ABSTRACT
In this study, we present dye-sensitized solar cells (DSSCs) with improved efficiencies by using SnO2/TiO2 composite photoanodes containing SnO2 at various concentrations. The composites consisted of hollow nanofibers (h-NFs) of SnO2 and TiO2 nanoparticles (NPs). The combination of the large surface area of the NPs and the efficient charge transport in the h-NFs make the use of the SnO2/TiO2 composites advantageous. DSSCs in which composite photoanodes with 50 wt% h-NFs were incorporated showed enhanced efficiencies that were 20% higher than the efficiencies of cells containing TiO2 NP-based photoanodes. These results indicated the improved electron diffusion length and shorter electron transfer time in the composite structures due to the crosslinking between h-NFs and NPs.
ABSTRACT
Regardless of high capacity and stability during lithium extraction, LiFePO4 materials have difficulty in the applications for high electrical density because of low electrical conductivities. In order to optimize this problem, we synthesized carbon coated LiFePO4 by adding humic acid using solid state reaction method. We characterized the synthesized compounds via the crystallinity, the valence states of Fe ions, and their shapes. We found the carbon coating using X-ray photoelectron spectroscopy (XPS) and transmission electron microscope (TEM). We also found that the iron ion is substituted from 3+ to 2+ through XPS measurement. We showed that the carbon coating increased the electrochemical behavior by measuring the charge-discharge characteristics.
ABSTRACT
BACKGROUND: Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. Clustering is an important tool for finding groups of genes with similar expression patterns in microarray data analysis. However, hard clustering methods, which assign each gene exactly to one cluster, are poorly suited to the analysis of microarray datasets because in such datasets the clusters of genes frequently overlap. RESULTS: In this study we applied the fuzzy partitional clustering method known as Fuzzy C-Means (FCM) to overcome the limitations of hard clustering. To identify the effect of data normalization, we used three normalization methods, the two common scale and location transformations and Lowess normalization methods, to normalize three microarray datasets and three simulated datasets. First we determined the optimal parameters for FCM clustering. We found that the optimal fuzzification parameter in the FCM analysis of a microarray dataset depended on the normalization method applied to the dataset during preprocessing. We additionally evaluated the effect of normalization of noisy datasets on the results obtained when hard clustering or FCM clustering was applied to those datasets. The effects of normalization were evaluated using both simulated datasets and microarray datasets. A comparative analysis showed that the clustering results depended on the normalization method used and the noisiness of the data. In particular, the selection of the fuzzification parameter value for the FCM method was sensitive to the normalization method used for datasets with large variations across samples. CONCLUSION: Lowess normalization is more robust for clustering of genes from general microarray data than the two common scale and location adjustment methods when samples have varying expression patterns or are noisy. In particular, the FCM method slightly outperformed the hard clustering methods when the expression patterns of genes overlapped and was advantageous in finding co-regulated genes. Thus, the FCM approach offers a convenient method for finding subsets of genes that are strongly associated to a given cluster.
