Dynamic simulation of chemical plant

A CSSL- type modular FORTRAN package, called ACES, has been developed to assist in the simulation of the dynamic behaviour of chemical plant. ACES can be harnessed, for instance, to simulate the transients in startups or after a throughput change. ACES has benefited from two existing simulators. The structure was adapted from ICL SLAM and most plant models originate in DYFLO. The latter employs sequential modularisation which is not always applicable to chemical engineering problems. A novel device of twice- round execution enables ACES to achieve general simultaneous modularisation. During the FIRST ROUND, STATE-VARIABLES are retrieved from the integrator and local calculations performed. During the SECOND ROUND, fresh derivatives are estimated and stored for simultaneous integration. ACES further includes a version of DIFSUB, a variable-step integrator capable of handling stiff differential systems. ACES is highly formalised . It does not use pseudo steady- state approximations and excludes inconsistent and arbitrary features of DYFLO. Built- in debug traps make ACES robust. ACES shows generality, flexibility, versatility and portability, and is very convenient to use. It undertakes substantial housekeeping behind the scenes and thus minimises the detailed involvement of the user. ACES provides a working set of defaults for simulation to proceed as far as possible. Built- in interfaces allow for reactions and user supplied algorithms to be incorporated . New plant models can be easily appended. Boundary- value problems and optimisation may be tackled using the RERUN feature. ACES is file oriented; a STATE can be saved in a readable form and reactivated later. Thus piecewise simulation is possible. ACES has been illustrated and verified to a large extent using some literature-based examples. Actual plant tests are desirable however to complete the verification of the library. Interaction and graphics are recommended for future work.

Reinforced thermoplastics for engineering applications

The effect of mechano-chemically bound polypropylene modifiers on the mechanical performance and thermal-oxidative stability of polypropylene composites has been studied. The mechanical performance of unmodified polypropylene containing silane coupled glass and Rockwool (mineral) fibre was poor by comparison with a similar commercially produced glass reinforced composite; this was attributed to poor fibre-matrix adhesion. Mechano-chemical binding with unsaturated additives was obtained in the presence of a free radical initiator (di-cumyl peroxide). This process was inhibited by stabilisers present in commercial grades of polypropylene composites by chemical bond formation between the chemically bound modifier and the silane coupling agent on the fibre surface, resulting in a dramatic improvement in the mechanical properties, dimensional stability and retention of mechanical performance after immersion in fluids typically found in under-bonnet environments.A feature unique to some of these modifiers was their ability not only to enhance the mechanical properties of polypropylene composites to levels substantially in excess of currently available commercial materials, but their ability to act as effective thermal-oxidative polypropylene stabilisers. The mode of action was shown to be a chain-breaking mechanism and as a result of the high binding levels achieved during melt processing, these modifiers were able to efficiently stabilise polypropylene in the most severe volatilising and solvent-extracting environments, thus giving much better protection to the polymer than currently available commercially stabilised grades of polypropylene.

Etching and microstructure of engineering ceramics

Engineering ceramics are often difficult to prepare metallographically because of their hardness, wear resistance and chemical inertness. Two silicon carbides, a silicon nitride and a sialon, are prepared and etched using several different techniques. The most efficient methods are identified. © 1995.

Salting out in organic solvents:a new route to carbon nanotube bundle engineering

In this study we investigate salt effects on bundle formation of carbon nanotubes (CNTs) dispersed in an organic solvent, N-methyl-2-pyrrolidone (NMP). Addition of NaI salt leads to self-assembly of CNTs into well-recognizable bundles. It is possible to control the size of the CNT bundles by varying the salt concentration. © the Owner Societies 2011.

Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Mechanical and electrical properties of single-walled carbon nanotubes synthesized by chemical vapor deposition

Despite the tremendous application potentials of carbon nanotubes (CNTs) proposed by researchers in the last two decades, efficient experimental techniques and methods are still in need for controllable production of CNTs in large scale, and for conclusive characterizations of their properties in order to apply CNTs in high accuracy engineering. In this dissertation, horizontally well-aligned high quality single-walled carbon nanotubes (SWCNTs) have been successfully synthesized on St-cut quartz substrate by chemical vapor deposition (CVD). Effective radial moduli (Eradial) of these straight SWCNTs have been measured by using well-calibrated tapping mode and contact mode atomic force microscopy (AFM). It was found that the measured Eradial decreased from 57 to 9 GPa as the diameter of the SWCNTs increased from 0.92 to 1.91 nm. The experimental results were consistent with the recently reported theoretical simulation data. The method used in this mechanical property test can be easily applied to measure the mechanical properties of other low-dimension nanostructures, such as nanowires and nanodots. The characterized sample is also an ideal platform for electrochemical tests. The electrochemical activities of redox probes Fe(CN)63-/4-, Ru(NH3) 63+, Ru(bpy)32+ and protein cytochrome c have been studied on these pristine thin films by using aligned SWCNTs as working electrodes. A simple and high performance electrochemical sensor was fabricated. Flow sensing capability of the device has been tested for detecting neurotransmitter dopamine at physiological conditions with the presence of Bovine serum albumin. Good sensitivity, fast response, high stability and anti-fouling capability were observed. Therefore, the fabricated sensor showed great potential for sensing applications in complicated solution.^

Site specific growth of metal catalyzed silica nanowires for biological and chemical sensing

In this research the integration of nanostructures and micro-scale devices was investigated using silica nanowires to develop a simple yet robust nanomanufacturing technique for improving the detection parameters of chemical and biological sensors. This has been achieved with the use of a dielectric barrier layer, to restrict nanowire growth to site-specific locations which has removed the need for post growth processing, by making it possible to place nanostructures on pre-pattern substrates. Nanowires were synthesized using the Vapor-Liquid-Solid growth method. Process parameters (temperature and time) and manufacturing aspects (structural integrity and biocompatibility) were investigated. Silica nanowires were observed experimentally to determine how their physical and chemical properties could be tuned for integration into existing sensing structures. Growth kinetic experiments performed using gold and palladium catalysts at 1050°C for 60 minutes in an open-tube furnace yielded dense and consistent silica nanowire growth. This consistent growth led to the development of growth model fitting, through use of the Maximum Likelihood Estimation (MLE) and Bayesian hierarchical modeling. Transmission electron microscopy studies revealed the nanowires to be amorphous and X-ray diffraction confirmed the composition to be SiO2 . Silica nanowires were monitored in epithelial breast cancer media using Impedance spectroscopy, to test biocompatibility, due to potential in vivo use as a diagnostic aid. It was found that palladium catalyzed silica nanowires were toxic to breast cancer cells, however, nanowires were inert at 1μg/mL concentrations. Additionally a method for direct nanowire integration was developed that allowed for silica nanowires to be grown directly into interdigitated sensing structures. This technique eliminates the need for physical nanowire transfer thus preserving nanowire structure and performance integrity and further reduces fabrication cost. Successful nanowire integration was physically verified using Scanning electron microscopy and confirmed electrically using Electrochemical Impedance Spectroscopy of immobilized Prostate Specific Antigens (PSA). The experiments performed above serve as a guideline to addressing the metallurgic challenges in nanoscale integration of materials with varying composition and to understanding the effects of nanomaterials on biological structures that come in contact with the human body.

Engineering metallic nanoparticles for enhancing and probing catalytic reactions

Recent developments in tailoring the structural and chemical properties of colloidal metal nanoparticles (NPs) have led to significant enhancements in catalyst performance. Controllable colloidal synthesis has also allowed tailor-made NPs to serve as mechanistic probes for catalytic processes. The innovative use of colloidal NPs to gain fundamental insights into catalytic function will be highlighted across a variety of catalytic and electrocatalytic applications. The engineering of future heterogenous catalysts is also moving beyond size, shape and composition considerations. Advancements in understanding structure-property relationships have enabled incorporation of complex features such as tuning surface strain to influence the behavior of catalytic NPs. Exploiting plasmonic properties and altering colloidal surface chemistry through functionalization are also emerging as important areas for rational design of catalytic NPs. This news article will highlight the key developments and challenges to the future design of catalytic NPs.

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Chemical composition of nodules from two locations in the Pacific Ocean using different methods

Manganese nodules recovered in the Pacific Ocean by the U. S. Bureau of Mines and by DeepSea Ventures Ltd. are studied for their chemical composition using X microprobe and X-ray fluorescence methods.