Bilainu Obozokhai Oboirien

Thermogravimetric analysis (TGA) was used to screen nanometal-modified acrylonitrile–butadiene–styrene (ABS) composites for potential use as hybrid rocket motor fuel grains. ABS formulations containing graphene nanoplatelets (GnP) or reduced graphene oxide (rGO) were doped with aluminium, boron, nickel, copper, and iron nanoparticles at loadings between 1 and 5 wt%. Thermal behaviour was assessed using onset decomposition temperature, peak decomposition temperature, fast-region mass-loss rate, and residual mass as indicators of thermal stability, decomposition kinetics, and condensed-phase residue formation under pyrolysis conditions. Neat ABS exhibited a peak decomposition temperature of 425.64 °C, while most nanoparticle-containing formulations decomposed within a narrow range of approximately 417–430 °C, indicating that the dominant degradation mechanism of ABS was not substantially altered. Aluminium-, boron-, and nickel-containing systems retained peak decomposition temperatures comparable to neat ABS, whereas copper-containing formulations showed a reduced thermal stability at higher loadings, with peak temperatures decreasing to 408.38 °C at 5 wt%. Onset decomposition temperature was more sensitive to additive type and loading, decreasing markedly for copper-containing systems to 331.07 °C at 5 wt%, while boron-containing formulations maintained or increased onset temperatures relative to neat ABS. Residual mass increased systematically with metal loading and varied strongly with metal type, reflecting differences in condensed-phase contribution. Fast-region mass-loss rates for aluminium-, boron-, and nickel-modified systems ranged from 1.61 to 1.84 mg/min, indicating accelerated decomposition kinetics during the primary degradation stage. The combined screening metrics indicate that aluminium-, boron-, and nickel-containing formulations, particularly in GnP-modified ABS, satisfied the predefined selection criteria for subsequent hybrid rocket motor testing. This study demonstrates the use of TGA as a rapid and material-efficient screening tool prior to motor testing.

Nanomaterials (NMTs) production modeling and optimization is a huge task. Even though several authors have been able to model the process, nevertheless, deeper investigation needs to be carried out since the process itself is a combination of several disciplines from physical sciences to engineering. This chapter investigates various research works that have carefully modeled the production process of carbon-based NMTs (mainly carbon nanotubes) over the last two decades. Models such as the machine-learning models, simple weight model, kinetic model, as well as the model derived from chemical kinetic models, are all investigated and presented in this chapter. In addition, reactors such as fixed bed reactors, fluidized bed reactors, screw kilns, and autoclaves have all been employed in NMT production using different techniques. The optimization of NMT production process is carefully evaluated from the works of authors who have found ways to improvise NMT production from a combination of different processes, pre-processes, catalysts, operating conditions, and reactors, amongst several others. To optimize the NMT production process aside from the conditions and variables mentioned, the coproduction of valuable products such as hydrogen gas is essential.

The development of nanotechnology has led to the exploration and discovery of nanomaterials increasing at an exponential rate. This growth presents the challenge of accurately predicting the characterization of nanomaterials at a faster pace. Machine learning (ML), encompassing both traditional ML and deep learning tools, has the ability to address these challenges. This chapter explores the use of several ML models – including artificial neural networks (ANN), support vector machines (SVM), decision trees, convolutional neural networks (CNN), and deep neural networks (DNN) – to successfully predict various nanomaterial properties based on experimental data. These models are meticulously discussed, as well as the details of how they are utilized to predict the physicochemical properties of nanomaterials. Additionally, the challenges currently faced in using these ML tools are examined, along with discussions on the changes that must take place in the near future for ML to make a significant contribution to nanomaterial synthesis.