Physical investigation of ZnSe QDs synthesized by polyol method at 200 °C

In this paper, zinc selenide nanoparticles powder was successfully synthesized using rapid polyol method. The preparation method was changed by using new Se solvents in the final stage to delete seleniums which have not participated in reaction product. This change in the preparation method increased the purity of final product (92 %); and using selenium’s solvents as detergents caused the production of ZnSe with roughly 100 % purity. X-ray diffractions showed that the samples had a cubic structure with lattice constant equalling 5.6699 Å and with 5.5 nm for crystallite size. Atomic force microscopy (AFM) and high resolution transmission electron microscopy (HRTEM) images showed that the particles were almost spherical and well crystallized ZnSe nanoparticles were formed. The average sizes of nanoparticles were 15 and 16.4 nm for AFM and HRTEM, respectively. Absorption Spectra of all samples showed a blue shift in comparison with bulk ZnSe. It showed low absorption in a wide range of wavelengths. Band gap energy of the pure ZnSe nanoparticles was found to be 4.51 eV, which is higher than that of the bulk value of ZnSe (2.67 eV). Photoluminescence spectra of the samples showed emission at 450–500 nm wavelengths at room temperature which are useful for the application of solar cells, quantum dot light-emitting diodes and blue organic light-emitting diodes devices.     

Effects of resampling method and adaptation on clustering ensemble efficacy

Clustering ensembles combine multiple partitions of data into a single clustering solution of better quality. Inspired by the success of supervised bagging and boosting algorithms, we propose non-adaptive and adaptive resampling schemes for the integration of multiple independent and dependent clusterings. We investigate the effectiveness of bagging techniques, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms. In our adaptive approach, individual partitions in the ensemble are sequentially generated by clustering specially selected subsamples of the given dataset. The sampling probability for each data point dynamically depends on the consistency of its previous assignments in the ensemble. New subsamples are then drawn to increasingly focus on the problematic regions of the input feature space. A measure of data point clustering consistency is therefore defined to guide this adaptation. Experimental results show improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and adaptive techniques. A meaningful consensus partition for an entire set of data points emerges from multiple clusterings of bootstraps and subsamples. Subsamples of small size can reduce computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical study also compares the performance of adaptive and non-adaptive clustering ensembles using different consensus functions on a number of datasets. By focusing attention on the data points with the least consistent clustering assignments, whether one can better approximate the inter-cluster boundaries or can at least create diversity in boundaries and this results in improving clustering accuracy and convergence speed as a function of the number of partitions in the ensemble. The comparison of adaptive and non-adaptive approaches is a new avenue for research, and this study helps to pave the way for the useful application of distributed data mining methods.