| 1 | Pressure-induced photoluminescence enhancement and ambient retention in confined carbon dots显示文摘Piezochromic luminescent materials have shown great potential in advanced optoelectronic applications.However,most of luminescent materials usually undergo emission quenching under external stimuli.Herein,we demonstrate for the first time that the photoluminescence of carbon dots(CDs)confined within sodium hydroxide can be enhanced when high pressure is applied.They exhibit a 1.6-fold fluorescence enhancement compared with pristine CDs.Importantly,the enhanced fluorescence intensity can be retained after the release of pressure to ambient conditions.A combination of experimental analysis and theoretical simulations indicates that such an enhanced emission is mainly attributed to the strong confinement resulting from the sodium hydroxide matrix,which can separate the CDs spatially and restrict the nonradiative pathway.These results provide a rational strategy for manipulating the optical properties of CDs with enhanced and retainable photoluminescence(PL)performance,thus opening up a venue for designing luminescent CDs-based materials. | Qing Lou Xigui Yang Kaikai Liu Zhongzheng Ding Jinxu Qin Yizhe Li Chaofan Lv Yuan Shang Yuewen Zhang Zhuangfei Zhang Jinhao Zang Lin Dong Chong-Xin Shan | 2022 | Nano Research2022，15，3： | 1 |
| 2 | Composite T-Process Regression Models显示文摘Process regression models,such as Gaussian process regression model(GPR),have been widely applied to analyze kinds of functional data.This paper introduces a composite of two T-process(CT),where the first one captures the smooth global trend and the second one models local details.TheCThas an advantage in the local variability compared to general T-process.Furthermore,a composite T-process regression(CTP)model is developed,based on the composite T-process.It inherits many nice properties as GPR,while it is more robust against outliers than GPR.Numerical studies including simulation and real data application show that CTP performs well in prediction. | Zhanfeng Wang Yuewen Lv Yaohua Wu | 2023 | Communications in Mathematics and Statistics2023，11，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员