CARBON-BASED NANOCOMPOSITES: PROCESSING, ELECTRONIC PROPERTIES AND APPLICATIONS

Mapping Intimacies ◽

10.31224/osf.io/dsb7v ◽

2021 ◽

Author(s):

Mainak Saha ◽

Manab Mallik

Keyword(s):

Electronic Properties ◽

Process Parameters ◽

Chemical Properties ◽

Vital Role ◽

Structure Property ◽

Property Correlation ◽

Recent Developments ◽

Present Context ◽

Dependent Parameter ◽

Carbon Based

The last two decades have witnessed a large volume of research revolving around structure-property correlation in carbon-based nanocomposites, synthesized by several methods. In the simplest of terms, the electronic properties of these nanomaterials, which form the present context of discussion, vary mainly as a function of three parameters, out of which two are process parameters (viz. (i) the kind of reinforcement and (ii) method of synthesis), and one is a structure-dependent parameter. The structure-dependent parameter is highly influenced by the two process parameters and plays a vital role in determining the ionic and electronic transport phenomenon in these materials. In other words, the interaction between electrons and the equilibrium 0-D (point) defects, along with different types of 2-D interfaces, plays a crucial role in the understanding of electronic properties, apart from physical and chemical properties of these materials. The present chapter provides a brief overview of the state-of-the-art on research along with detailed discussions on some recent developments in understanding electronic properties of some conventional carbon-based nanocomposites (synthesized by different techniques) based on the structure-property correlation in these materials. Finally, some of the significant challenges in this field have been addressed from both industrial and fundamental viewpoints.

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An overview of recent developments of carbon-based sensors for the analysis of drug molecules

Journal of Electrochemical Science and Engineering ◽

10.5599/jese.999 ◽

2021 ◽

Author(s):

Jamballi G. Manjunatha ◽

Pemmatte A. Pushpanjali ◽

Nagarajappa Hareesha

Keyword(s):

Electrode Materials ◽

Diclofenac Sodium ◽

Chemical Properties ◽

Linear Sweep Voltammetry ◽

Stripping Voltammetry ◽

Carbon Paste ◽

Square Wave ◽

Drug Molecules ◽

Recent Developments ◽

Carbon Based

This review summarizes some recent developments in the fabrication of modified sensors and biosensors using carbon-based materials. The great potential of carbon-based electrodes as sensing platforms is exciting due to their unique electrical and chemical properties, high accessibility and high biocompatibility. Carbon-based materials are particularly interesting due to almost infinite possibility of their functionalization with a wide variety of organic molecules, biologically important compounds and pharmaceuticals. This review is specifically focused on recent developments in the utilization of various carbon-based electrodes in sensing devices for the electrochemical investigation of drug molecules. Various voltammetric techniques considered in this effort include linear sweep voltammetry (LSV), cyclic voltammetry (CV), differential pulse voltammetry (DPV), square wave voltammetry (SWV), and square wave adsorptive stripping voltammetry (SWAdSV). The carbon-based electrode materials considered in this review comprise carbon paste, carbon nanotubes, graphite, graphene, and glassy carbon. The analytes chosen are some routinely used drugs such as paracetamol (PC), diclofenac sodium (DCF), 5-fluorouracil (5-FU), cetirizine (CTZ) and salbutamol (SAL). All here reported sensing electrodes produced very good results in electrochemical investigations of these drug molecules.

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Editorial: Topological investigations of chemical networks

Main Group Metal Chemistry ◽

10.1515/mgmc-2021-0030 ◽

2021 ◽

Vol 44 (1) ◽

pp. 267-269

Author(s):

Muhammad Javaid ◽

Muhammad Imran

Keyword(s):

Information Science ◽

Biological Activities ◽

Chemical Properties ◽

Vital Role ◽

Topological Indices ◽

Molecular Structures ◽

Quantitative Structure ◽

Structure Property ◽

Chemical Structures ◽

Interdisciplinary Field

Abstract The topic of computing the topological indices (TIs) being a graph-theoretic modeling of the networks or discrete structures has become an important area of research nowadays because of its immense applications in various branches of the applied sciences. TIs have played a vital role in mathematical chemistry since the pioneering work of famous chemist Harry Wiener in 1947. However, in recent years, their capability and popularity has increased significantly because of the findings of the different physical and chemical investigations in the various chemical networks and the structures arising from the drug designs. In additions, TIs are also frequently used to study the quantitative structure property relationships (QSPRs) and quantitative structure activity relationships (QSARs) models which correlate the chemical structures with their physio-chemical properties and biological activities in a dataset of chemicals. These models are very important and useful for the research community working in the wider area of cheminformatics which is an interdisciplinary field combining mathematics, chemistry, and information science. The aim of this editorial is to arrange new methods, techniques, models, and algorithms to study the various theoretical and computational aspects of the different types of these topological indices for the various molecular structures.

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Structure-property correlation in glasses by infrared reflectance spectroscopy

Journal of Non-Crystalline Solids ◽

10.1016/s0022-3093(97)00389-x ◽

1997 ◽

Vol 222 (1-2) ◽

pp. 59-68 ◽

Cited By ~ 34

Author(s):

E Kamitsos

Keyword(s):

Reflectance Spectroscopy ◽

Infrared Reflectance ◽

Structure Property ◽

Infrared Reflectance Spectroscopy ◽

Property Correlation

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Graph Neural Networks for Prediction of Fuel Ignition Quality

10.26434/chemrxiv.12280325.v1 ◽

2020 ◽

Author(s):

Artur Schweidtmann ◽

Jan Rittig ◽

Andrea König ◽

Martin Grohe ◽

Alexander Mitsos ◽

...

Keyword(s):

Neural Networks ◽

Octane Number ◽

Molecular Graph ◽

Chemical Properties ◽

Graph Representation ◽

Structure Property ◽

Oxygenated Hydrocarbons ◽

Physico Chemical ◽

Ignition Quality ◽

Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

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Chitosan-Nanocellulose Composites for Regenerative Medicine Applications

Current Medicinal Chemistry ◽

10.2174/0929867327666200127152834 ◽

2020 ◽

Vol 27 (28) ◽

pp. 4584-4592 ◽

Cited By ~ 2

Author(s):

Avik Khan ◽

Baobin Wang ◽

Yonghao Ni

Keyword(s):

Regenerative Medicine ◽

Preparation Method ◽

Chemical Properties ◽

Biological Properties ◽

Next Generation ◽

Structure Property ◽

Physico Chemical ◽

Structure Property Relationship ◽

Attractive Candidate ◽

Fundamental Understanding

Regenerative medicine represents an emerging multidisciplinary field that brings together engineering methods and complexity of life sciences into a unified fundamental understanding of structure-property relationship in micro/nano environment to develop the next generation of scaffolds and hydrogels to restore or improve tissue functions. Chitosan has several unique physico-chemical properties that make it a highly desirable polysaccharide for various applications such as, biomedical, food, nutraceutical, agriculture, packaging, coating, etc. However, the utilization of chitosan in regenerative medicine is often limited due to its inadequate mechanical, barrier and thermal properties. Cellulosic nanomaterials (CNs), owing to their exceptional mechanical strength, ease of chemical modification, biocompatibility and favorable interaction with chitosan, represent an attractive candidate for the fabrication of chitosan/ CNs scaffolds and hydrogels. The unique mechanical and biological properties of the chitosan/CNs bio-nanocomposite make them a material of choice for the development of next generation bio-scaffolds and hydrogels for regenerative medicine applications. In this review, we have summarized the preparation method, mechanical properties, morphology, cytotoxicity/ biocompatibility of chitosan/CNs nanocomposites for regenerative medicine applications, which comprises tissue engineering and wound dressing applications.

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