8 research outputs found

    层片状结构超细晶镍的腐蚀行为

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    通过动电位极化试验、电化学阻抗试验研究了等通道角挤压制备得到的层片状结构超细晶Ni在3.5%NaCl溶液中的耐腐蚀性,并与退火态粗晶Ni的耐蚀性进行了对比。结果表明,相较于粗晶Ni而言,层片状结构超细晶Ni具有更高的腐蚀电位、更低的腐蚀电流、更稳定的表面钝化膜以及更大的容抗弧直径

    2005~2015年CERN光合有效辐射数据集

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    光合有效辐射在生态学、农学以及气候学等多个学科中都有重要的应用价值。它是揭示物质与能量交换过程的基本生理变量,是光合潜力、潜在产量的评估研究和作物生长模拟研究、土壤碳的固定模拟研究中不可缺少的关键数据之一。该数据集涵盖了中国8个典型陆地生态类型、中国生态系统研究网络(CERN)下属的40个辐射观测站观测的光合有效辐射日均值,时间跨度为2005~2015年。通过对传感器的集中标定与规范的数据质量控制方案,保障了观测数据的可靠性与可比性。采用光谱仪、辐射标准灯传递辐射基准方案,对光合有效辐射传感器进行集中标定与比对,标定精度小于5%,符合世界气象组织(WMO)标准;采用极值法对观测的光合有效辐射数据进行质量控制

    Prediction of Energy Resolution in the JUNO Experiment

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    International audienceThis paper presents the energy resolution study in the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The study reveals an energy resolution of 2.95% at 1 MeV. Furthermore, the study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data taking. Moreover, it provides a guideline in comprehending the energy resolution characteristics of liquid scintillator-based detectors

    JUNO Sensitivity on Proton Decay pνˉK+p\to \bar\nu K^+ Searches

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    The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in pνˉK+p\to \bar\nu K^+ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via pνˉK+p\to \bar\nu K^+ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is 9.6×10339.6 \times 10^{33} years, competitive with the current best limits on the proton lifetime in this channel

    JUNO sensitivity on proton decay pνK+p → νK^{+} searches

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    JUNO sensitivity on proton decay p → ν K + searches*

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    The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the pνˉK+ p\to \bar{\nu} K^+ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via pνˉK+ p\to \bar{\nu} K^+ is 36.9% ± 4.9% with a background level of 0.2±0.05(syst)±0.2\pm 0.05({\rm syst})\pm 0.2(stat) 0.2({\rm stat}) events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is 9.6×1033 9.6 \times 10^{33} years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies
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