N-DOPED MULTIWALLED CARBON NANOTUBES: FUNCTIONALIZATION, CHARACTERIZATION AND APPLICATION IN LI ION BATTERIES

The focus of this dissertation is to utilize chemical functionalization as a probe to investigate the reactivity of N-doped multiwalled carbon nanotubes (N-MWCNTs). The surface of N-MWCNTs, being a set of potentially reactive graphene edges, provides a large number of reactive sites for chemical modification, so considerable changes in chemical and physical properties can be envisaged. We observed that both reduction (dissolving metal reduction/alkylation) and oxidation (H2SO4/HNO3 and H2SO4/KMnO4 mixtures) of N-MWCNTs lead to formation of interesting spiral channels and spiraled carbon nanoribbons. A variety of techniques, including TGA, SEM, TEM, XRD and surface area measurements were used to analyze these new textural changes. We have developed methods to demonstrate that specific chemistry has occurred on these new structures. To this end, we introduced metal-binding ligands that could be used as probes in imaging and spectroscopic techniques including TEM, STEM, EDX, and EELS. A proposal for the underlying structure of N-MWCNTs responsible for the formation of the new textures is presented. We have investigated the performance of our materials as potential negative electrodes for rechargeable lithium ion batteries

Rechargeable Batteries Including High-Voltage Cathode and Redox Shuttle Conferring Overcharge Protection

Compounds for use as photoredox catalysts and as redox shuttles in a rechargeable battery having a high-voltage cathode providing overcharge protection capabilities are provided, including a compound according to the formula: (see patent for formula) wherein R is selected from the group consisting of alkyl, aryl, alkylaryl, alkoxyaryl, alkylcarboxyl, aryl carbonyl, haloalkyl, perfluoroalkyl, glycols, haloaryl, a negative electrolyte, and a polymer

Liquid Phenothiazine Catholytes for Non-Aqueous Redox Flow Batteries

Highly soluble, liquid phenothiazines containing methoxy-terminated ether and oligoether substituents are disclosed with high diffusion coefficients and robust performance in electrochemical measurements, which can be synthesized in one step from commercially-available starting materials, thereby circumventing previous synthetic limitations

Oxidation of N-Doped Multiwalled Carbon Nanotubes and Formation of Discontinuous Spiraled Carbon Nanoribbons

The effects of five commonly used wet chemical oxidations were studied for the extent of oxidation of graphitized nitrogen-doped multiwalled carbon nanotubes (N-MWCNTs). KMnO4/ H2SO4 was the most potent oxidant, as it produced the highest fraction of oxygen-containing functional groups. Electron microscopy studies showed that the treatment of annealed N-MWCNTs (G-N-MWCNTs) with H2SO4/HNO3 and H2SO4/KMnO4 mixtures leads to interesting spiraled ribbon textures. A structural model, involving the stacking of coiled subunits to form a discontinuous carbon nanoribbon rather than a continuous ribbon is proposed to explain the range of textures that result from oxidation as well as from reduction

Two-Electron Donating Phenothiazines and Use Thereof

Compounds for use as electrolyte in a non-aqueous redox battery are provided, including an N-substituted phenothiazine compound according to the formula: (see patent for formula.) R\u27 is alkyl and R is selected from alkyl, aryl, alkylaryl, alkoxyaryl, alkylcarboxyl, arylcarbonyl, haloalkyl, perfluo- roalkyl, glycol, polyether, haloaryl, a negative electrolyte, a polymerizable unit, and a polymer

Non-Aqueous Redox Flow Batteries Including 3,7-Perfluoroalkylated Phenothiazine Derivatives

A non-aqueous redox flow battery includes a negative electrode immersed in a first non-aqueous liquid electrolyte solution, a positive electrode immersed in a second nonaqueous liquid electrolyte solution, and a semi-permeable separator interposed between the negative and positive electrodes, wherein the second the non-aqueous liquid electrolyte solution comprises a compound of the formula... To see the remainder of this abstract, please download this patent

Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016

A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment

The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models