The advantages of once-through molten salt reactors include readily available fuel,low nuclear proliferation risk,and low technical difficulty.It is potentially the most easily commercialized fuel cycle mode for molte...The advantages of once-through molten salt reactors include readily available fuel,low nuclear proliferation risk,and low technical difficulty.It is potentially the most easily commercialized fuel cycle mode for molten salt reactors.However,there are some problems in the parameter selection of once-through molten salt reactors,and the relevant burnup optimization work requires further analysis.This study examined once-through graphitemoderated molten salt reactor using enriched uranium and thorium.The fuel volume fraction(VF),initial heavy nuclei concentration(HN_(0)),feeding uranium enrichment(E_(FU)),volume of the reactor core,and fuel type were changed to obtain the optimal conditions for burnup.We found an optimal region for VF and HN_(0) in each scheme,and the location and size of the optimal region changed with the degree of E_(FU),core volume,and fuel type.The recommended core schemes provide a reference for the core design of a once-through molten salt reactor.展开更多
It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes(SWCNTs).Here,a high-throughput method combined with machine learning is reported that ...It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes(SWCNTs).Here,a high-throughput method combined with machine learning is reported that efficiently screens the growth conditions for the synthesis of high-quality SWCNTs.Patterned cobalt(Co)nanoparticles were deposited on a numerically marked silicon wafer as catalysts,and parameters of temperature,reduction time and carbon precursor were optimized.The crystallinity of the SWCNTs was characterized by Raman spectroscopy where the featured G/D peak intensity(IG/ID)was extracted automatically and mapped to the growth parameters to build a database.1,280 data were collected to train machine learning models.Random forest regression(RFR)showed high precision in predicting the growth conditions for high-quality SWCNTs,as validated by further chemical vapor deposition(CVD)growth.This method shows great potential in structure-controlled growth of SWCNTs.展开更多
基金supported by the Shanghai Sailing Program(No.19YF1457900)Chinese TMSR Strategic Pioneer Science and Technology Project(No.XDA02010000)+1 种基金National Natural Science Foundation of China(No.12005290)Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.2020261)。
文摘The advantages of once-through molten salt reactors include readily available fuel,low nuclear proliferation risk,and low technical difficulty.It is potentially the most easily commercialized fuel cycle mode for molten salt reactors.However,there are some problems in the parameter selection of once-through molten salt reactors,and the relevant burnup optimization work requires further analysis.This study examined once-through graphitemoderated molten salt reactor using enriched uranium and thorium.The fuel volume fraction(VF),initial heavy nuclei concentration(HN_(0)),feeding uranium enrichment(E_(FU)),volume of the reactor core,and fuel type were changed to obtain the optimal conditions for burnup.We found an optimal region for VF and HN_(0) in each scheme,and the location and size of the optimal region changed with the degree of E_(FU),core volume,and fuel type.The recommended core schemes provide a reference for the core design of a once-through molten salt reactor.
基金This project is supported by the National Key Research and Development Program of China(No.2016YFA0200101)the National Natural Science Foundation of China(Nos.51522210,51972311,51625203,51532008,51761135122 and 52001322)JSPS KAKENHI Grant Number JP20K05281 and JP25820336,and MOST 108-2634-F-006-009 and MOST 109-2224-E-006-003.
文摘It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes(SWCNTs).Here,a high-throughput method combined with machine learning is reported that efficiently screens the growth conditions for the synthesis of high-quality SWCNTs.Patterned cobalt(Co)nanoparticles were deposited on a numerically marked silicon wafer as catalysts,and parameters of temperature,reduction time and carbon precursor were optimized.The crystallinity of the SWCNTs was characterized by Raman spectroscopy where the featured G/D peak intensity(IG/ID)was extracted automatically and mapped to the growth parameters to build a database.1,280 data were collected to train machine learning models.Random forest regression(RFR)showed high precision in predicting the growth conditions for high-quality SWCNTs,as validated by further chemical vapor deposition(CVD)growth.This method shows great potential in structure-controlled growth of SWCNTs.
