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Quantum dots (QDs)/gel composites have sparked tremendous interests due to their great potentials in sensors. However, in most cases, these sensors are focused on single signal detection, suffers from a key hurdle of low signal density. Thus, it is highly desirable to develop multi-signal QDs/gel-based sensors. In this work, we constructed dual-signal QDs/gel assemblies by a microfluidic-assisted method, which shows promising application in dual-signal sensors. Typically, self-healing QDs/gel beads with different fluorescent signals were fabricated via microfluidic techniques. In virtue of the microfluidic-assisted self-assembly, various dual-signal assemblies were constructed controllably in the confined channels, where self-healing QDs/gel beads serve as building blocks. As expected, the assemblies exhibit rapid dual-signal detection capability toward organic amines and pH values. In addition, by using self-healing gels as printable materials, the combination of the self-assembly strategy with 3D microfluidic printing allows the generation of diverse responsive dual-signal patterns and structures. The self-assembly of self-healing building blocks not only offers a promising route toward fluorescent dual-signal architectures for advanced sensors, but will also guide the development of 3D printing for controllable fabrication of functional assemblies. [ABSTRACT FROM AUTHOR]

To investigate the application of hyperspectral remote sensing to estimate grassland biomass at the peak of the growing season, hyperspectral data were measured with an analytical spectral device (ASD) Fieldspec3 spectroradiometer, and harvested aboveground net primary productivity (ANPP) was recorded simultaneously in Hulunbeier grassland, Inner Mongolia, China. Ground spectral models were developed to estimate ANPP from the normalized difference vegetation index (NDVI) measured in the field following the same method as that of the National Aeronautic and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS-NDVI). Regression analysis was used to assess the relationship between ANPP and NDVI. Based on coefficients of determination (R 2) and error analysis, we determined that each vegetation type and the entire study area had unique optimal regression models. A linear equation best fit the arid steppe data, an exponential equation was best suited to wetland vegetation and power equations were optimal for meadow steppe and sand vegetation. After considering all factors, an exponential model between ANPP and NDVI (ANPP = 20.1921e3.2154(NDVI); standard error (SE) = 62.50 g m–2, R 2 = 0.7445, p < 0.001) was selected for the entire Hulunbeier grassland study area. Ground spectral models could become the foundation for yield estimation over large areas of Hulunbeier grassland. [ABSTRACT FROM PUBLISHER]