Preparation and Characterization of TiO₂Photocatalytic Thin Film and Its Compounds by Micro-Arc Oxidation Technique

Mesoporous TiO2 ceramic films have been prepared upon the Ti alloy substrate by the micro-arc oxidation (MAO) technology. To enhance the photo-catalytic property of the films, Eu2O3 particles were added into the electrolyte solution of Na2CO3/Na2SiO3. Scanning electron microscope (SEM), energy dispersive (EDS), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) are employed to characterize the modified films. Diffuse reflectance spectra (DRS) test, photo-generated current test and photo decomposition test are applied to evaluate the photo-catalytic property of the modified films. The results show that Eu2O3 transformed into one-dimensional (1-D) nano-wires embedded within the composite film, and the film has high photo-catalytic property.

Assessing Drought Conditions in Cloudy Regions Using Reconstructed Land Surface Temperature

Temperature vegetation dryness index(TVDI)in a triangular or trapezoidal feature space can be calculated from the land surface temperature(LST)and normalized difference vegetation index(NDVI),and has been widely applied to regional drought monitoring.However,thermal infrared sensors cannot penetrate clouds to detect surface information of sub-cloud pixels.In cloudy areas,LST data include a large number of cloudy pixels,seriously degrading the spatial and temporal continuity of drought monitoring.In this paper,the Remotely Sensed Daily Land Surface Temperature Reconstruction model(RSDAST)is combined with the LST reconstructed(RLST)by the RSDAST and applied to drought monitoring in a cloudy area.The drought monitoring capability of the reconstructed temperature vegetation drought index(RTVDI)under cloudy conditions is evaluated by comparing the correlation between land surface observations for soil moisture and the TVDI before and after surface temperature reconstruction.Results show that the effective duration and area of the RTVDI in the study area were larger than those of the original TVDI(OTVDI)in 2011.In addition,RLST/NDVI scatter plots cover a wide range of values,with the fitted dry–wet boundaries more representative of real soil moisture conditions.Under continuously cloudy conditions,the OTVDI inverted from the original LST(OLST)loses its drought monitoring capability,whereas RTVDI can completely and accurately reconstruct surface moisture conditions across the entire study area.The correlation between TVDI and soil moisture is stronger for RTVDI(R=-0.45)than that for OTVDI(R=-0.33).In terms of the spatial and temporal distributions,the R value for correlation between RTVDI and soil moisture was higher than that for OTVDI.Hence,in continuously cloudy areas,RTVDI not only expands drought monitoring capability in time and space,but also improves the accuracy of surface soil moisture monitoring and enhances the applicability and reliability of thermal infrared data under extreme conditions.

Retrieval of Aerosol Optical Depth for Chongqing Using the HJ-1 Satellite Data

Aerosol optical depth （AOD） is a common indicator applied in monitoring aerosols in the atmosphere. The hilly landscape and rapid economic growth of the megacity Chongqing have facilitated increased aerosol concentration, and it is meaningful to accurately retrieve AOD over Chongqing. The HJ-1A/B satellite of China carries a sensor/camera called the Charge Coupled Device （CCD）, the spatial resolution of which meets the requirement for re- trieving high resolution AOD. In this paper, analysis of the AOD retrievals from different methods using the H J-1 satellite data revealed the most suitable algorithm. Through comparison with the AOD product of Moderate Resolu- tion Imaging Spectroradiometer （MODIS）, the AOD retrieval results using enhanced vegetation index （EVI） to estim- ate dark pixels showed the highest correlation. The continental aerosol model was used to build a lookup table that was able to facilitate a good AOD retrieval for both city and rural areas. Finally, the algorithm that combined dark pixels, buffer areas, and the deep blue algorithm was found to be most suitable for AOD retrieval. The AOD retrieval results based on the HJ-1 data were consistent with MODIS products, and our algorithm yields reasonable results in most cases. The results were also compared with ground-based PMl0 measurements synchronized with the overpass time of the HJ-1 satellite, and high correlation was found. The findings are relevant to other Chinese satellite data used for retrieving AOD on the same channels.

Error Sensitivity Analysis in 10–30-Day Extended Range Forecasting by Using a Nonlinear Cross-Prediction Error Model

Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous met- eorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear cross- prediction error （NCPE） model, and their stability in the prediction validity period in 1 0-30-day extended range fore- casting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small （10＂5-10-2）, minor vari- ation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random er- ror has minimal effect on the prediction. When the ratio is in the range of 10-1-2 （i.e., random error dominates）, at- tention should be paid to the random error instead of only the initial error. When the ratio is around 10 2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecast- ing, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depic- ted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect （m 〉 4） is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperat- ure or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.

Concept to devices:from plasmonic light trapping to upscaled plasmonic solar modules[Invited]

The concept of using plasmonic nanostructures to manage light in solar cells has offered an unprecedented potential for dramatically increased solar energy conversion efficiency that breaks the previously predicated efficiency limit.In the past decade,intensive research efforts have been focused on this field.However,nanoplasmonic solar cells still remained in the laboratory level.To facilitate the transformation of the nanoplasmonic solar cell concept to a viable high-efficiency technology solution for the solar industry,it is essential to address key fundamental as well as practical challenges including the detrimental absorption of metallic nanostructures,narrow-band absorption enhancement in the active layer,the high cost and scarcity of noble metals,and the expensive and complicated plasmonic nanomaterial fabrication and integration methods.In this paper,after a brief review of our main results in nanoplasmonic solar cells,we present our strategies for using innovative photonic methods to overcome these challenges and demonstrate a large-area(173 cm2)broadband plasmonic thin-film solar minimodule with an efficiency of 9.5%resulting from the enhanced plasmonic light scattering enabled by silver lumpy nanoparticles with an ultralow nanoparticle coverage density of 5%.