The Bayesian neural network(BNN)method is proposed to predict the isotopic cross-sections in proton induced spallation reactions.Learning from more than 4000 data sets of isotopic cross-sections from 19 experimental m...The Bayesian neural network(BNN)method is proposed to predict the isotopic cross-sections in proton induced spallation reactions.Learning from more than 4000 data sets of isotopic cross-sections from 19 experimental measurements and 5 theoretical predictions with the SPACS parametrization,in which the mass of the spallation system ranges from 36 to 238,and the incident energy from 200 MeV/u to 1500 MeV/u,it is demonstrated that the BNN method can provide good predictions of the residue fragment cross-sections in spallation reactions.展开更多
A simple method to prepare two-dimensional hexagonal boron nitride(h-BN) scalably is essential for practical applications. Despite intense research in this area, high-yield production of two-dimensional h-BN with larg...A simple method to prepare two-dimensional hexagonal boron nitride(h-BN) scalably is essential for practical applications. Despite intense research in this area, high-yield production of two-dimensional h-BN with large size and high crystallinity is still a key challenge. In the present work, we propose a simple exfoliation process for boron nitride nanosheets(BNNSs) with high crystallinity by sonication-assisted hydrothermal method, via the synergistic effect of the high pressure, and cavitation of the sonication. Compared with the method only by sonication, the sonication-assisted hydrothermal method can get the fewer-layer BNNSs with high crystallinity.Meanwhile, it can reach higher yield of nearly 1.68%, as the hydrothermal method with the yield of only 0.12%. The simple sonication-assisted hydrothermal method has potential applications in exfoliating other layered materials, thus opening new ways to produce other layered materials in high yield and high crystallinity.展开更多
基金Supported by the National Natural Science Foundation of China(11975091,U1732135,11875070)Natural Science Foundation of Henan Province(162300410179)supported by the US Department of Energy(DE-FG02-93ER40773)
文摘The Bayesian neural network(BNN)method is proposed to predict the isotopic cross-sections in proton induced spallation reactions.Learning from more than 4000 data sets of isotopic cross-sections from 19 experimental measurements and 5 theoretical predictions with the SPACS parametrization,in which the mass of the spallation system ranges from 36 to 238,and the incident energy from 200 MeV/u to 1500 MeV/u,it is demonstrated that the BNN method can provide good predictions of the residue fragment cross-sections in spallation reactions.
文摘A simple method to prepare two-dimensional hexagonal boron nitride(h-BN) scalably is essential for practical applications. Despite intense research in this area, high-yield production of two-dimensional h-BN with large size and high crystallinity is still a key challenge. In the present work, we propose a simple exfoliation process for boron nitride nanosheets(BNNSs) with high crystallinity by sonication-assisted hydrothermal method, via the synergistic effect of the high pressure, and cavitation of the sonication. Compared with the method only by sonication, the sonication-assisted hydrothermal method can get the fewer-layer BNNSs with high crystallinity.Meanwhile, it can reach higher yield of nearly 1.68%, as the hydrothermal method with the yield of only 0.12%. The simple sonication-assisted hydrothermal method has potential applications in exfoliating other layered materials, thus opening new ways to produce other layered materials in high yield and high crystallinity.
