PbS quantum dots (QDs) with strong near-infrared (NIR) fluorescence have been prepared directly in aqueous solution, by using dihydrolipoic acid (DHLA) as a stabilizer. The photoluminescence (PL) emission maximum could be tuned conveniently over a wide range (from ca. 870 nm to 1010 nm) by manipulating the experimental conditions, such as the Pb/ S or DHLA/Pb molar ratios. Under optimized conditions, the maximum PL quantum yield was approximately 10 %. These resultant PbS QDs were highly stable when stored in the dark at 4°C. After one month of storage, the PL emission intensity decreased by only about 20 %, and no obvious spectral redshift was observed. We scaled up further the synthesis of PbS QDs in the lab, where the concentration of QDs was increased to 8 mM from the usual 1 mM. The experimental

fPI-01 is a promising optical agent for diagnosis of FR-positive tumors, especially in their early stage.

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An efficient palladium-catalyzed three-component domino reaction of 2-iodobiphenyls, O-benzoylhydroxylamines, and norbornadiene has been developed to construct diverse 1-amino phenanthrene derivatives. The methodology realizes simultaneous construction of one C-N bond and...

Folate-NHG could actively accumulate in three models of folate receptor positive tumors with different sizes and keep retention for more than 96 h, which enables it to be used as a diagnostic reagent or anti-tumor drug carrier for tumor therapy.

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Ultra-short-term wind power prediction is of great importance for the integration of renewable energy. It is the foundation of probabilistic prediction and even a slight increase in the prediction accuracy can exert significant improvement for the safe and economic operation of power systems. However, due to the complex spatiotemporal relationship and the intrinsic characteristic of nonlinear, randomness and intermittence, the prediction of regional wind farm clusters and each wind farm’s power is still a challenge. In this paper, a framework based on graph neural network and numerical weather prediction (NWP) is proposed for the ultra-short-term wind power prediction. First, the adjacent matrix of wind farms, which are regarded as the vertexes of a graph, is defined based on geographical distance. Second, two graph neural networks are designed to extract the spatiotemporal feature of historical wind power and NWP information separately. Then, these features are fused based on multi-modal learning. Third, to enhance the efficiency of prediction method, a multi-task learning method is adopted to extract the common feature of the regional wind farm cluster and it can output the prediction of each wind farm at the same time. The cases of a wind farm cluster located in Northeast China verified that the accuracy of a regional wind farm cluster power prediction is improved, and the time consumption increases slowly when the number of wind farms grows. The results indicate that this method has great potential to be used in large-scale wind farm clusters.