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| 1 | First results on ^(76)Ge neutrinoless double beta decay from CDEX-1 experiment显示文摘We report the first results on ^(76)Ge neutrinoless double beta decay from stage one of the China dark-matter experiment(CDEX).A p-type point-contact high-purity germanium detector with a mass of 994 g has been installed to detect neutrinoless double beta decay events, as well as to directly detect dark matter particles. An exposure of 304 kg d has been analyzed over a wide spectral band from 500 keV to 3 MeV. The average event rate obtained was about 0.012 counts per keV per kg per day over the 2.039 MeV energy range. The half-life of ^(76)Ge neutrinoless double beta decay derived based on this result is T^(0ν)1/2>6.4×10^(22) yr(90% C.L.). An upper limit on the effective Majorana-neutrino mass of 5.0 eV has been achieved. | Li Wang Qian Yue KeJun Kang JianPing Cheng YuanJing Li TszKing Henry Wong ShinTed Lin JianPing Chang JingHan Chen QingHao Chen YunHua Chen Zhi Deng Qiang Du Hui Gong Li He QingJu He JinWei Hu HanXiong Huang TengRui Huang LiPing Jia Hao Jiang HauBin Li Hong Li JianMin Li Jin Li Jun Li Xia Li XueQian Li YuLan Li FongKay Lin ShuKui Liu Hao Ma JingLu Ma XingYu Pan Jie Ren XiChao Ruan ManBin Shen Vivek Sharma Lakhwinder Singh Manoj Kumar Singh Manoj Kumar Singh Arun Kumar Soma ChangJian Tang WeiYou Tang ChaoHsiung Tseng JiMin Wang Qing Wang ShiYong Wu YuCheng Wu HaoYang Xing Yin Xu Tao Xue LiTao Yang SongWei Yang Nan Yi ChunXu Yu HaiJun Yu WeiHe Zeng XiongHui Zeng Zhi Zeng Lan Zhang YunHua Zhang MingGang Zhao Wei Zhao JiFang Zhou ZuYing Zhou JingJun Zhu WeiBin Zhu ZhongHua Zhu | 2017 | Science China(Physics,Mechanics & Astronomy)2017，60，7： | 3 |
| 2 | High-energy-density plasma in femtosecond-laser-irradiated nanowire-array targets for nuclear reactions显示文摘In this work,the high-energy-density plasmas(HEDP)evolved from joule-class-femtosecond-laser-irradiated nanowire-array(NWA)targets were numerically and experimentally studied.The results of particle-in-cell simulations indicate that ions accelerated in the sheath field around the surfaces of the nanowires are eventually confined in a plasma,contributing most to the high energy densities.The protons emitted from the front surfaces of the NWA targets provide rich information about the interactions that occur.We give the electron and ion energy densities for broad target parameter ranges.The ion energy densities from NWA targets were found to be an order of magnitude higher than those from planar targets,and the volume of the HEDP was several-fold greater.At optimal target parameters,8%of the laser energy can be converted to confined protons,and this results in ion energy densities at the GJ/cm^(3) level.In the experiments,the measured energy of the emitted protons reached 4 MeV,and the changes in energy with the NWA’s parameters were found to fit the simulation results well.Experimental measurements of neutrons from 2H(d,n)3He fusion with a yield of(24±18)×10^(6)/J from deuterated polyethylene NWA targets also confirmed these results. | Defeng Kong Guoqiang Zhang Yinren Shou Shirui Xu Zhusong Mei Zhengxuan Cao Zhuo Pan Pengjie Wang Guijun Qi Yao Lou Zhiguo Ma Haoyang Lan Wenzhao Wang Yunhui Li Peter Rubovic Martin Veselsky Aldo Bonasera Jiarui Zhao Yixing Geng Yanying Zhao Changbo Fu Wen Luo Yugang Ma Xueqing Yan Wenjun Ma | 2022 | Matter and Radiation at Extremes2022，7，6： | 1 |
| 3 | Photonuclear production of medical isotopes^(62,64)Cu using intense laser-plasma electron source显示文摘^(62,64)Cu are radioisotopes of medical interest that can be used for positron emission tomography(PET)imaging.Moreover,64Cu hasβ−decay characteristics that allowfor targeted radiotherapy of cancer.In the present work,a novel approach to experimentally demonstrate the production of ^(62,64)Cu isotopes fromphotonuclear reactions is proposed in which large-current laser-based electron(e−)beams are generated fromthe interaction between sub-petawatt laser pulses and near-critical-density plasmas.According to simulations,at a laser intensity of 3.431021 W/cm2,a dense e−beamwith a total charge of 100 nCcan be produced,and this in turn produces bremsstrahlung radiation of the order of 1010 photons per laser shot,in the region of the giant dipole resonance.The bremsstrahlung radiation is guided to a natural Cu target,triggering photonuclear reactions to produce themedical isotopes ^(62,64)Cu.An optimal target geometry is employed to maximize the photoneutron yield,and ^(62,64)Cuwith appropriate activities of 0.18 GBq and 0.06 GBq are obtained for irradiation times equal to their respective half-livesmultiplied by three.The detection of the characteristic energy for the nuclear transitions of ^(62,64)Cu is also studied.The results of our calculations support the prospect of producing PET isotopes with gigabecquerel-level activity(equivalent to the required patient dose)using upcoming high-intensity laser facilities. | ZhiGuo Ma HaoYang Lan WeiYuan Liu ShaoDong Wu Yi Xu ZhiChao Zhu Wen Luo | 2019 | Matter and Radiation at Extremes2019，4，6： | 1 |
| 4 | Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning显示文摘Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R2) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms. | Haoyang Xian Pinjing He Dongying Lan Yaping Qi Ruiheng Wang Fan Lü Hua Zhang Jisheng Long | 2023 | Frontiers of Environmental Science & Engineering2023，17，10： | 0 |
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