Mathematical Analysis in Characterization of Carbon Nanotubes (CNTs) as possible Mosquito Repellents

Mosquitoes are a great threat to human health to date and are a subject of interdisciplinary research involving scientists from many areas. Recently much attention has been put to novel approaches to mosquito repellent products that involve the use of novel materials, such as carbon nanomaterials, where it is essential to determine their properties. This research discusses the full molecular characterization of carbon nanotubes (CNTs) produced by electrolysis in molten salts. Each CNT has its mathematical representation due to its hexagonal lattice structure. Multi-wall carbon nanotubes (MWCNTs) are considered. The focus is on determining their structural parameters: innermost and outermost diameters, chiral indices m and n, number of walls, and unit cell parameters. Corresponding frequency parts of Raman spectra of four experimentally produced CNTs are elaborated, and Python programming and Mathematica are employed for the most accurate (m,n) assignment. Determining the chirality of these samples enables the calculation of other structural properties, which are performed now, including their graph representation. The latter enables the evaluation of different distance-based topological indices (Wiener, Balaban, Sum-Balaban, Harary index, etc.) to predict some index-related properties of the molecules

Optimal Selection of Parameters for Production of Multiwall Carbon Nanotubes (MWCNTs) by Electrolysis in Molten Salts using Machine Learning

The production and use of carbon nanotubes (CNTs) have become extremely wide within the last decade. Hence, the high interest in producing non-expensive and quality CNTs has motivated many research projects. This research considers the design and development of new technology for producing MWCNTs by electrolysis in molten salts using non-stationary and stationary current regimes. The electrolysis is simple, ecological, economical, and flexible, and it offers possibilities for accurate control of various parameters, such as applied voltage, current density, or temperature. We infer the underlying relationship between the parameters and the quality of the experimentally produced MWCNTs by using explainable tree-based Machine Learning (ML) models. We train several models in a supervised manner, whereas in model covariates, we use the parameters of the MCWNTs, and as a target variable, the quality of the produced MWCNT. Domain experts label all the experimental examples in our data set. Controlling these parameters enables high-yield production and, particularly important, obtaining MWCNTs, which are up to ten times cheaper than other existing technologies

Distance based topological indices on multiwall carbon nanotubes samples obtained by electrolysis in molten salts

The interest for the intensive studies and methods of structural characterization of multiwall carbon nanotubes (MWCNTs) to date has resulted in many valuable contributions and an amazingly wide application area. This paper offers an approach that combines several techniques. It is the first direct application of the graph theory upon nanotubical structures obtained by electrolysis in molten salts using non-stationary current regimes. The spectroscopic data enables studying the diameters and performing an (n,m) assignment of nanotube samples. Using the graph representation and the chirality of the studied samples, different distance based topological indices (Wiener, Balaban, Sum-Balaban, and Gutman indices) have been evaluated in order to enable further prediction of index-related properties of the molecules

DISTANCE BASED TOPOLOGICAL INDICES ON MULTIWALL CARBON NANOTUBES SAMPLES OBTAINED BY ELECTROLYSIS IN MOLTEN SALTS

The interest for the intensive studies and methods of structural characterization of multiwall carbon nanotubes (MWCNTs) to date has resulted in many valuable contributions and amazingly wide application area. This work offers an approach that combines several techniques. It is the first direct application of graph theory upon nanotubical structures obtained by electrolysis in molten salts using non-stationary current regimes. The spectroscopic data enables studying the diameters and performing an (n,m) assignment of nanotube samples. Using the graph representation and the chirality of the studied samples, different distance based topological indices (Wiener, Balaban, Sum-Balaban, and Gutman indices) have been evaluated in order to enable further prediction of index-related properties of the molecules