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An efficient and inexpensive double transition metal oxide catalyst, MoO2/β-MnO2 composite is developed for oxygen reduction reaction (ORR) and showing better performance than either MoO2 or β-MnO2. The composite catalyst was made into a multi-layered cathode (MLC) for a direct glucose fuel cell (GFC). The MLC consists of a graphite flake and several porous catalyst layers attached to both sides, allowing more adsorption and reaction sites for O2. The better cathode performance over single metal oxide catalysts or single-layer electrodes is confirmed by cyclic voltammetry (CV) and Tafel analysis. By coupling the MLC with a glucose oxidation system catalyzed by Fe(III)/H3PO4, a double-chambered GFC device has been built with an open-circuit potential of 0.68 V and a stable current density of 24 mA·cm−2. Therefore, the utilization of bimetallic multi-layer porous cathodes is a good method to improve the efficiency of ORR for fuel cells.

Since 1982, Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth’s land surface. In this study, Landsat 5, 7, and 8 land surface temperature (LST) products...

Land surface temperature (LST) is an important parameter in various fields, including hydrological, meteorological, and agricultural studies. Passive microwave techniques provide a practicable method to retrieve LST under both clear and cloudy conditions. In this study, LST derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature data during nighttime in the period 2015–2016 using a physically-based algorithm was compared with Moderate Resolution Imaging Spectroradiometer (MODIS) LST product MYD11A1 over 16 study sites that represent four different land cover types, i.e., barren/sparsely vegetated, grasslands, croplands, and evergreen broadleaf forest. Compared to MODIS-derived LST, the root-mean-square error (RMSE) of AMSR2-derived LST is 6.0 K and the bias is 4.4 K over all study sites. For barren/sparsely vegetated sites, LST was overestimated by 6.7 K. To eliminate the systematic bias induced by the penetration depth effect of microwave radiation over barren/sparsely vegetated sites, a linear regression between AMSR2- and MODIS-derived LST was applied and the RMSE decreases from approximately 7.8 to 3.5 K. For the other three land cover types, the bias ranges from approximately 1.4 to 4.2 K and the RMSE ranges from approximately 2.1 to 5.9 K. The bias between AMSR2- and MODIS-derived LST is related to vegetation coverage. The value of bias increases with the decrease of normalized difference vegetation index. Furthermore, the RMSE has a strong dependency on precipitable water vapor (PWV). It presents a descending pattern of RMSE with the increase of PWV.

Satellite remote sensing provides a unique way to measure land surface temperature (LST) at regional and global scales. Algorithms using thermal infrared (TIR) data provide a reliable way to retrie...

Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The National Centers for Environmental Prediction (NCEP) reanalysis atmospheric profiles combined with the radiation transport model MODerate resolution atmospheric TRANsmission version 5.2 (MODTRAN 5.2) were utilized to obtain atmospheric water vapor content (WVC). The ASTER Global Emissivity Database Version 3 (ASTER GED v3) product was utilized to estimate surface emissivity in order to improve the accuracy of LST estimation over barren surfaces. Using a simulation database, the coefficients of the algorithm were fitted and the performance of the algorithm was evaluated. The root-mean-square error (RMSE) values of the differences between the estimated LST and the actual LST of the MODTRAN radiative transfer simulation at each WVC subrange of 0–6.5 g/cm2 were less than 1.0 K. To validate the retrieval accuracy, ground-based LST measurements were collected at two relatively homogeneous desert study sites in Dalad Banner and Wuhai, Inner Mongolia, China. The bias between the retrieved LST and the in situ LST was about 0.2 K and the RMSE was about 1.3 K at the Dalad Banner site, whereas they were approximately -0.4 and 1.0 K at the Wuhai site. As a reference, the retrieved LST was compared with the operational SLSTR LST product in this study. The bias between the SLSTR LST product and the in situ LST was approximately 1 K and the RMSE was approximately 2 K at the Dalad Banner site, whereas they were approximately 1.1 and 1.4 K at the Wuhai site. The results demonstrate that the split-window algorithm combined with improved emissivity estimation based on the ASTER GED product can distinctly obtain better accuracy of LST over barren surfaces.

The accumulation of chemical constituents of some medicinal plants, such as Paeonia ostii T. Hong et J. X. Zhang, Houpo&euml, a officinalis (Rehder and E. H. Wilson) N. H. Xia and C. Y. Wu. and Atractylodes lancea (Thunb.) DC, can precipitate on the surface and form frosts after natural or artificial intervention. The characteristics of these three medicinal plants and their frosts were analyzed by light microscope, polarizing microscope, stereomicroscope, and metalloscope. The results of ordinary Raman of P. ostii and H. officinalis showed that the frosts of P. ostii matched paeonol, while that of H. officinalis matched magnolol and honokiol. In P. ostii and its frost, 19 peaks were identified by UPLC-Q/TOF-MS, and the main component was paeonol. Eleven components were identified in H. officinalis and its frosts, and the main components were magnolol and honokiol. A. lancea and its frosts were analyzed by gas chromatography-mass spectrometry (GC-MS), 21 were identified, and its main components were hinesol and &beta, eudesmol. These three medicinal plants accumulate compounds and precipitate frosts on the surface. The results show that the components of the frosts provide a basis for quality evaluation and research on similar medicinal plants, and reveals the scientific connotation of &ldquo, taking the medicinal materials&rsquo, precipitated frosts as the best&rdquo, of P. ostii, H. officinalis, and A. lancea, to some extent.

Passive microwave data are much less affected by clouds than TIR data for the retrieval of land surface temperature (LST), providing its unique advantages in global mapping of LST. In this study, a physically-based algorithm for LST retrieval was applied to AMSR2 global brightness temperature data. The performances of this algorithm applied on different land cover types were further evaluated against nighttime MYD11A1 thermal infrared LST products. The results showed that (i) the overall accuracy of the algorithm is about 5.42 K by root mean square error (RMSE) and 2.99 K by bias against MODIS LST during nighttime; (ii) the algorithm overestimates the LST over all land types. The overestimation is most evident over barren/sparsely vegetated surfaces. The algorithm shows that the algorithm has a robust performance comparing with MODIS LST and could be applied to estimate LST effectively.

Evapotranspiration (ET) plays a key role for energy transfer and water circulation in the biosphere, lithosphere, hydrosphere, cryosphere and atmosphere. In present study, spatially complete ET over the Heihe river basin, Northwest of China, was estimated from the synergistic use of MODIS (MODerate-resolution Imaging Spectroradiometer) data and CLDAS (China Meteorological Administration Land Data Assimilation) gridded meteorological data from June 1 to September 15 in 2012. For the estimation of ET over clear-sky pixels, a pixel-to-pixel pattern of land surface temperature (LST)-vegetation index (VI) feature space was developed where meteorological data were used to determine the dry and wet edges for each pixel; whereas the traditional Penman-Monteith equation was implemented to obtain ET over clouds pixels. Finally, ground ET measurements collected at two sites (corn and orchard) were used to evaluate the estimated results, root mean square error (RMSE) of 77.2W/m2 and 74.9W/m2 can be obtained for the two sites, respectively, indicating that spatially complete ET can be derived from currently available satellite images and meteorological data.

International audience; Land surface temperature (LST) plays an important role in land surface processes, and it is a key input for estimating important hydrological states and fluxes, such as soil moisture and evapotranspiration. In this study, a three-channel method is proposed to retrieve cloudy LST values from passive microwave data based on the relationship among surface emissivities at 18.70, 36.50, and 89.00 GHz over a bare soil surface. The performance of the method was evaluated using simulated data, resulting in a root mean square error (RMSE) of approximately 1.4 K over the bare soil surface. This method was further extended to retrieve cloudy LST values over a natural surface. Due to the lack of in situ LST measurements, ground-based air temperatures were used as proxy data to validate the cloudy LST values retrieved from AMSR-E data. The RMSE values of the differences between the retrieved cloudy LST values and the ground-based air temperatures are approximately 3.4 K and 4.3 K for the descending and ascending overpasses, respectively. The results demonstrate that the three-channel method can be used to retrieve cloudy LST values from passive microwave data with reasonable accuracy.

Land surface temperature (LST) is an important variable in the physics of land–surface processes controlling the heat and water fluxes over the interface between the Earth’s surface and the atmosphere. Space-borne remote sensing provides the only feasible way for acquiring high-precision LST at temporal and spatial domain over the entire globe. Passive microwave (PMW) satellite observations have the capability to penetrate through clouds and can provide data under both clear and cloud conditions. Nonetheless, compared with thermal infrared data, PMW data suffer from lower spatial resolution and LST retrieval accuracy. Various methods for estimating LST from PMW satellite observations were proposed in the past few decades. This paper provides an extensive overview of these methods. We first present the theoretical basis for retrieving LST from PMW observations and then review the existing LST retrieval methods. These methods are mainly categorized into four types, i.e., empirical methods, semi-empirical methods, physically-based methods, and neural network methods. Advantages, limitations, and assumptions associated with each method are discussed. Prospects for future development to improve the performance of LST retrieval methods from PMW satellite observations are also recommended.

Portable sensors have been developed for the determination of ionizable compounds based on molecularly imprinted polymers (MIPs). These polymers are charged due to the utilization of ionizable monomers such as diallylamine and sodium vinyl sulfonate (SVS). Mediated by the charged templates, imprinted sites with specific shapes and charge distributions were build in the MIPs. At the time of molecular recognition, the imprinted sites were reoccupied by the templates, making the polymer charges neutralized. This procedure led to significant permittivity changing and contributed much to device sensitivity. The capacitive signal drop of 1 million molecules (~1.66 × 10−18 mol) adds up to >10−12 f, which is detectable to a current portable multimeter. At the same time interferent components were expediently washed off because of poor affinities to an MIP. Consequently a handheld LCR (Inductance Capacitance Resistance) meter was used to successfully monitor the molecular recognition signals. High sensitive and selective analysis has been achieved for three exemplified compounds, i.e. aspirin, propranolol hydrochloride, and glyphosate, with linear ranges as 5.50 × 10−13–10−9 mol·L−1, 3.39 × 10−13–10−6 mol·L−1 and 5.89 × 10−16–10−6 mol·L−1. Meanwhile the related detection limits are 1.68 × 10−13 mol·L−1, 3.02 × 10−13 mol·L−1 and 1.94 × 10−16 mol·L−1. The more charges the template possesses, the more sensitive the sensor is.

International audience; An analysis of the atmospheric impact on ground brightness temperature (Tg) is performed for numerous land surface types at commonly-used frequencies (i.e., 1.4 GHz, 6.93 GHz, 10.65 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz and 89.0 GHz). The results indicate that the atmosphere has a negligible impact on Tg at 1.4 GHz for land surfaces with emissivities greater than 0.7, at 6.93 GHz for land surfaces with emissivities greater than 0.8, and at 10.65 GHz for land surfaces with emissivities greater than 0.9 if a root mean square error (RMSE) less than 1 K is desired. To remove the atmospheric effect on Tg, a generalized atmospheric correction method is proposed by parameterizing the atmospheric transmittance τ and upwelling atmospheric brightness temperature Tba↑. Better accuracies with Tg RMSEs less than 1 K are achieved at 1.4 GHz, 6.93 GHz, 10.65 GHz, 18.7 GHz and 36.5 GHz, and worse accuracies with RMSEs of 1.34 K and 4.35 K are obtained at 23.8 GHz and 89.0 GHz, respectively. Additionally, a simplified atmospheric correction method is developed when lacking sufficient input data to perform the generalized atmospheric correction method, and an emissivity-based atmospheric correction method is presented when the emissivity is known. Consequently, an appropriate atmospheric correction method can be selected based on the available data, frequency and required accuracy. Furthermore, this study provides a method to estimate τ and Tba↑ of different frequencies using the atmospheric parameters (total water vapor content in observation direction Lwv, total cloud liquid water content Lclw and mean temperature of cloud Tclw), which is important for simultaneously determining the land surface parameters using multi-frequency passive microwave satellite data.