In this paper, we present a new method for highly efficient size separation of water-soluble CdTe quantum dots (QDs) based on CGE using polymer solution as sieving medium. CdTe QDs were synthesized in aqueous phase by a chemical route with mercaptopropionic acid as a ligand. In the alkaline solution, CdTe QDs possess negative charges and migrate to the anode in the electric field. In linear polyacrylamide sieving medium, the migration time of CdTe QDs was increased with the size of CdTe QDs. The effects of some factors, such as types, concentrations, and pH of sieving media, on the separation of CdTe QDs were investigated systematically. Highly efficient separation of CdTe QDs was obtained in linear polyacrylamide sieving medium, and collection of fractions was automatically accomplished by CGE technique. Our preliminary results show that CGE technique is an efficient tool for characterization and size-dependent separation of water-soluble nanoparticles. In addition, the fraction collection in CGE may be useful in certain special applications such as fabrication of nanodevices in the future.
In the power distribution system, the missing or incorrect file of users-transformer relationship (UTR) in lowvoltage station area (LVSA) will affect the lean management of the LVSA, and the operation and maintenance of the distribution network. To effectively improve the lean management of LVSA, the paper proposes an identification method for the UTR based on Local Selective Combination in Parallel Outlier Ensembles algorithm (LSCP). Firstly, the voltage data is reconstructed based on the information entropy to highlight the differences in between. Then, the LSCP algorithm combines four base outlier detection algorithms, namely Isolation Forest (I-Forest), One-Class Support Vector Machine (OC-SVM), Copula-Based Outlier Detection (COPOD) and Local Outlier Factor (LOF), to construct the identification model of UTR. This model can accurately detect users' differences in voltage data, and identify users with wrong UTR. Meanwhile, the key input parameter of the LSCP algorithm is determined automatically through the line loss rate, and the influence of artificial settings on recognition accuracy can be reduced. Finally, this method is verified in the actual LVSA where the recall and precision rates are 100% compared with other methods. Furthermore, the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed. The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually. And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity, which improves the stability and accuracy of UTR identification in LVSA.
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