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Lead-based reactor nuclear power has the advantages of inherent safety, compact size, light weight, long service life, and high efficiency, and it is widely applied to advanced marine equipment, unmanned underwater vehicles, and deep-sea space stations for energy and power purposes. Conducting research on lead-based reactor marine nuclear power is key to the technological innovation of energy and power for marine equipment in China. This study clarifies the application scenarios and development demands of marine nuclear power, summarizes the development status of lead-based reactor nuclear power from the aspects of technical characteristics, representative applications, and research status, and analyzes corresponding key technologies including nuclear fuel and cladding materials, key components in the primary loop, coolant processes and oxygen control, and advanced power generation technologies. Moreover, the study explores the development challenges for lead-based reactor marine nuclear power and proposes there feasible technical routes: low-temperature lead-based reactor marine nuclear power, high-temperature and high-efficiency lead-based reactor marine nuclear power, and integrated natural-circulation lead-based reactor marine nuclear power. Furthermore, we propose the following suggestions to promote the leapfrog development of marine nuclear power in China:(1) strengthening research and development (R&D) of specialized technologies for the lead-based reactor marine nuclear power and accelerating the deployment of demonstrative projects; (2) incorporating lead-based reactors into the country's major energy strategies and creating a standards system; (3) establishing national joint R&D centers and creating new models for technological innovation and industrial development.

A 100-W radioisotope thermoelectric generator (RTG) is by far the most suitable power supply for long-term deep space exploration where solar power would not be feasible. Understanding the thermal performance and electrical performance of the RTG under operational conditions is paramount for its nominal and safety performance during the space mission. In this paper, modeling and experimental studies on the thermal behavior and electrical performance of the 100-W RTG have been conducted. The RTG uses high conversion efficiency skutterudite-based thermoelectric convertor (TEC) arrays thermally coupled with a radioisotope heat unit (RHU) to generate electricity. A comprehensive finite element model and an electrical heating prototype of the 100-W RTG have been built to assess the performance of the RTG designs. Critical temperature, generated power, and energy conversion efficiency were evaluated. The simulation results show that the maximum output power of the RTG can reach about 120 W(electric); the temperature of the hot end of the TECs is about 853 K, and the temperature of the cold end is about 473 K, making a temperature difference of about 380 K. The RTG prototype with Bi2Te3 TECs generated about 60 W(electric) of electrical power in the first experimental research stage. These research results have significant reference for extension of the RTG prototype to the actual power source of the RHU and allow for future research and development improvements of the 100-W RTG. [ABSTRACT FROM AUTHOR]

Soil total nitrogen (STN) is a crucial component of the ecosystem's nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to predict the STN content and map its spatial distribution in the northeastern coastal region of Hebei Province, China, employing the random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods. The purpose was to compare the ability of remote sensing images (Landsat-8, Sentinel-1, and Sentinel-2) with different machine learning methods for predicting STN content. The research results show the following: (1) The three machine learning methods accurately predicted the STN content and the optimal model provided by the XGBoost method, with an R2 of 0.627, RMSE of 0.127 g·kg−1, and MAE of 0.092 g·kg−1. (2) The combination of optical and synthetic aperture radar (SAR) images improved prediction accuracy, with the R2 improving by 45.5%. (3) The importance of optical images is higher than that of SAR images in the RF, GBM, and XGBoost methods, with optical images accounting for 87%, 76%, and 77% importance, respectively. (4) The spatial distribution of STN content predicted by the three methods is similar. Higher STN contents are distributed in the northern part of the study area, while lower STN contents are distributed in coastal areas. The results of this study can be very useful for inventories of soil nitrogen and provide data support and method references for revealing nitrogen cycling. [ABSTRACT FROM AUTHOR]