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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Roles of Solvent in the Catalytic Hydrogen Release from Liquid Organic Hydrogen Carriers: Chemical, Thermodynamical and Technological Aspects

A Liquid Organic Hydrogen Carrier (LOHC) enables the storage and transport of hydrogen at ambient pressures and temperatures in a safe and convenient form using current infrastructure. However, it is challenging to directly compare reactivity and selectivity for hydrogen release, especially when comparing the catalytic efficiencies of neat LOHCs to highly diluted LOHCs in different solvents, reaction conditions, and catalysts. This work evaluates the role of solvents in catalysis and quantifies the energy efficiency of the overall process. The presence of solvent dilutes the volumetric density of available hydrogen, but may be necessary to achieve optimal catalysts stability, reactivity, and product selectivity. With respect to the reaction conditions as determined by thermodynamics, solvents with higher vapor pressures than that of the carrier can cause the erroneous impression of a more favorable reaction equilibrium. Concerning energy efficiency, solvents can result in increased energy demand for hydrogen release as the inert solvent must be heated to reaction temperatures required for release of H 2 from the LOHC. Further, this work recommends that investigations of catalyst reactivity should be carried out at different ratios of solvent to LOHC to understand how the reactivity changes and what the implications are for maximizing energy density and catalyst stability and reactivity. Investigations should also consider how these implications will affect the technical needs of applications intended for the LOHC system. Based on the results of this study, it is advised to focus research activities on LOHC systems with a gravimetric solvent content below about 50% as the thermodynamic disadvantages become very pronounced beyond this threshold.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Impact of Cyber Threat Awareness on Driver Response to an Unexpected Vehicle Cyberattack

Here, the integration of advanced cyber-physical systems in heavy vehicles introduces new vulnerabilities by expanding the possibility of cyberattacks. The objective of this study is to evaluate (1) how threat awareness influences driver response to an unexpected cyberattack, (2) how the provision of a basic cyberattack response protocol influences driver performance, and (3) how professionally trained versus standard drivers compare in their responses to a cyberattack. An on-road driving study (N = 50) was conducted using a medium heavy-duty vehicle. Participants were divided into three groups: Control, which remained unaware of any potential cyberattack; Aware, which was informed about the potential cyberattack; and Aware + Protocol, which received the same warning as the Aware group with the addition of a basic cyberattack response protocol. An instrument cluster cyberattack was executed at the same location for all participants. The findings highlight the essential role of awareness and response protocol in enhancing driver response to an unexpected vehicle cyberattack. The Aware + Protocol group had the highest stop rate (100%) and the shortest stopping distances (224 m for standard drivers and 254 m for professionals), compared to the Control group (828 and 520 m, respectively). Aware + Protocol also had the fastest reaction time, averaging 7.53 s, versus 16.12 s (Aware) and 30.29 s (Control). These results emphasize that awareness alone is insufficient. Providing drivers with clear, actionable protocols significantly improves their ability to react quickly and safely to cyberattacks, enhancing overall road safety.

Cybersecurity↗
DOE OSTI2024

Endogeneity of pedestrian survival time and emergency medical service response time: Variations across disadvantaged and non-disadvantaged communities

The Vision Zero-Safe Systems Approach prioritizes fast access to Emergency Medical Services (EMS) to improve the survivability of road users in transportation crashes, especially concerning the recent increase in pedestrian-involved crashes. Pedestrian crashes resulting in immediate or early death are considerably more severe than those taking longer. The time gap between injury and fatality is known as survival time, and it heavily relies on EMS response time. The characteristics of the crash location may be associated with EMS response and survival time. A US Department of Transportation initiative identifies communities often facing challenges. Six disadvantaged community (DAC) indicators, including economy, environment, equity, health, resilience, and transportation access, enable an analysis of how survival and EMS response times vary across DACs and non-DACs. To this end, this study created a unique and comprehensive database by linking DACs data with 2017–2021 pedestrian-involved fatal crashes. This study utilizes two-stage residual inclusion models with segmentation for DACs and non-DACs accounting for the endogenous relationship between EMS response and pedestrian survival time. The results indicate that EMS response time is higher and pedestrian survival time is lower in DACs than in non-DACs. A delayed EMS response time is associated with a greater reduction in survival time in DACs compared to non-DACs. Factors, e.g., nighttime and interstate crashes, contribute to higher EMS response time, while pedestrian drugs, driver speeding, and hit-and-run behaviors are associated with a greater reduction in survival time in DACs than non-DACs. Finally, the implications of the findings are discussed in the paper.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Long-term thermal aging behavior and strength reduction in a laser powder bed fusion 316H stainless steel

The long-term thermal stability of structural alloys is essential for ensuring the safe and reliable operation of nuclear reactors and other power plants. While extensive research has explored the effects of thermal aging on conventional stainless steels, the behavior of additively manufactured (AM) alloys remains less understood. This study examines the thermal aging response of laser powder bed fusion (LPBF) 316H stainless steel (SS) at temperatures ranging from 550 °C to 750 °C over durations of up to 10,000 h (approximately 1.14 years). Advanced characterization techniques, including electron microscopy and synchrotron X-ray diffraction, were used to investigate dislocation recovery and phase evolution. Based on these findings, a time-temperature-precipitation (TTP) diagram was developed for LPBF 316H SS, revealing a 10- to100-fold acceleration in precipitation kinetics compared to wrought 316H SS. A physics-informed model was calibrated using the short-term experimental data, enabling predictions of average precipitate sizes, volume fractions of M 23 C 6 and Laves phases, and changes in molybdenum solute concentration for aging up to 1 × 10⁶ h (114 years). These microstructural insights were further utilized to estimate yield strength and extrapolate strength reduction factors over the extended aging period. Despite the accelerated aging kinetics, LPBF 316H SS demonstrated superior yield strength retention compared to its wrought counterpart. In conclusion, this study establishes a framework for evaluating long-term performance using short-term experimental data and supports the accelerated qualification of AM materials for high-temperature structural applications.

Laser powder bed fusion↗
DOE OSTI2025

The molten salt tritium transport experiment: A pumped fluoride salt loop for hydrogen isotope experimentation

Molten salt reactors (MSRs) and fusion reactors propose to use molten salts as coolants and breeder blanket materials, respectively. Tritium, however, poses safety concerns in both reactor types due to its ability to permeate through reactor materials and potential for environmental release. This manuscript addresses the tritium transport phenomena in molten salts and presents the design and analysis of the Molten Salt Tritium Transport Experiment (MSTTE). MSTTE is a forced-convection fluoride salt loop intended to measure hydrogen isotope permeation through structural materials in a flowing salt system. In the first phase, MSTTE will use FLiNaK salt and deuterium as surrogates for FLiBe and tritium, with future plans to utilize tritium and FLiBe. MSTTE couples a Copenhagen Atomics pumped salt loop with an external test section that introduces hydrogen isotopes into the loop and measures transport phenomena. The Hydrogen Injection System (HIS) controls hydrogen isotope introduction into the molten salt loop. Here, the permeation test section measures the permeation rate through stainless steel tubing in contact with flowing salt. Computational fluid dynamics (CFD) analysis ensures fully developed salt flow in the permeation test section. MSTTE is modeled with MELCOR-TMAP to predict the permeation rate as a function of experimental variables such as source term, salt flow rate, and salt temperature. Results indicate that the source term is the only parameter with a significant effect on the permeation rate. Pressure drop analysis suggests that the loop should operate below 200 LPM to maintain a pressure drop below 200 kPa. Additionally, finite-element analysis assesses thermal stress during loop operation to ensure the experiment's safe design. MSTTE will provide semi-integral data on tritium transport phenomena in molten salts and serve as a testbed for advancing molten salt technology.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Decay heat analysis for advanced reactor spent fuel transportation and storage applications

Accurate characterization of nuclide inventories and decay heat in spent nuclear fuel is critical for ensuring its safe handling, storage, transportation, and disposal. Although extensive research has been conducted on light-water reactor fuel, advanced reactors present unique challenges due to their diverse core configurations, fuel characteristics, neutron energy spectra, and burnup levels. Building upon previous efforts that developed representative reactor core models for various advanced reactor types and fuels, this study evaluates reactor-specific decay heat characteristics. The results highlight significant variations across advanced reactor types as well as across reactor designs within the same reactor type, and they provide comparison to typical commercial light-water reactor fuel. For example, thermal-spectrum reactor fuels were observed to have an approximately 100-fold decrease in decay heat over the first decade of cooling, whereas the reduction was 10-fold for fast-spectrum reactor fuels. Mass-specific decay heat at discharge can differ by three orders of magnitude among the fast and thermal reactor systems considered. Overall, for the analyzed advanced reactor fuel, fewer than 17 nuclides account for over 99% of total decay heat at 0.5 years, and that number drops to fewer than 7 nuclides at 100 years of cooling. By quantifying reactor-specific decay heat trends and nuclide contributions, this work provides a technical basis to support the development of spent fuel management strategies for advanced reactor fuels as well as safety evaluations for storage, transportation, and long-term waste disposal.

Advanced reactors↗
DOE OSTI2025

Genetic algorithm optimization of nuclear criticality experiment for reduction of intermediate-energy 239 Pu nuclear data uncertainties

Nuclear criticality experiments are conducted to investigate specific nuclear data important for safe handling and storage of fissile materials, reactor design and operation, and the validation of radiation transport codes. Incorrect or uncertain nuclear data can prohibitively impact operational safety limits, reactor licensing, and predictive simulation capability; therefore, integral measurements from criticality experiments are necessary and should be performed frequently. To maximize the impact of the integral measurements, it is important to consider experiment geometry, material selection, and component dimensions. When taking these considerations into account, the experiment design process becomes iterative and very time intensive. This work utilizes a genetic algorithm to efficiently explore potential nuclear criticality experiment designs for the Laboratory Directed Research & Development project PARADIGM (PARallel Approach of Differential and InteGral Measurements) at Los Alamos National Laboratory. In this paper, the building blocks of the genetic algorithm are discussed in detail, the genetic algorithm methodology is verified, and the genetic algorithm is used to produce three candidate experiment models for the final PARADIGM design. The three candidate models produced by the genetic algorithm consist of copper-reflected assemblies containing 14 repeating units of alumina, graphite, boron, and plutonium plates. Furthermore, in addition to the optimization results, final design considerations are also discussed for designs with a height and/or weight very close to or slightly above assembly machine operational limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom

Energy-efficient ventilation control plays an important role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations provide detailed and physically accurate representations of indoor airflow, their high computational cost limits their use in real-time building control. In this work, we present a neural operator learning framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable building ventilation control with the high-fidelity fluid dynamics models. Our method jointly optimizes the airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train an ensemble of neural operator transformer models to learn the mapping from building control actions to airflow fields using high-resolution CFD data. This learned neural operator is then embedded in an optimization-based control framework for building ventilation control. Experimental results show that our approach achieves significant energy savings compared to maximum airflow rate control, rule-based control, as well as data-driven control methods using spatially averaged CO 2 prediction and deep learning–based reduced-order models, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method in maintaining energy efficiency and indoor air quality in real-world buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Virtual refrigerant charge sensing algorithm for residential CO₂ heat pumps

Natural refrigerants are increasingly adopted in next-generation heat pump systems, among which CO₂ heat pumps have attracted significant attention. However, due to their high operating pressures, the leakage risk is higher, resulting in undercharge conditions and degraded heat pump performance. Thus, developing an accurate refrigerant charge level detection technique is necessary to guarantee safe and efficient operation. Although virtual refrigerant charge (VRC) level calculation algorithms for CO₂ heat pumps exist, they typically rely on empirically selected features without a systematic selection framework, leading to multicollinearity and potential overfitting, which limit their prediction accuracy and generalizability. To address these issues, this study proposes a VRC algorithm framework with a systematic feature selection method that identifies physically meaningful and statistically significant features, and is applied using a residential CO₂ heat pump as a case study. The method is extended from previous work on conventional refrigerants to account for charge behavior in CO₂ gas coolers. The selected features include gas cooler outlet density, evaporator pressure, and superheat temperature. The results demonstrate that the proposed feature selection method significantly improves prediction accuracy compared to existing VRC approaches. A relatively small training dataset (∼30 samples) is sufficient for feature identification and model development. The developed algorithm achieves less than 3% prediction error under both undercharge and overcharge conditions, representing reductions of 46.7% and 35.3% compared to two recent reference VRC algorithms for transcritical CO₂ heat pumps reported in the literature. The proposed algorithm and feature selection method enhance leakage detection capability, facilitate the deployment of CO₂ heat pump systems, and contribute to reduced energy waste and maintenance costs.

Guo, Fangzhou [Lawrence Berkeley National Laborato↗
DOE OSTI2025

Cool rooms for indoor heat resilience: Evaluating affordable cooling strategies in heat-stressed California homes

Extreme heat conditions pose significant indoor survivability challenges for resource-constrained communities, which often lack access to cooling, have poorly insulated homes, and face compounding socioeconomic vulnerabilities. Moreover, concurrent power outages worsen health risks and heat-related illnesses. It is therefore crucial to develop innovative and affordable cooling approaches to protect vulnerable populations. This study assesses the efficacy of “cool rooms”– a designated space within a home equipped with passive and low-power active cooling measures to maintain safe indoor temperatures during extreme heat events and power disruptions. Using a physics-based building energy modeling approach, we evaluate the efficacy of various retrofit packages in maintaining thermal safety within the cool room under recent extreme heat conditions. The results indicate that passive measures can reduce 64% of hours with unmet standard effective temperatures, while the combination of passive and low-power active measures with built-in batteries further cuts this to 86%. Nevertheless, these strategies remain insufficient to maintain indoor thermal safety during extended outages. In contrast, integrating a solar-powered mini-split heat pump, whose technical potential was evaluated in this study, reduces indoor air temperatures below the 28 °C overheating threshold and significantly improves indoor habitability. The localized cool room strategy also offers potential for grid resilience by reducing peak electricity demand by up to 70% compared to whole house cooling during heat waves. The findings can inform the development of actionable heat mitigation plans and retrofit policies for residential communities with relatively low adoption of air conditioning such as warm marine climates.

Cool room↗
DOE OSTI2024

Stabilizing high-Ni cathodes with gradient surface Ti-enrichment

High-Ni cathodes are being intensely pursued worldwide for electric vehicles and other energy-dense applications due to their high capacity and low cost. However, structural instabilities during electrochemical cycling and when subjected to thermal treatment have been the major issues hindering their practical deployment. We here report a rational design of coating-integrated-into-synthesis protocol for fabricating surface Ti-enriched LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811#Ti) material. The coating to intermediates is crucial to obtain high structural ordering, both in the bulk and surface of high-Ni cathodes, and the Ti substitute has a unique tri-valence (Ti 3+ ) in a gradient surface distribution. Further, the simulations of projected density of states in the atomistic understanding further certify significantly enhanced stability of lattice oxygen for the NMC811 through such a Ti 3+ -based structure reinforcement. Consequently, the NMC811#Ti cathode delivers a high capacity up to 200mAhg -1 at 0.1 C, along with superior stabilities during air-storage and thermal treatment (up to 297°C at the fully charged state under differential scanning calorimetric measurements). The corresponding NMC811#Ti||graphite full cell exhibits a desired 83.6% capacity retention after 1000 cycles at 0.5 C in a voltage range of 2.8–4.3V. This work demonstrates a delicate surface reinforcement to stabilize high-Ni cathodes for long-life and safe lithium-ion batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Technoeconomic analysis and life cycle assessment of purification processes for captured CO 2 streams

Captured carbon dioxide (CO 2 ) streams contain impurities that must be removed to meet specifications for safe transport, storage, and utilization. Among these impurities, oxygen poses challenges due to its high reactivity and potential to cause corrosion, motivating stringent purity limits below 10 ppmv. Building on recent experimental demonstrations of catalytic oxygen removal using hydrogen (H 2 ), carbon monoxide (CO), methanol (CH 3 OH), and methane (CH 4 ) as reducing agents, this study presents a technoeconomic (TEA) and life cycle assessment (LCA) of these four catalytic purification pathways. Process flowsheets were developed and simulated in Aspen Plus for CO 2 streams representative of both low-temperature and high-temperature capture processes, with integrated heat recovery and energy optimization. Results showed that total purification costs were dominated by feedstock procurement and electricity consumption. Among the studied reducing agents, the CH 4 -assisted route achieved the lowest purification cost and highest CO 2 recovery. Sensitivity analyses showed that the H 2 route became competitive at H 2 prices below $\$$0.56/kg to $\$$0.84/kg, depending on the CO 2 feed temperature conditions. In conclusion, environmental impacts were primarily driven by indirect CO 2 emissions from raw material production and utility consumption.

CO2 pipeline specifications↗
DOE OSTI2025

Analysis of semivolatile organics in liquid radioactive residue using sorptive stir bar and solvent back-extraction

Current methods for semivolatiles analysis in radioactive samples can produce large volumes of radioactive solvent residue. A method utilizing stir-bar sorptive extraction has been explored in this work for its applicability to radioactive waste samples. This low solvent analytical method may accelerate remediation, minimize hazardous solvent waste, and reduce exposure risk to workers. Organic compounds (polyaromatic hydrocarbons, chlorinated aromatics, and phenolic compounds) were chosen as surrogates for common Liquid Waste System (LWS) contaminants at Savannah River Site (Aiken, SC). Stir-bar extraction parameters (extraction time, matrix modification, and effective pH range) and solvent back extraction parameters (solvent type, volume, and extraction time) were optimized experimentally for the chosen compounds. Affinity of the stir-bar extraction polymer to radionuclides Cs-137 and Am-241 was observed to determine radionuclide concentration effects. The stir-bar method achieved mean recovery of 100 ± 0.7% (1σ), relative to 114 ± 7% using solvent extraction, while reducing weekly method hands-on time by 93.4% and solvent volume consumption by 99.3%. Sensitivity was improved by 378% in simulated tank waste and 278% in real-world LWS matrix, relative to solvent extraction. This work has produced a safe and optimized method for the low solvent analysis of organics in legacy radioactive tank waste by stir-bar sorptive extraction.

GC-MS↗
DOE OSTI2026

Separation of radium and actinium in acetic acid/acetate solutions using TK101 resin

Here, the uptake and elution behavior of Ra and Ac was studied on TK101 resin using acetic acid and acetate buffer (acetic acid/lithium acetate) solutions. There is high extraction of Ra (>10 3 ) over a wide range of acetic acid concentrations (0.02 to 4 M) with slightly lower extraction (>10 2 ) in the acetate buffer solutions (pH 3.9 to 5.6). The Ac extraction is considerably lower with a maximum D w of ∼100, and negligible extraction at high acetic acid concentrations (> 1 M) and in the acetate buffer solutions. The difference in D w can be leveraged for highly efficient separations of Ac from Ra, with no detectable Ra in the Ac fractions and high Ac yields (∼100%). These separations may be useful for radiopharmaceutical applications as the Ac purity is extremely high and the elution conditions can be optimized to be biologically safe and appropriate for chelation to molecules relevant to targeted alpha therapy.

and nuclear chemistry↗
DOE OSTI2025

Protecting honey bees through microbiome engineering

Honey bees are indispensable insects. Their pollination services support modern agriculture and natural ecosystems. Managed honey bee colonies face increasing threats to their survival, ranging from environmental stressors that include agrochemicals to infestations of arthropod pests and infections with microbial pathogens. Like humans, honey bees have a native gut microbiome that supports their health. However, the bee gut microbiome has a simpler composition than the gut microbiome of mammals, and its main constituent bacterial species can be easily cultured outside of the host. Furthermore, this experimental tractability and the need for new methods for protecting hive health have made honey bees a testbed for synthetic microbiomes augmented with probiotic bacteria and engineered DNA. Here, we discuss the natural benefits of bee gut bacteria, recent progress in genetically modifying these bacteria, and how symbiont-mediated RNA interference and other microbiome engineering approaches can boost bee immunity and suppress bee pathogens and parasites. Finally, we discuss how emerging methods for microbiome engineering and biocontainment could be applied to honey bees and used to address challenges in translating these proof-of-principle achievements into safe and effective technologies for field applications at scale.

Biological and medical sciences↗
DOE OSTI2025

An isotopic labeling investigation into the influence of the nitro group on LLM-105 thermal decomposition

Here, this work presents the first application of isotopically labeled LLM-105 (2,6-diamino-3,5-dinitropyrazine-1-oxide) to investigate thermal decomposition pathways. Specially synthesized LLM-105 isotopologues were utilized to isolate the influence of labeled 15 NO 2 nitro groups on the formation of lightgas products. Simultaneous differential scanning calorimetry, thermo-gravimetric, and mass spectrometry measurements were employed to track the evolution of product gases, enabling the direct comparison of isotopically shifted species with unlabeled LLM-105. Key findings show that C 2 N 2 production is mainly dependent on nitrogen sources from either the amine groups or the pyrazine ring (i.e., not the nitro groups). The formation of NO, N 2 , and N 2 O all involves the nitro groups to some extent. NO (nitric oxide) was found to be the predominant gas species directly formed from the nitro group of LLM-105. In contrast, mixed nitrogen isotopologues of N 2 and N 2 O (i.e., 14 N 15 N and 15 NNO) formed more readily in comparison to their pure counterparts (i.e., 15 N 2 and 15 N 2 O). This indicates the amine and/or pyrazine groups of LLM-105, in addition to the nitro group, are involved in the decomposition pathways forming N 2 and N 2 O. In addition, our investigation led to the discovery of two previously unreported decomposition products (CHO and HNCO), which were confirmed through hydrogen labelling utilizing deuterium isotopes. These results provide detailed speciation trends of gaseous products during LLM-105 decomposition, offering new insights into reaction pathways. Experimental data reported here will support the development of a detailed chemical kinetics model for LLM-105, essential for the safe handling of high explosives.

Chemistry - Chemical explosives↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2026

Iron surface corrosion in supercritical CO2 at atomic scale investigated by molecular dynamics simulations

Understanding the corrosion behavior of steels in supercritical carbon dioxide (S-CO2) is essential for ensuring the safe application of S-CO2 as a heat-transfer fluid in high-temperature energy systems, including advanced nuclear reactors. In this work, molecular dynamics (MD) simulations using ReaxFF potential are performed to explore the atomic-scale corrosion mechanisms of body-centered cubic iron (BCC-Fe) in S-CO2. The results show that CO2 molecules in S-CO2 decompose at the Fe surface, generating free C and O atoms that form Fe-C and Fe-O bonds and subsequently produce oxides and carbides. Concurrently, Fe atoms dissolve from the surface and diffuse into the S-CO2 region, resulting in interdiffusion of Fe, C and O atoms at the interface. The corrosion-layer thickness calculations show that high pressure and temperature induced by S-CO2 have stronger effects than surface orientation on the corrosion process. In addition, surface Fe atoms undergo substantial displacement under S-CO2 exposure, further accelerating corrosion. When a radiation-induced void is introduced near the Fe surface, the corrosion is enhanced. The void-matrix interface expands the reaction surface area and simultaneously induces corrosion reactions inside the bulk, resulting in a deeper penetration of C and O and thicker corrosion layers. All these results indicate that high-temperature, high-pressure and radiation-induced voids can seriously affect the corrosion of Fe in S-CO2, and must be considered to better use S-CO2 in nuclear facilities.

Li, Wenhua↗