«Dissertation zur Erlangung des akademischen Grades Doktoringenieurin (Dr.-Ing.) von: Yashodhan Pramod Gokhale geboren am: 05. October 1981 in Pune, ...»
Das Populationsbilanzmodell in dieser Arbeit wird numerisch auf Grundlage der sogenannten Cell-Average-Methode gelöst. Die Ergebnisse der Simulationsrechnungen auf Basis der Populationsgleichungen unter Verwendung unterschiedlicher Ansätze für die jeweiligen Agglomerations- und Desintegrationskerne werden mit den experimentellen Ergebnissen verglichen. Die experimentellen Partikelgrößenverteilungen können durch die Simulationsergebnisse für verschiedene Schergeschwindigkeiten wiedergegeben werden. Das beinhaltet einen Vergleich der berechneten Partikelgrößenverteilungen bzw. Momente sowie deren Genauigkeit in Abhängigkeit von den am Beginn vorliegenden Partikelgrößenverteilungen.
Die vorliegende Arbeit zeigt, dass die Partikelgröße, Morphologie und Monodispersität der kolloidalen Partikel aus Silber bzw. Titan(IV)-oxid durch zwei Prozesse gesteuert werden können, einerseits durch die geeignete Wahl von Stabilisatoren und Reduktionsmitteln, andererseits durch den Zusatz von Tensiden, Polymeren oder Elektrolyten während des Herstellungsprozesses.
1 Nanoparticles, Motion and Life
1.2 Problem and Motivation
1.3 Outline of Contents
2 Fundamental Aspects
2.1 Nano Scale Materials
2.2 Synthesis of Nano Materials
2.3 Different methods for synthesis of Silver and TiO2 nanoparticles
2.3.1 Synthesis of silver nanoparticles by different processes
2.3.2 Sol-gel synthesis
2.3.3 Synthesis of titanium dioxide nanoparticles by different methods
2.3.4 Synthesis of Surfactant based nanoparticles by different methods
2.4 Colloidal Particles
2.5 Interparticle Forces
2.5.1 Van der Waals Attraction Forces
2.5.2 Electrostatic Repulsion Forces
2.5.3 DLVO theory
2.5.4 Steric Interaction
2.6 Colloidal Stabilization
2.6.1 Steric Stabilization
2.6.2 Electrostatic Stabilization
2.6.3 Zeta Potential
3 Characterization methods of Nanoparticles
3.1 Particle Size Distribution
3.2 Dynamic Light Scattering- DLS
3.2.1 Principle of Measurement
3.2.2 Non-Invasive Back-Scatter (NIBS)
3.2.3 Operation of the Zetasizer Nano-Size measurements
3.3 Low Angle Laser Light Scattering (LALLS)
3.4 Zeta Potential Measurement
3.4.1 Laser Doppler Electrophoresis
3.4.2 Measuring Electrophoretical Mobility
3.4.3 Laser Doppler Velocimetry
3.4.4 Operation of the Zetasizer Nano- Zeta potential measurements
3.5 Scanning Electron Microscope - SEM
3.6 Transmission Electron Microscopy-TEM
4 Experimental Set up and Synthesis of Materials
4.1 Experimental Set up
4.1.1 Types and Characteristics of Stirrer
4.1.2 Apparatus and Experimental Design
4.2 Silver nanoparticles synthesis
4.2.1 Experimental Method for Silver
18.104.22.168 Double reduction method for synthesis of silver nanoparticles.................. 58 22.214.171.124 Production of colloidal silver
4.3 Titanium dioxide nanoparticles synthesis
4.3.1 Experimental method for Titanium dioxide
126.96.36.199 Sol-gel synthesis of TiO2
188.8.131.52 Surfactant based Titania nanoparticles
5 Population Balance Modeling
5.2 Recent survey
5.3 Kinetics of the Simultaneous Agglomeration and Disintegration SubProcesses
5.3.1 Agglomeration Sub-Process
5.3.2 Disintegration Sub-Process
5.3.3 The Moment Form of the Population Balance
5.4 Kernels of the Agglomeration and Disintegration Kinetics
5.4.1 Agglomeration rate kernel
5.4.2 Convection-Controlled Agglomeration
184.108.40.206 Laminar Flow
220.127.116.11 Turbulent Flow
5.4.3 Diffusion- Controlled Agglomeration
5.4.4 Relative Sedimentation
5.4.5 Effects of hydrodynamic interactions
5.4.6 Comparison of Agglomeration Kernels:
5.4.7 Disintegration rate kernel
18.104.22.168 Austin Kernel
22.214.171.124 Diemer Kernel
5.4.8 Comparison of Disintegration Kernels
5.5 Methods to Solve the Population Balance Equations
5.5.1 Numerical Methods
5.5.2 Cell Average Technique- CAT
6 Experimental and Modeling Results
6.1 Experimental results of silver nanoparticles
6.1.1 Effect of Capping Agent
6.1.2 Effect of Reducing Agent
6.1.3 Effect of Shear Rate on the particle size distribution
6.1.4 Morphology and Particle Size Distribution
126.96.36.199 Scanning Electron Microscopy (SEM)
188.8.131.52 Transmission Electron Microscopy (TEM)
6.2 Experiment and Modeling of Titanium dioxide nanoparticles
6.2.1 Simultaneous process of agglomeration-disintegration of titanium dioxide.... 108 184.108.40.206 Austin kernel and Shear kernel
220.127.116.11 Diemer Kernel and Shear kernel
18.104.22.168 Effect of Sum and Austin kernel on PSD
22.214.171.124 Effect of Sum and Diemer kernel on PSD
126.96.36.199 Effect of Process parameters on particle size distributions
6.2.2 Disintegration of Surfactant based Titanium dioxide
188.8.131.52 Effects of Different Surfactants
7 Conclusions and Outlook
A. Shear Rate Calculation
B. Disintegration function from normalized cumulative disintegration
Nomenclature 7 Nomenclature
Subscripts agg Aggregation disn Disintegration break Breakage nuc Nucleation i; j Index Acronyms CAT Cell Average Technique FPT Fixed Pivot Technique ODE Ordinary Differential Equation PBE Population Balance Equation PSD Particle Size Distribution
Chapter 1Nanoparticles, Motion and Life
1 Nanoparticles, Motion and Life
1.1 Introduction N anoscience is a scientific effort towards achieving complete control over of atoms, molecules and larger atomic structures including surfaces and bulk material. This control at the most basic level does not, however, come without difficulty, and at this point basic science is struggling to understand even the simplest building blocks and how they interact. Once this understanding is secured, nanotechnology will be apt to affect every aspect of human life, from the way we produce energy to the way we cure diseases. The basis of all life is molecular motion. As the great physicist Richard Feynman (Feynman, Leighton et al.
1995) said “If, in some cataclysm, all of scientific knowledge were to be destroyed, and only one sentence passed on to the next generations of creatures, what statement would contain the most information in the fewest words? I believe it is the atomic hypothesis (or the atomic fact, whatever you wish to call it) that all things are made of atoms – little particles that move around in perpetual motion, attracting each other when they are a little distance apart, but repelling upon being squeezed into one another. In that one sentence, you will see, there is an enormous amount of information about the world, if just a little imagination and thinking are applied.” Controlling the physical and chemical properties of materials requires detailed knowledge about the behavior of the atoms and their interplay with other atoms in their surroundings. It also requires materials that will allow the manipulations to result in a broad range of properties. Metal oxides are proving to be a very interesting group of materials in this respect, because they cover the entire range of properties available; some are high superconducting while other are insulators, some are magnetic others not, and both their optical and mechanical properties vary a great deal.
Nanoparticulate metal clusters/colloids are deﬁned as isolable particles in the nanometer size range, which are prevented from agglomeration by protecting shells. They can be redispersed in water (hydrosols) or organic solvents (organosols). The number of potential applications of these colloidal particles is growing rapidly because of the unique electronic structure of the nanosized metal particles and their extremely large surface areas (J.Turkevich, P.C.Stevenson et al. 1951). Highly dispersed mono and bimetallic colloids can be used as precursors for a new type of catalyst that is applicable both, in the homogeneous and heterogeneous phases (Schmid. 1996). Nanoparticles, comprised of one or two diﬀerent metal elements, are of considerable interest from both the scientific and technological points of view (Rodriguez and Goodman 2002).
Many efforts have been made to develope appropriate processes to prepare silver and titania nanoparticles for generating colloidal particles due to their technological importance. Silver nanoparticles and titanium oxide particle coating is a very important material due to its multifunctional application in solar cells, anti-reflective optical coatings, hydrophobic materials, photochromic and electrochromic devices, gas sensors, biosensors, corrosion protection, bactericides, optical devices, among others (Daoud and Xin 2004; Toma, Bertrand et al. 2006) One of the fundamental issues that need to be addressed in modeling macroscopic mechanical behavior of nano-structured materials based on molecular structure is the large difference in length scales. On the opposite end of the length scale, the spectrum of computational chemistry and solid mechanics consists of highly developed and reliable modeling methods.
Computational chemistry models predict molecular properties based on known quantum interactions, while computational solid mechanics models predict the macroscopic mechanical behavior of materials idealized as continuous media based on known bulk material properties.
However, a corresponding model does not exist in the intermediate length scale range. If a hierarchical approach is used to model the macroscopic behavior of nano-structured materials, then a methodology must be developed to link the molecular structure and macroscopic properties.
Many properties of solid particles are not only a function of the material‟s bulk properties but also depend on the particle size distribution (PSD). These property changes arise from the increasing inﬂuence of surface properties in comparison to volumetric bulk properties as the particle size decreases. Especially nanoscaled particles show altered properties and have therefore widespread applications like pigments, pharmaceuticals, cosmetics, ceramics, catalysts and ﬁlling materials. Since the desired product properties might vary with particle size as well as with the degree of aggregation or the aggregate structure, controlling of the PSD and the aggregate structure is a key criterion for product quality. New and improved products can then be designed by adjusting and optimizing the PSD and the particle structure.
Precipitation is a promising method for the economic production of commercial quantities of nanoparticles as it is fast and operable at an ambient temperature. However, process control due to the rapidity of the involved sub-processes and especially to prevent aggregation through stabilization represents a challenge.
To control these sub-processes, balance models are used in particle technology. Population balances for agglomeration and disintegration appear in a wide range of applications including nano-technology, granulation, crystallization, atmospheric science, physics and pharmaceutical industries. There are several numerical methods such as Monte Carlo, ﬁnite element, ﬁnite volume, sectional approaches to solve the agglomeration and disintegration population balance equations (Israelachvili 1985; F. Einar Kruis, Arkadi Maisels et al. 2000).
1.2 Problem and Motivation
In recent times, oxide and noble materials have attracted special attention and a lot of research is concentrated on the synthesis of silver and titania nanoparticles by various techniques. The objective of this work is to synthesize silver nanoparticles by means of chemical double reduction method with further stabilization by means of capping agents utilizing different long chain acid. Silver nanoparticles are also made in the liquid phase using reducing agents on a laboratory scale. This technology has several advantages over conventional methods. Nano-sized particles especially those less than fifty nanometers (50 nm) are receiving significant attention in industries. Numerous industries apply nano-scale materials in their operations.
The objective of the thesis is to study the agglomeration and disintegration process of TiO2 nanoparticles by using sol-gel synthesis. Also it is important to study the effect of parameters like stirrer speeds, electrolyte solution, and pH during agglomeration and disintegration kinetics of titanium dioxide nanoparticles. Characterization of diffusion driven disintegration process was taken from the particle size distributions measured in the dynamic light scattering and low angle laser light scattering in order to follow the agglomeration and redispersion kinetics. The experimental results have been used for simulation by the mathematic modeling.
The population balance model for agglomeration disintegration leads to a system of integro-partial diﬀerential equations which is numerically solved by the cell average technique. This includes a comparison of the derived particle size distributions, moments and its accuracy depending on the starting particle size distribution. The experimental results are also compared with the simulation using diﬀerent agglomeration and disintegration kernels.
In this thesis, we investigate the synthesis of surface stabilized TiO2 nanoparticles with diﬀerent surfactants. The steric stabilization of the polymer and various functional groups of dispersants are also considered. The inﬂuence of various precursor concentrations and different surfactants on the particle size distribution is investigated. The population balance model for disintegration process is numerically solved by the cell average technique. The experimental results are also compared with the simulation using two diﬀerent disintegration kernels.