High-quality crystalline nanostructured ZnO thin films were grown on sapphire substrates by reactive sputtering. As-grown and post-annealed films (in air) with various grain sizes (2 to 29 nm) were investigated by scanning electron microscopy, X-ray diffraction, and Raman scattering. The electron–phonon coupling (EPC) strength, deduced from the ratio of the second- to the first-order Raman scattering intensity, diminished by reducing the ZnO grain size, which mainly relates to the Fröhlich interactions. Our finding suggests that in the spatially quantum-confined system the low polar nature leads to weak EPC. The outcome of this study is important for the development of nanoscale high-performance optoelectronic devices.
To comprehensively understand the behaviors of the near-band-edge emission and green emission (NBE, GE), the volume-weighting (VW) model is adapted to take into account a dead layer of confined excitons.
This study proposes a deep neural network (DNN) as a downscaling framework to compare original variables and nonlinear data features extracted by kernel principal component analysis (KPCA). It uses them as learning data for DNN downscaling models to assess future regional rainfall trends and uncertainties in islands with complex terrain. This study takes Taichung and Hualien in Taiwan as examples. It collects data in different emission scenarios (RCP 4.5, RCP 8.5) simulated by two Global Climate Models: ACCESS and CSMK3, in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), and monthly rainfall data of case regions from January 1950 to December 2005 in the Central Weather Bureau in Taiwan. DNN model parameters are optimized based on historical scenarios to estimate the trends and uncertainties of future monthly rainfall in the case regions. A multivariate linear regression is used as a baseline model to compare their effectiveness. The simulated results show that by both ACCESS and CSMK3, the dimensionless root mean squared error (RMSE) of KPCA was higher than that of the original variables in Taichung and Hualien. According to the analysis of three-class classification (according to the arrangement in descending power of historical rainfall, the predicted rainfall is divided into three ranges, low, normal, and high, marked by 30% and 70% of monthly rainfall), the wet season rainfall at the two stations is concentrated in the normal range. The probability of rainfall increase will improve in the dry season and will reduce in the wet season in the mid-term to long-term. The future wet season rainfall in Hualien has the highest variability. It ranges from 201 mm to 300 mm, with representative concentration pathways RCP 4.5 much higher than RCP 8.5. The median percentage increase and decrease in RCP 8.5 are higher than in RCP 4.5. This indicates that RCP 8.5 has a greater impact on future monthly rainfall.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.