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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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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

AN EXPERIMENTAL STUDY OF FUEL SELECTION FOR A GASOLINE MULTI-MODE

In this study of fuels of different reactivities and their performance in a low-temperature combustion (LTC) engine. The engine was a 2.2L CRDI engine code D4HB provided by industry partner Hyundai America Technical Center inc. (HATCI). The engine was instrumented with in-cylinder pressure sensors allowing for monitoring of the combustion process. First, the engine was operated with standard US pump grade gasoline, Research Octane number (RON) 91 E10, to find initial operation conditions as well as control points of stable operation for daily checks on system conditions. Tested were 8 different fuel blends, the four base blends were provided by Phillips 66 (P66) with a RON value ranging from 60 to 91, and three other variations of these fuels were formulated by splash blending alcohols, iso-butanol, and ethanol, with the RON60 fuel and the final blend, was US RON91 E10 pump gasoline. Using these 8 fuels, the low load performance was analyzed allowing a selection of one of the fuels to be made to proceed with experimentation. To study the low load performance of each fuel, the load was decreased until combustion became too unstable to continue. This was done at 1500 rpm with all of the fuels. At 1200 rpm, the same methodology was applied, though all of the fuels were not tested, only the fuels which showed promise at 1500 rpm. Meaning that the lowest targeted load was achieved without any aid to the combustion process other than advancing combustion, increasing intake air temperature (IAT) for example was not done to maintain consistency across all fuels. The same methodology was applied at 800 rpm based upon the 1200 rpm results to see the ability of the engine to idle with the given fuel. It was found that fuel with a RON of 80 showed good low load performance while allowing for high load performance to be maintained. A blend of 25% Iso-Butanol and 75% RON 60 equated to a RON of 80 and showed good low load performance and showed peak indicated thermal efficiency (ITE) numbers of 46% at 17 bar indicated mean effective pressure (IMEP) and 2200 rpm.

02 PETROLEUM↗
DOE OSTI2020

Decommissioning of the V1 Nuclear Power Plant Jaslovske Bohunice - 20270

The paper focuses on introducing the approach and method applied for decommissioning of the V1 nuclear power plant (further the 'V1 NPP') located in Jaslovske Bohunice in the Slovak Republic, to the conference participants. The Paper outlines activities and steps necessary to comply with the requirements contained in the decision of the Slovak government from 1999 regarding the early shutdown of the V1 NPP. The company responsible for the V1 NPP decommissioning is Jadrova a vyradovacia spolocnost, a.s., (further JAVYS) with the State as a sole shareholder. On the basis of the V1 NPP Decommissioning Conceptual Plan, out of four assessed options, JAVYS selected the immediate decommissioning option (the 'IDO') for the V1 NPP. On the basis of multi-criteria analysis, the IDO specified the approach of immediate and continuous dismantling of equipment followed by demolition of buildings and preparation of the site for its further potential use. The IDO also included the summary of technical, environmental, legislative information as well as the determination of assumed costs for decommissioning of the V1 NPP. Prior to commencement of the V1 NPP decommissioning, within the process of termination of operation, activities connected with de-fuelling, monitoring, processing of historical waste and, not to forget, the process of obtaining the license for the V1 NPP decommissioning were executed. JAVYS decided to divide the decommissioning process into two stages. In 2011, JAVYS obtained a license for the first stage of the V1 NPP decommissioning. Within this stage, dismantling of inactive equipment, demolitions of inactive buildings, monitoring of systems and preparatory works for the upcoming stage of decommissioning were implemented. In 2015, in line with the license for the 2. stage of the V1 NPP decommissioning, the activities of decontamination and dismantling of contaminated and active equipment as well as demolitions of remaining buildings began. In this stage radioactive waste (RAW) produced during the decommissioning is to be processed. After decontamination, the material fulfilling the free release criteria is to be released into the environment, the site is to be cleared from the scope of the Atomic Act and released from the surveillance of the national regulators. Future application of certain methods and techniques for decontamination and dismantling in similar environments might bring an added value to the operators. Paper includes a description of best practices for Nuclear Power Plant VVER type reactors decommissioning. Schedule comparison is given to show how the critical path of certain projects is being managed. Application of the International Standard for Decommissioning Costing (ISDC) methodology provides a uniform method which enables comparison of cost estimates to other nuclear facilities under decommissioning. The cost estimate for the V1 NPP decommissioning has been continuously updated based on more advanced and detailed information on the decommissioning activities and projects. Monitoring of progress of the V1 NPP decommissioning is performed via four key-performance indicators. The Earned Value Management (EVM) methodology is used for evaluating time and cost aspect of decommissioning activities while waste conditioning index is calculated by comparing the actually produced waste to planned values of waste for a respective monitored period. Radiological safety index is assessed by comparison of the personnel records on the individual yearly doses received by each respective employee to the boundary value. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Operation Optimization using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation

Study region: The Dnieper and Dniester Rivers of Ukraine. Study focus: The ongoing conflict in Ukraine has caused disruptions to electricity generation, of which hydroelectric sources contribute approximately 9 % to the country’s needs. With the takeover of the Zaporizhzhia nuclear power plant by enemy forces, the loss of the Kakhovka hydroelectric dam, and the future impacts of the conflict on electricity generation unclear, it may be valuable for the Ukrainian government to better understand how it could leverage hydroelectric power sources in the near future. Unfortunately, measurements of river discharge throughout Ukraine ceased data collection in the late 1980’s to early 1990’s. To address this data gap, we developed a protocol that combined satellite-based time-series measurements of river width at seven locations throughout Ukraine from 2013 to 2023 with reanalysis data, climate-model predictions, and hydrologic models to both provide a means of monitoring a proxy for near-real-time discharge and also predict near-term (i.e., 2023–2030) hydrologic patterns for the region. New hydrological insights for the region: We ran new algorithms on 144 WorldView-2 and WorldView-3 satellite images to map rivers and extract width, one of which was validated against river gauge data located along the same river but in a neighboring country. Hydrologic models using two climate scenarios found minimal change in annual discharge at all sites, but magnitude and timing of peak discharge showed a moderate trend. The results suggest that hydropower is underutilized in Ukraine.

13 HYDRO ENERGY↗
DOE OSTI2021

Calendar Year 2020 Post-Closure Monitoring Report for Corrective Action Unit 99: Rainier Mesa/Shoshone Mountain, Underground Test Area, Nevada National Security Site, Nevada (Rev. 1)

This report presents the results of monitoring conducted for water quality, water levels, and institutional controls in calendar year (CY) 2020 by the U.S. Department of Energy (DOE), Environmental Management (EM) Nevada Program’s Underground Test Area (UGTA) Activity at Corrective Action Unit (CAU) 99, Rainier Mesa/Shoshone Mountain (RM/SM), at the Nevada National Security Site (NNSS), Nevada. This report also presents analytical laboratory results (including results of quality assurance/quality control samples, such as field duplicates [FDs]), and verification of use restrictions (URs), institutional controls, and water usage. Groundwater samples were collected; water levels were measured; and well site surveillance was conducted in support of the Underground Test Area Closure Report (CR) for Corrective Action Unit 99: Rainier Mesa/Shoshone Mountain, Nevada National Security Site, Nevada (DOE/EMNV, 2020). Rainier Mesa (RM) was the site of 61 underground nuclear tests, and Shoshone Mountain (SM) was the site of 6 underground nuclear tests. As a result of these activities, which took place from 1957 to 1992, radionuclides (RNs) were released in the subsurface in the vicinity of the detonations. These 67 underground nuclear tests are associated with 66 specific corrective action site (CAS) numbers, as listed in the Federal Facility Agreement and Consent Order (FFACO) (1996, as amended). Two tests, HURON LANDING and DIAMOND ACE (conducted simultaneously), are included within one CAS. The CR (DOE/EMNV, 2020) establishes the regulatory boundaries and regulatory boundary objectives, monitoring program, UR boundaries and URs, and other institutional controls agreed to by EM Nevada Program and the Nevada Division of Environmental Protection (NDEP) for closure of CAU 99. Regulatory boundaries for the RM/SM CAU were established to protect receptors of groundwater from RN contamination within the three downgradient groundwater basins that receive recharge from RM. The SM regulatory boundary was established to verify that RN contamination does not reach the lower carbonate aquifer below SM. The UR boundaries were established based on contaminant simulation forecasts from the flow and transport simulations with consideration of site-specific topographical controls. The URs associated with the UR boundaries were identified to protect onsite workers and the public from inadvertent exposure to contaminated groundwater as forecasted to occur assuming current conditions. The UR is intended to restrict activities that might expose workers to contaminated groundwater within the UR area. In addition to the URs, other institutional controls are established to monitor and limit access to groundwater. These include federal ownership and management in perpetuity, controlled access of the NNSS and surrounding areas, and reporting water use on the NNSS and surrounding hydrographic basins.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Role of Side Chains and Hydration on Mixed Charge Transport in n ‐Type Polymer Films

Abstract Introducing ethylene glycol (EG) side chains to a conjugated polymer backbone is a well‐established synthetic strategy for designing organic mixed ion‐electron conductors (OMIECs). However, the impact that film swelling has on mixed conduction properties has yet to be scoped, particularly for electron‐transporting (n‐type) OMIECs. Here, the authors investigate the effect of the length of branched EG chains on mixed charge transport ofn‐type OMIECs based on a naphthalene‐1,4,5,8‐tetracarboxylic‐diimide‐bithiophene backbone. Atomic force microscopy (AFM), grazing‐incidence wide‐angle X‐ray scattering (GIWAXS), and scanning tunneling microscopy (STM) are used to establish the similarities between the common‐backbone films in dry conditions. Electrochemical quartz crystal microbalance with dissipation monitoring (EQCM‐D) and in situ GIWAXS measurements reveal stark changes in film swelling properties and microstructure during electrochemical doping, depending on the side chain length. It is found that even in the loss of the crystallite content upon contact with the aqueous electrolyte, the films can effectively transport charges and that it is rather the high water content that harms the electronic interconnectivity within the OMIEC films. These results highlight the importance of controlling water uptake in the films to impede charge transport inn‐type electrochemical devices.

Chemistry↗
DOE OSTI2023

An Integrated Approach to Predicting Ash Deposition and Heat Transfer in Coal-Fired Boilers

The overall goal of this project is to develop via measurements and simulations an advanced online technology to predict, monitor and manage fireside ash deposition in a coal-fired boiler allowing for more efficient operations under a range of load conditions and fuel property variability. With this in place fuel sorting and blending can be done upstream and operations can be optimized to compensate for load and fuel properties. In support of this objective, three experimental campaigns were undertaken during the course of the project to measure ash deposition rates within the boiler at different fuel flow rates and its ash composition variability. Simulations of the experimental conditions representing actual geometry, operational scenarios in terms of air flow rates, coal flow rates as well as coal compositions, heating values, and particle size distributions were also carried out. Deposition rates were predicted using a unique particle kinetic energy and viscosity based ash deposition methodology whose validity was ascertained by comparing against deposition rate measurements for widely varying operating conditions and ash compositions in a lab-scale furnace. With a unique end-to-end combustion modeling methodology established and different simulation scenarios carried out, the results from our computational fluid dynamic (CFD) simulations in conjunction with the plant data summarized in this report were used to refine Microbeam Technology Incorporated’s MTI CSPI-CT Tool to predict and monitor fire-side ash deposition under a range of load conditions and fuel property variability in real time.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Real-time Condition Monitoring of Power Modules in Grid-tied Power Converter

This paper proposes and demonstrates a real-time condition monitoring method using gate driver integrated sensing circuits and sensor fusion algorithms. Power device on-state voltage (VDSon) measurement and junction temperature (Tj) sensing circuits with high noise immunity are built and integrated into an adaptive gate driver circuit for power modules. Considering the inherently noisy measurement environment of power converters, VDSon together with several other measurements are fused together to provide accurate descriptions of the stress and degradation of each power device. Furthermore, the gate driver circuit can actively control the turn-on gate voltage (VGSon) for each device based on the stress and degradation state. The proposed sensing circuits and power device condition assessment algorithms are implemented in a 75 KVA grid-tied power converter. This paper focuses on the sensing circuits hardware designs and testing in grid-tied power converter prototype and device stress index generation. More comprehensive results on the device state of health index generation will be presented in future work.

Fan, Junchong↗
DOE OSTI2025

Modeling High Current Pulsed Discharge in AA Battery Cathodes: The Effect of Localized Charging during Rest

During high current operation, substantial heterogeneity develops within battery cathodes, particularly when their thickness is large. Heterogeneity relaxation during subsequent rest is important for understanding battery performance under pulsed conditions. Localized charge balancing phenomena within batteries at zero net current are not well understood and merit investigation. In this work, the heterogeneity within cathodes of commercial alkaline Zn–MnO 2 batteries is measured during discharge and monitored during rest using energy dispersive X-ray diffraction (EDXRD). Significant gradients in protonation form during discharge and partially relax under rest. It is demonstrated that the proton gradient relaxation is through local redox activity at zero net current, where local (de)protonation works to redistribute charge across the cathode thickness. To support this redox-based relaxation, a fundamental kinetic study on prismatic MnO 2 cathodes is conducted to determine an appropriate model to describe both discharge and charge kinetics of MnO 2 . These kinetics are incorporated into a computational model to simulate the proton gradient formation and partial relaxation under identical discharge conditions as the operando EDXRD experiments. Model and experimental data are found to be in excellent agreement, correctly predicting localized charge balancing at rest.

Batteries↗
DOE OSTI2024

Multi-Parameter Optical Fiber Sensing of Humidity, CH4, CO2, and Corrosion

In this work, the previously demonstrated capability of the optical fiber sensor (OFS) for successful monitoring of humidity has been extended to monitor the humidity, CH4, and CO2 with different gas composition based on the strain produced along the single mode fiber (SMF) sensor. This is enabled by absorption of H2O/gases on to the commercially available polyacrylate coated jacketed portion of the fiber resulting a change in strain. Under equilibrium, a differential microstrain was observed along the jacketed portion of the SMF with N2, CH4, and CO2 at different humidity conditions, while the unjacketed portion of the fiber was used only for sensing pressure/temperature induced strain. In case of N2 at 800 psig pressure, the observed microstrain was approximately 80, 65, 50 and 35 µε at 100, 75.0, 46.8, and 23.4 RH% respectively. Comparatively, a microstrain of approximately 95 µε was observed with 100% RH CH4 which demonstrates that SMF produces a measurable CH4 response alongside water. Similarly, the observed microstrain with CO2 was approximately 105, 90, 85, 80, and 70 µε at 100, 75.0, 46.8, 23.4, and 0 RH% respectively. Linear regression and principal component analysis of these dataset provided deconvolution of the impact of strain from H2O, CH4, and CO2 enabling good cross sensitivity. Additionally, modified OFS comprising of Fe coated fiber section was employed to monitor corrosion, using Fe as corrosion proxy, under harsh corrosive environment.

Mainali, Badri↗
DOE OSTI2021

Data-driven estimation of energy consumption for electric bus under real-world driving conditions

Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, we developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. Here, a K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.

Artificial neural network↗
DOE OSTI2024

A prediction model of failure threshold for shear deformation in a Zr-based bulk metallic glass

The failure of bulk metallic glasses (BMGs) during plastic deformation at room temperature is abrupt and instantaneous, while the analysis of precursor information based on avalanche events helps predict catastrophic failure. An acoustic emission (AE) signal can provide accurate precursor information for material failure, due to its sensitive and high fast calculation ability. In the current study, AE monitoring tests are carried out during uniaxial compression tests of BMGs at different strain rates. The AE experimental failure threshold, E max , is proposed on the basis of AE cumulative energy, which reflects the intensity of damage evolution at different loading conditions. Compared with the critical shear band velocity (CSBV) associated with stick-slip dynamics of serrated flow, E max is a more sensitive failure parameter since it is connected with the local microscopic changes that occur during the material response process. Here, the E max is obtained prior to reaching the CSBV since the calculation of these two avalanches analysis focuses on the different stages of shear band growth. In particular, AE events are related to the “dry” friction process in the first stage, however, the CSBV is responsible for the “viscous” glide in the second stage. Therefore, Emax is not affected by the complex interactions between the shear bands during the stick-slip process. The maximum avalanche of serrated flow, S max , is proposed as the experimental failure threshold, which depends on the applied strain rate as S max ~ $\dot{ε}$ –λ . According to the relationship of E max and S max , the theoretical failure threshold, E max , follows a criterion E max = 2545$\dot{ε}$ –λ - 4468, where λ is equivalent to 0.15 for this work. Finally, combining the different calculations and AE measurements, this model gives new insights to predict the deformation failure behavior of Zr-based BMGs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Toward memory-efficient melt pool monitoring: a classification framework using event-based imaging and sparse sensing technique

Vision sensors like CMOS and CCD cameras are often used for in-process monitoring of melt pools in laser-based additive and welding processes, but they require transferring large amounts of data and computational processing resources. Event-based neuromorphic imagery, on the other hand, detects only the change in pixel intensity, thus potentially reducing the data amount and latency. With an event imager, this study develops a framework for melt pool condition classification, including image construction, time scale selection, optimal pixel selection, and sparse classification, to achieve a highly memory-efficient scheme. These are based on sparse sensing techniques with singular value decomposition (SVD) and QR pivoting, the two fundamental matrix transformations for linear dimensionality reduction. The framework is then validated by classifying a controlled experiment by exciting various mode shapes of liquid gallium pools of varying depths (3, 6, and 8 mm). At 200 pixels, the classifier can reach overall accuracy of 75%, while at 2000 pixels (0.013% of the total possible pixels), the accuracy is nearly 90% (89.86%). At the same number of pixels, random selection can only achieve 46% and 67%, respectively. The memory savings of the sparsely sampled event data compared to a conventional imager is about 500 times. In addition to performance, implementation and limitations of the framework are also discussed.

42 ENGINEERING↗
DOE OSTI2023

Air Conditioning Systems Fault Detection and Diagnosis-Based Sensing and Data-Driven Approaches

The air conditioning (AC) system is the primary building end-use contributor to the peak demand for energy. The energy consumed by this system has grown as fast as it has in the last few decades, not only in the residential section but also in the industry and transport sectors. Therefore, to combat energy crises, urgent actions on energy efficiency should be taken to support energy security. Consequently, the faults in AC system components increase energy consumption due to the degradation of the system’s performance and the losses in the energy conversion procedure. In this work, AC system fault detection and diagnosis (FDD) methods are investigated to propose analytic tools to identify faults and provide solutions to those problems. The analysis of existing work shows that data-driven approaches are more accurate for both soft and hard fault detection and diagnosis in AC systems. Therefore, the proposed methods are not accurate for simultaneous fault detection, while in some works, authors tested the method with several faults separately without investigating scenarios that combine more than one fault. Moreover, this study shows that integrating data-driven approaches requires deploying an optimal sensing and measurement architecture that can detect a maximum number of faults with minimally deployed sensors. The new sensing, information, and communication technologies are discussed for their integration in AC system monitoring in order to optimize system operation and detect faults.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Emergent Trends Complicate the Interpretation of the United States Drought Monitor (USDM)

Abstract Effective drought management must be informed by an understanding of whether and how current drought monitoring and assessment practices represent underlying nonstationary climate conditions, either naturally occurring or forced by climate change. Here we investigate the emerging climatology and associated trends in drought classes defined by the United States Drought Monitor (USDM), a weekly product that, since 2000, has been used to inform drought management in the United States. The USDM classifies drought intensity based in part on threshold percentiles in key hydroclimate quantities. Here we assess how those USDM‐defined drought threshold percentiles have changed over the last 23 years, examining precipitation, runoff, soil moisture (SM), terrestrial water storage (TWS), vapor pressure deficit (VPD), and near‐surface air temperature. We also assess underlying trends in the frequency of drought classifications across the U.S. Our analysis suggests that the frequency of drought class occurrence is exceeding the threshold percentiles defined by the USDM in a number of regions in the United States, particularly in the American West, where the last 23 years have emerged as a prolonged dry period. These trends are also reflected in percentile‐based thresholds in precipitation, runoff, SM, TWS, VPD, and temperature. Our results emphasize that while the USDM appears to be accurately reflecting observed nonstationarity in the physical climate, such trends raise critical questions about whether and how drought diagnosis, classification, and monitoring should address long‐term intervals of wet and dry periods or trends.

Geology↗
DOE OSTI2022

Relationship Between the Marginal Probability of Failure for a CPP Test and the Recommended Corrosion Control Requirements for Hanford Double Shell Waste Tanks

The Hanford Site in Washington State currently stores millions of gallons of radioactive waste in underground, carbon steel, double shell tanks (DSTs) that were constructed between 1968 and 1986. A chemistry control and monitoring program has been established mitigate corrosion in order to extend the service life for the DSTs. The current waste temperatures in the DSTs are at historical lows (i.e., typically less than 50 °C). The previous chemistry control requirements were determined for conditions at temperatures significantly higher. SRNL undertook a statistically based investigation of the role of nitrate and halide ion (i.e., chloride and fluoride) induced pitting corrosion. The objective was to develop a comprehensive waste chemistry envelope for the simultaneous minimization of the pitting and SCC risks caused by halide and nitrate ions at the lower temperature conditions. On the basis of these tests, new chemistry control requirements were proposed and have since been implemented for pitting corrosion control.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Embedded High-Temperature Sensors: Enhancing Thermoelectrical Performance with Refractory Composites Gradient Layers

To monitor the stability of various energy and manufacturing systems, sensors capable of operating at temperatures exceeding 1000 °C in diverse environments for extended durations are essential. However, under harsh conditions, degradation of sensing materials is a concern that can be controlled by embedding the sensors into refractory oxides. Doped-LaCrO3 based composites are excellent candidates for high-temperature sensing applications due to their good thermoelectrical properties. However, chemical reactivity between the conductive phase and the refractory oxides can decrease the performance of the sensors. In this work, it was devised gradient-type protective layers safeguarding conductive phases within embedded thermocouples, which were fabricated and characterized by X-Ray Diffraction for phase development analysis, Scanning Electron Microscopy and Energy-dispersive X-ray spectroscopy for microstructure and cationic interdiffusion kinetics, long-term thermoelectric testing was completed up to 1400 °C. The enhanced performance of these novel sensors addresses a critical limitation, rendering them viable for long-term high-temperature applications.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Effect of surface segregation on the oxidation resistance of Cu 3 Pt ( 100 )

Alloying element segregation often occurs under a reactive environment but its interplay with the subsequent surface oxidation of the alloy remains unclear. Using synchrotron-based ambient-pressure x-ray photoelectron spectroscopy, we dynamically monitor the surface segregation in Cu 3 Pt(100) in response to temperature and oxygen gas. Vacuum annealing leads to surface segregation of Cu along with the enrichment of Pt in the subsurface region. Upon switching to the O 2 atmosphere, dissociative chemisorption of oxygen does not change the surface segregation profile from that under the vacuum annealing condition. A stepwise increase in the oxygen pressure results in the transformation pathway of Cu → Cu 2 O → CuO, in which the selective oxidation of Cu gives rise to further accumulation of Pt underneath the oxide/alloy interface that hinders the supply of Cu from the bulk to the oxide/alloy interface, thereby leading to the termination of the surface oxidation after the Cu 2 O → CuO conversion is completed. This differs from the transformation pathway of Cu → Cu 2 O → Cu 2 O/CuO for the oxidation of pure Cu and Cu-Au alloys, in which the oxidation of Cu continues and the Cu 2 O/CuO bilayer growth is constantly maintained. Furthermore, these key differences provide useful insight into alloy design for controlling the surface properties such as corrosion resistance and catalytic performance of Cu base alloys.

36 MATERIALS SCIENCE↗