Analysis of the Effect of Conditions of Preparation of Nitrogen-Doped Activated Carbons Derived from Lotus Leaves by Activation with Sodium Amide on the Formation of Their Porous Structure

This paper presents results of the analysis of the impact of activation temperature and mass ratio of activator to carbonized precursor R on the porous structure of nitrogen-doped activated carbons derived from lotus leaves by carbonization and chemical activation with sodium amide NaNH2. The analyses were carried out via the new numerical clustering-based adsorption analysis (LBET) method applied to nitrogen adsorption isotherms at −195.8 °C. On the basis of the results obtained it was shown that the amount of activator, as compared to activation temperatures, has a significantly greater influence on the formation of the porous structure of activated carbons. As shown in the study, the optimum values of the porous structure parameters are obtained for a mass ratio of R = 2. At a mass ratio of R = 3, a significant decrease in the values of the porous structure parameters was observed, indicating uncontrolled wall firing between adjacent micropores. The conducted analyses confirmed the validity of the new numerical clustering-based adsorption analysis (LBET) method, as it turned out that nitrogen-doped activated carbons prepared from lotus leaves are characterized by a high share of micropores and a significant degree of surface heterogeneity in most of the samples studied, which may, to some extent, undermine the reliability of the results obtained using classical methods of structure analysis that assume only a homogeneous pore structure.

EXTENSIONS TO THE K-AMH ALGORITHM FOR NUMERICAL CLUSTERING

The k-AMH algorithm has been proven efficient in clustering categorical datasets. It can also be used to cluster numerical values with minimum modification to the original algorithm. In this paper, we present two algorithms that extend the k-AMH algorithm to the clustering of numerical values. The original k-AMH algorithm for categorical values uses a simple matching dissimilarity measure, but for numerical values it uses Euclidean distance. The first extension to the k-AMH algorithm, denoted k-AMH Numeric I, enables it to cluster numerical values in a fashion similar to k-AMH for categorical data. The second extension, k-AMH Numeric II, adopts the cost function of the fuzzy k-Means algorithm together with Euclidean distance, and has demonstrated performance similar to that of k-AMH Numeric I. The clustering performance of the two algorithms was evaluated on six real-world datasets against a benchmark algorithm, the fuzzy k-Means algorithm. The results obtained indicate that the two algorithms are as efficient as the fuzzy k-Means algorithm when clustering numerical values. Further, on an ANOVA test, k-AMH Numeric I obtained the highest accuracy score of 0.69 for the six datasets combined with p-value less than 0.01, indicating a 95% confidence level. The experimental results prove that the k-AMH Numeric I and k-AMH Numeric II algorithms can be effectively used for numerical clustering. The significance of this study lies in that the k-AMH numeric algorithms have been demonstrated as potential solutions for clustering numerical objects.