Synthesis and characterisation of aliphatic hyperbranched polyamidoamines and polyamides

Dendrimers are perfectly branched macromolecules possessing large internal cavities and a known number and location of terminal groups. This unique architecture leads to many interesting properties and countless potential applications have been discussed. Their synthesis involves numerous repetitive steps, often requiring protection and deprotection chemistry and complex purification procedures. This limits their availability and leads to extremely high costs, a factor that has limited their use. Polyamidoamine (PAMAM) dendrimers, the first well-established series of dendrimers, were reported in the mid nineteen eighties. For many applications the synthetic difficulties associated with dendrimers are so great that many potential applications are prohibited. Hyperbranched polymers are produced by a simpler synthetic route, the step growth polymerisation of AB(_x) monomers in a one-pot procedure. They lack the architectural perfection of dendrimers but retain the large number of terminal groups and high degree of branching. Crucially, these polymers can be produced for a fraction of the cost of dendrimers. The synthesis of hyperbranched analogues to both the full and half generation PAMAM dendrimers from AB(_2) monomers is reported here. Attempts to extend this method to control the molecular weight, degree of branching and the terminal group functionality are discussed, as is the synthesis of a related series of polyamides. The characterisation of these materials and their physical properties are also described

Synthesis of nanocomposites of difference architectures and applications based on copper, nickel and alumina

Considering that nanostructural materials are expected to have special physical and mechanical properties, in the recent years the examinations of synthesis and characterization of the nanocomposite system attracts even greater scientific interest. This paper presents production of sintered contacts materials produced from nanocomposite powders obtained by combination of thermochemical synthesis of Cu-Al2O3 powder and mechanical alloying of atomized copper powder with previously sinthetized Cu-Al2O3 powder. Produced powders were characterized by X-ray diffraction and Analytical Electron Microscopy. Characterization of sintered samples included Scanning Electron Microscopy (SEM), Energy Dispersive Spectrometry (EDS), measurement of hardness and specific electrical conductivity. By thermochemical method of Cu-Al2O3 nanocomposite synthesis, i.e. deposition from aqueous solutions, in combination with mechanical alloying, significant effects of reinforcement were achieved as a result of homogenous distribution of alumina in the nanocomposite system. In combination with conventional methods, thermochemical process of nanocomposite powders synthesis could be successfully applied for synthesis of new nanocomposite catalysts, which are characterized by a high degree of dispersion of the catalytically active component, respectively the catalyst with high activity and selectivity. The high degree of dispersion is the result of uniform distribution of the catalytically active component into alumina suspension, realized during the thermochemical treatment in the synthesis of nanocomposite catalysts. In accordance with this, the paper shows the synthesis of Ni/Al2O3 and Ni-Pd/Al2O3 nanocomposite catalysts with homogeneously dispersed Ni particles, as catalytically active component, and Pd, as activity modifier, supported on ceramic Al2O3 based foam. Namely, the previous synthesized monolith was soaked in a mixed alumina suspension with NiCl2, PdCl2 and appropriate organic additives in order to obtain a nanocomposite catalysts with homogeneous distribution of catalytically active components. Characterization of obtained Al2O3 foam, as the active catalytic components primary carrier, and synthesized nanocomposite catalysts included SEM, EDS, gas permeability and mechanical properties. Synthesis of nanocomposite materials with homogeneous distribution of particles on the nanometer level may lead to formation of new materials with improved or even unexpected properties

Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality

As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach. Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties. Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel

ADC 11(2)

Article Abstrac

Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality

As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach. Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties. Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel