database (CED) with extensions of the emerging facility-agnostic Particle Accelerator Lattice Standard (PALS) to produce a knowledge graph (KG) containing coherent, machine-interpretable views of devices, signals, and regions. With this KG, CEBAF’s setpoints, readbacks, and device hierarchies become queryable using a uniform declarative graph query language (e.g., Neo4j Cypher), providing intents and inspectable semantics suitable for agentic control. The resulting graph-backed interfaces will allow autonomous agents to retrieve authoritative machine configurations, reason over device- and signal-level relationships, and execute tuning and diagnostic workflows without bespoke CEBAF-specific logic, thereby delivering a scalable pathway from operational data to trustworthy agentic accelerator tuning frameworks.

Zhang, He [Thomas Jefferson National Accelerator F↗
DOE OSTI2025

Enhancing Security and Resiliency in Operational Technology Environments Through Network Slicing and Federated Learning

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation. The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, security, and architectural implications of introducing network slicing into traditionally static OT infrastructures such as Industrial Control Systems (ICS) and SCADA. Through simulated deployments and case studies, the research demonstrates how slicing enables better isolation between critical and non-critical services, thereby improving response time, throughput, and security in sensitive environments. The second question considers: How to dynamically implement network slicing and take advantage of network resources towards integrating decentralized machine learning? In response, this thesis proposes a framework that combines Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Federated Learning (FL) to enable real-time analytics while maintaining data locality. The proposed approach reduces the burden on centralized infrastructure and minimizes privacy risks by supporting on-site training of models across distributed OT nodes, coordinated through dynamically allocated network slices. The third focus explores: How slicing helps to increase the resiliency of OT networks through the orchestration of a dynamic DMZ? To answer this, the thesis presents a method for creating and managing Dynamic Demilitarized Zones (DMZs) using network slicing. This enables flexible and automated isolation of sensitive subsystems during threat scenarios or high-risk operations. Coupled with intelligent orchestration and containerized security services, the dynamic DMZ significantly enhances the system's ability to respond to cyber incidents without halting production. Ultimately, this thesis contributes a comprehensive architecture that blends network slicing with machine learning, secure segmentation, and automation, paving the way for resilient, adaptive, and intelligent OT environments. Performance evaluations across multiple scenarios show improvements in system reliability, threat response time, model accuracy, and resource utilization, providing a strong foundation for future industrial automation systems.

Rodiles Delgado, Brian G↗
DOE OSTI2025

Active Humidity Control Chamber for Desorption Electrospray Ionization-Mass Spectrometry Imaging Applications

Ambient ionization techniques enable mass spectrometry (MS) to expand into broader experimental contexts, although it is increasingly clear that results are influenced by the environmental conditions at the site of sampling. Desorption electrospray ionization (DESI), in particular, is affected by variations in relative humidity (RH) levels. Here we describe the design, development, and construction of an environmental control chassis that can actively modulate RH within ± 3% of user-defined set points across a broad humidity range (15%–70% RH). Preliminary characterization demonstrated differential analyte responses across a range of set points, with observed enhancement of leucine-enkephalin, sulfadimethoxine (negative mode), and maltose (positive mode) in response to increased humidity. The measurable differences in analyte signals across discrete humidity set points underscore the importance of environmental control in ambient ionization strategies. The humidity control system outlined here can be translated to other DESI platforms, with construction information provided herein.

DESI↗
DOE OSTI2024

Design and development of the magnetic diagnostic systems for the first operational phase of the SMART tokamak

A set of magnetic diagnostics has been designed, manufactured, and calibrated for the first operational phase of the small aspect ratio tokamak. The sensor suite comprises of Rogowski coils; 2D magnetic probes; and poloidal, saddle, and diamagnetic flux loops. Here, a set of continuous Rogowski coils has been manufactured for the measurement of plasma current and induced eddy currents in conductive elements. A set of flux loops and magnetic probes will be used as input for the reconstruction of the magnetohydrodynamic equilibrium. The quantity and position of these sensors have been verified to be sufficient with synthetic equilibrium reconstructions using the equilibrium fitting code and baseline scenarios computed with the Fiesta code. These sensors will also be used as input for the real-time control system, and magnetic probes will be used for the detection of plasma instabilities. The calibration procedure for the magnetic probes is described, and the results are shown. The signal conditioning and data acquisition systems are described.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

An Educational Program on Concentrated Solar Power and Heliostats for Power Generation and Industrial Processes

The objective of this project was to design and implement a comprehensive educational and applied research program in Concentrated Solar Thermal Power (CSTP) and heliostat technologies at Northeastern University. In alignment with the U.S. Department of Energy's Heliostat Consortium (HelioCon) goals, the project aimed to expand student and public understanding of CSTP systems while simultaneously contributing to workforce development and the broader decarbonization strategy. A particular emphasis was placed on integrating hands-on student design projects and publicly disseminating educational content relevant to CSTP systems. The project addressed a critical gap in renewable energy education: CSTP and heliostats, despite their importance in utility-scale solar energy, are rarely included in standard mechanical engineering programs. This project established new pathways for students to engage with the topic through the creation of a 4-credit graduate/senior elective course, development of five industry-facing short courses, and the inclusion of CSTP-based capstone design projects. Over two academic years, 36 students across six senior design teams developed and tested technologies such as deformable heliostats, beacon-based tracking systems, and solar-powered pyrolizers for biomass-to-biochar conversion. Concurrently, 30 undergraduate and graduate students were enrolled in the new academic course centered around CSTP principles. To ensure the relevance and accessibility of the short course content, the project team engaged with industry professionals, technical policy stakeholders, and potential course participants through structured surveys and informal consultations. Feedback from 28 respondents guided the structure, length, and delivery format of the courses - resulting in a modular design broken into five workshops. The feedback emphasized the need for flexible, asynchronous delivery and practical case studies, particularly in areas such as heliostat control, thermal storage, and solar fuel production. This engagement helped align the courses with the evolving knowledge demands of the renewable energy workforce and ensured that participants from both technical and policy backgrounds could meaningfully benefit from the material. The research and educational activities advanced the understanding of heliostat control systems, optical performance under misalignment, and thermal system integration in solar-driven pyrolysis applications. Methods and designs explored in this project proved to be both technically effective and economically feasible at the lab scale. Prototypes were constructed using commercially available components and custom-fabricated elements, demonstrating that meaningful performance improvements can be achieved with modest material and fabrication costs, supporting the feasibility of student-led research in this field. The public benefit of this project is twofold. First, it cultivates a pipeline of engineers trained to be familiar with CSTP principles, an essential workforce need identified by the Department of Energy for achieving its 2030 cost and deployment targets. Second, it contributes openly accessible educational materials, course content, and experimental frameworks to the broader community, enabling other institutions to adopt or adapt similar programming. Through outreach activities, curriculum integration, and technical exposure, this project contributes to a more informed and capable renewable energy workforce while supporting innovation in heliostat and CSTP system design. A new technical report is being prepared to document the development of the course and its outcomes, with plans to publish it in the ASME Open Access Journal of Engineering to ensure global accessibility, free of cost.

14 SOLAR ENERGY↗
DOE OSTI2026

Robust Heat-Flux Sensors for Coal-Fired Boiler Extreme Environments

In this project, robust heat-flux measurement systems were developed. The heat-flux sensors utilize thermoelectric effects to directly transduce the heat-flux inputs to analog electrical voltage signals. They were constructed from dedicated materials that can withstand temperatures of at least 1000°C and maintain adequate performance at these conditions for prolonged periods of time. The proposed approaches took into account numerous considerations, including system cost, sensor head resilience, sensor footprint, data accuracy, response time, and maintenance requirements. Through modern thermoelectric materials design, methodical materials selection and rigorous testing in materials characterization labs and medium-scale fire research facilities, we have demonstrated functioning laboratory prototypes, upon which one could base industrial heat-flux sensing platforms capable of operating in the challenging high-temperature, corrosive environments of the boilers of coal-fired power plants. A distributed sensor array for heat-flux measurements throughout the furnace water-wall, the superheater area and the economizer coils can provide critical data for the power plant control systems to increase efficiency, improve safety and reduce down times. For example, the combined heat-flux sensor/control systems can contribute to the optimization of burner and boiler operations under flexible loads, the optimization of heat-exchange conditions and overall reduction of heat rate and emissions, the prediction of imminent overheating conditions, and the optimization of the soot-blowing protocols.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Reducing Methane Emissions with an Engine Fuel Reformer

Southwest Research Institute (SwRI) designed and demonstrated a compact, plasma-based fuel reformer designed to reduce methane leaks from natural-gas engines used in pipeline compression and industrial power applications. The system uses pulsed electrical discharges to convert a small portion of methane (CH4) into hydrogen (H2) and light hydrocarbons, promoting more complete combustion and reducing unburned methane in the exhaust. The reformer was evaluated through bench-scale testing, high-flow facility testing, and engine-integration testing on representative low-speed and high-speed natural-gas engines. Measured methane-leak reductions ranged from 1 to 27 percent, depending on engine type and operating condition, while reformer energy consumption remained below one percent of total engine output. Design refinements addressed thermal and electrical challenges, resulting in reliable operation with minimal impact on engine control systems. The project achieved all performance objectives within budget and schedule, produced a patentable pulsed-power system design, and established a scalable, energy-efficient pathway for mitigating methane leaks from existing natural-gas engine infrastructure.

03 NATURAL GAS↗
DOE OSTI2025

Optimization-Based Data-Driven Approach for Detecting Fault Location in Power Systems

In grids with large penetration of converterinterfaced resources (CIRs), measurements of voltage, current, and line parameters can fluctuate significantly during fault conditions. These fluctuations, combined with complex network topologies and extensive system branching, make accurate fault location challenging. Faults, such as short circuits, can cause prolonged outages with serious socio-economic impacts, highlighting the need for rapid fault identification to minimize downtime. However, current fault detection methods—such as relays and digital fault recorders—often relay information too slowly, impeding swift corrective action. Given the limited availability of high-resolution phasor measurement units, this paper introduces an optimization-based observer to estimate fault locations, grid line parameters, and voltages using local CIR measurements. To preserve the confidentiality of CIRs and enhance estimation accuracy, this study uses a black-box model of CIRs. This bottom-up, event-driven approach can enhances protection and control systems through optimized and real-time fault detection. Simulation results show that the optimization-based data-driven observer can accurately detect fault locations and estimate grid states and parameters, providing valuable insights for utilities and operators in grid applications.

Subedi, Sunil [ORNL] (ORCID:000000034069090X)↗
DOE OSTI2025

A High-Current Pulsed Prototype Power Supply

The Accelerator Controls Operations Research Network (ACORN) project aims to modernize the accelerator control system and replace aging power supplies at Fermilab. As part of this effort, outdated RF ferrite bias power supplies will be redesigned. These power supplies are essential for tuning the resonant frequency of RF cavities by delivering programmable current outputs of up to 2500 A and voltages ranging from −10 V to +35 V. They operate at a repetition rate of 15 Hz in the Booster ring, and 1 Hz at the Main Injector ring. The power supplies utilize a bank of transistors in the linear region, connected in parallel with the load, to actively regulate the output current from a 12-pulse SCR bridge. To support this upgrade, a new bias power supply topology was developed as proof of concept. The design utilizes an IGBT Hbridge operating in Pulse Width Modulation (PWM) mode, controlled by a microcontroller. A prototype, constructed using spare components, successfully delivered an output current of 500 A at a repetition rate of 15 Hz during initial testing. The circuit's bandwidth was measured at 480 Hz, highlighting opportunities for further optimization in the controller design to achieve the target bandwidth of 2 kHz.

Bullman, Austin [ORNL]↗
DOE OSTI2025

Equipment List Comparing Balance of Plant Containing a Heat Pump against a Reference Electricity Generating Plant

Approximately two-thirds of U.S. energy consumption in the industrial and transportation sectors relies on fossil fuels. These sectors require high-quality heat, i.e., thermal energy at very high temperatures, for molecular transformation processes. The Integrated Energy Systems (IES) program aims to assess the economic potential of utilizing nuclear-grade heat from Advanced Reactors (ARs) to meet the high-quality heat demands. By having industrial processes (IPs) supplied with nuclear-generated heat, manufacturers could benefit from more stable and potentially lower energy costs, reducing reliance on volatile fossil fuel markets. The main outcome of the FY24 research was the thermodynamic assessment and gap analysis of steam generation for IP applications. The study completed in June 2024 demonstrated that the required steam temperatures could be achieved by integrating a heat pump into an AR power plant. After identifying the thermal demands of target IPs, multiple balance of plant configurations for the Xe-100 reactor by X-energy, incorporating a heat pump, were analyzed. Their technical feasibilities were assessed, including the design of suitable axial compressors for these applications. Comparative performance analysis showed that thermal efficiency alone is insufficient to evaluate system suitability. To address this, a new indicator (heat factor) was introduced in the report released in September 2024 to quantify the low-quality thermal power needed to produce one unit of high-quality heat for the IP. Results showed that integrating heat pumps into Rankine cycles enables higher steam temperatures, though at the expense of increased thermal energy input. This report builds upon and completes the foundational work previously undertaken. It focuses on the design of two Balance of Plant (BOP) configurations, both based on Rankine energy conversion cycles: “Case 1”, which involves electricity generation only, and “Case 2”, which combines electricity and high-temperature heat generation for industrial use. For each configuration, a comprehensive equipment list was developed, detailing all major components such as turbines, compressors, heat exchangers, pumps, and control systems. These lists will serve as the basis for future comparative cost analyses, with the goal of assessing the number and type of components required to integrate a heat pump into the Rankine cycle and to establish a heat transport system capable of delivering thermal energy from the nuclear plant to an industrial facility. Using the constitutive equations presented in the June 2024 milestone, the operating conditions of all BOP components for both “Case 1” and “Case 2” were evaluated. These parameters—such as temperature, pressure, mass flow rate, and steam quality—served as the basis for calculating the associated thermal and mechanical power flows. The net power required from the heat pump to raise the steam temperature to the target level was also determined. The thermodynamic performance of the configuration was then assessed using the heat factor metric. The key outcome of this analysis is a comparative table that presents the equipment that was used in the “Case 1” and “Case 2” configurations. This study offers a preliminary comparison of the two designs, providing insight into the impact of integrating a heat pump in terms of component requirements and thermal efficiency. This equipment list, together with the evaluated operating conditions, also serves as a foundation for the economic analyses scheduled for the current fiscal year.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Securing the Modern Grid: Federal Investments, Digitization, and Supply Chain Strategy

Across the United States (U.S.) grid expansion and modernization is underway, paving the way for accelerated load growth and intelligent resource management. Digitization of the grid is supported by several state and federal programs, providing support for utilities installing advanced metering infrastructure (AMI), AI-powered analytics systems, battery energy storage systems (BESS), and distributed energy resource management systems (DERMS) to transform the grid from a one-way power delivery system into an intelligent, responsive network that will enable faster load growth and power expansion of data centers for advanced artificial intelligence (AI) applications. The digital transformation of America's grid presents opportunity for increased efficiency and resiliency but also introduces new digital risks that require careful management. Digital equipment often contains several vulnerabilities such as unencrypted communication protocols, and persistent remote access capabilities that could be exploited to manipulate device settings, coordinate service disruptions, or inject false data into grid operations. These digital risks become particularly important as the grid must rapidly scale to support AI-driven data centers, which the administration has identified as essential for maintaining U.S. technological leadership and economic competitiveness. These vulnerabilities are compounded by supply chain realities: Chinese manufacturers currently produce 70-90% of essential grid components including inverters, batteries, and control systems, with the U.S. lacking domestic manufacturing capacity for critical assets like extra-high voltage transformers. Recent federal legislation has established Foreign Entity of Concern (FEOC) restrictions to address these risks, requiring projects to achieve escalating thresholds of non-FEOC content to receive tax credits while utilities work to expand sourcing channels for their supply chains and strengthen security measures. These restrictions arrive precisely when utilities face unprecedented electricity demand growth driven by the rapid growth in data centers, creating a considerable challenge: rapidly expanding infrastructure while navigating complex compliance requirements while lacking viable alternatives for many critical components. Idaho National Laboratory (INL) and its partners have developed practical approaches to help utilities navigate these intersecting challenges as they leverage federal investment to strengthen and grow the grid. These solutions include Cyber-Informed Engineering (CIE) principles that build resilience directly into systems, the Cirrus tool for secure cloud migration, and enhanced procurement guidance that embeds security requirements throughout equipment lifecycles. Federal initiatives, such as the Technical Assistance for Digital Assurance (TADA) project, provide direct support to utilities implementing these approaches while facilitating knowledge sharing across the industry. While these tools and frameworks cannot eliminate all risks inherent in foreign supply chain dependencies, they offer pragmatic pathways for strengthening security posture without sacrificing the deployment momentum essential to meeting surging electricity demand. Ultimately, securing America's digital energy infrastructure demands dedicated coordination across multiple fronts: building domestic supply chains, implementing robust digital assurance practices, and maintaining the aggressive modernization timeline necessary for reliability, resilience, and energy independence.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak

The Imaging Neutral Particle Analyzer (INPA) at DIII-D is a diagnostic system used to accurately resolve the energy and spatial distributions of fast ions in fusion plasmas. A novel artificial intelligence (AI) technique named INPA-net is based on Reservoir Computing Networks and developed here to predict active and passive signals produced by charge-exchange reactions from injected and edge-cold neutrals, respectively, in magnetically confined fusion plasmas. This model is trained using a set of 21 time domain signals between 0 s to 3.35 s that includes injected beam and thermal plasma information, and 6444 real 2D experimental images of the INPA in 12 plasma discharges at DIII-D. The trained neural network is able to forecast experimental images in real-time. The model achieves an R-squared value of 0.91, which is higher than the 0.83 value achieved by a simple linear regression model. This improvement highlights the model's enhanced predictive accuracy for measured images from the validation set. This AI approach is valuable due to its rapid response times and potential for integration into real-time plasma control systems. A version of this model capable of generating syntehic images would be useful for the real-time monitoring of fast-ion transport. A comprehensive sensitivity study reveals that INPA-net maintains high performance even with variations in the input parameters, indicating the model's robustness and reliability. While developed for the INPA, the underlying architecture is adaptable and may be applied to various 2D imaging diagnostics in fusion research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Unveiling the Stability of Encapsulated Pt Catalysts Using Nanocrystals and Atomic Layer Deposition

Platinum exhibits desirable catalytic properties, but it is scarce and expensive. Optimizing its use in key applications such as emission control catalysis is important to reduce our reliance on such a rare element. Supported Pt nanoparticles (NPs) used in emission control systems deactivate over time because of particle growth in sintering processes. Here, in this work, we shed light on the stability against sintering of Pt NPs supported on and encapsulated in Al 2 O 3 using a combination of nanocrystal catalysts and atomic layer deposition (ALD) techniques. We find that small amounts of alumina overlayers created by ALD on preformed Pt NPs can stabilize supported Pt catalysts, significantly reducing deactivation caused by sintering, as previously observed by others. Combining theoretical and experimental insights, we correlate this behavior to the decreased propensity of oxidized Pt species to undergo Ostwald ripening phenomena because of the physical barrier imposed by the alumina overlayers. Furthermore, we find that highly stable catalysts can present an abundance of under-coordinated Pt sites after restructuring of both Pt particles and alumina overlayers at a high temperature (800 °C) in C 3 H 6 oxidation conditions. The enhanced stability significantly improves the Pt utilization efficiency after accelerated aging treatments, with encapsulated Pt catalysts reaching reaction rates more than two times greater than those of a control supported Pt catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗