1. 1. A Numerical Model of Template based Chemical Vapor Deposition Process for Carbon Nanotube Manufacturing 1 Thesis Proposal Thursday, July 31th, 2014 Yashar Seyed Vahedein Thesis Committee Members: Michael G. Schrlau, PhD; Mechanical Engineering (Advisor) Robert Parody, PhD; Statistician Steven Day; PhD; Mechanical Engineering Patricia Taboada-Serrano, PhD; Chemical and Biomedical Engineering Agamemnon Crassidis, PhD; Mechanical Engineering
2. 2. Nano-Bio Convergence Molecular Switch DNA barcode Molecular Imaging Biochip / Biosensor Nano-therapy / Delivery Bio-TechnologyNano-Technology Bionano-machine / Nano-Robot Bio-inspired device and system Development of tools and methods More sensitive More specific Multiplexed More efficient and economic Implementation: Diagnosis and treatment of diseases Rapid and sensitive detection (Biomarkers, Imaging) Targeted delivery of therapeutics Drug development Understanding of life science
3. 3. Analytical tools : Atomic force microscopy(AFM), Electron microscopy (EM) Nano-sized materials Magnetic nanoparticles (Ferromagnetic, super paramagnetic) Gold or Carbon nanotubes Quantum dots (Semiconductor nanocrystals) Carbon nanotubes: Properties: High Thermal conductivity(3500 > diamond), strength(>100 Gpa), durability relative to their small size, one dimensional transport. Applications in nanobiotechnology: Intracellular electrochemistry, drug delivery and fluid injection (single cell analysis) and etc. Ideal for single cell analysis. Useful in many different fields, e.g electronics, optics and etc. Example of Tools in Nano-biotechnology
4. 4. Carbon Nano Tubes (CNTs) Development Research groups What they did Impact (L. V.Radushkevich, V. M. Lukyanovich 1952) Multiwall nano tubes First to find imperfect CNTs (Iijima 1991) Highly perfect multiwall carbon nano tubes, used arc discharge And (Thess et al. 1996) used Laser ablation to manufacture CNTs (Iijima and Ichihashi 1993) Single Wall CNTs Dragged attention of Researchers (Zhang and Li 2009) Review of some other types Bent, waved, helically coiled, branched and beaded CNTs (Choy 2003) Review on those Used Chemical Vapor Deposition Introduced as most efficient way of making CNTs (Kyotani, Tsai, and Tomita 1995), (Martin 1994), (Schrlau et al. 2008) Template Based-CVD (TB-CVD) Can produce perfectly aligned amorphous CNTs (Sarno et al. 2012) (Ciambelli et al. 2011) Structural analysis of CNTs made by TB-CVD Effect of deposition time, temperature, gas mixture on CNT synthesis 4(M. Golshadi, J. Maita, D. Lanza, M. Zeiger, V. Presser, M. G. Schrlau) 2.5 m Study effect of gas flow rate, temperature of furnace and time of process on CNT synthesis using TB-CVD
5. 5. Schematics of the TB-CVD Setup in NBIL1 1. NBIL: Nano Bio Interface Laboratory 5 Exhaust Heated region - causing deposition Heater Heater Flow meter Precursorgas Carriergas Temperature knobs Position of the templates Carbon deposited in a template
6. 6. Template based manufacturing CVD Experiment CVD Simulation fabricating CNTs Need for template based manufacturing of CNTs Output of the processNBIL Single cell analysis Electrical,Bio ,nano, Mechanical app. High conductivity & strength Inlet Gasses AAO Template Furnace Dimensions Temperature and Flow rate NBIL 6 Motivations: Control the process and the effect of the parameters on deposition. save time and resources by simulating the process Create a universal method to be used by others for TB-CVD (not currently available in literature) Diagram of the Driving Needs, Process and Outcome of TB-CVD
7. 7. Research Questions and Plan 7 How can temperature profile and flow characteristics near the templates be identified? Is it possible to simulate the deposition due to CVD process for a single (>50nm) nano-pore in a template and to predict the deposition rate of carbon for different flow rates and temperatures? Is it possible to create a more flexible and comprehensive model that can predict deposition in different flow, temperature and furnace conditions? Schematics of the CVD 2 2. Model by Spear 1982
8. 8. 8 Research groups What they did Impact (Oberlin, Endo, and Koyama 1976b),(Tibbetts, Devour, and Rodda 1987), (T. Kato, K. Haruta 1992) & etc. Understanding reactor operation and product morphology Illustrated importance of CFD on vapor grown carbon fibers (VGCFs) (Endo et al. 2004a), (Kazunori Kuwana and Saito 2005), (Kazunori Kuwana, Li, and Saito 2006) Predicted Carbon deposition rate for catalytic decomposition of xylene Modeled catalytic CVD process including reactions using CFD (He, Li, and Bai 2011)- with 3 stage heating tube furnace Investigated non-uniform nanotube growth in horizontal CVD reactor and suggested changes for experimental setup accordingly 2D model and Experiment on space dependent growth rate, temperature and flow structure coupled with pyrolysis kinetics for samples (Mishra and Verma 2012) 2D, CFD simulation on the vertical furnace Made modifications on the CFD code to raise the accuracy of the model (Ibrahim and Paolucci 2011b), (Zhou and Wolden 2003), (Cheng, Li, and Huang 2008) & etc. Information on mass, momentum, concentration and energy conservation in porous media Provided information on how to model reactions in porous media Use of Computational Fluid Dynamics(CFD) on CVD Simulation
9. 9. The Gap in Simulations Conducted so Far and Significance of This Work CNT synthesis using TB-CVD is controlled by parameters such as: Temperature of the furnace, Flow rate and Time of the process. No simulations have been found on TB-CVD processes without catalyst and using only temperature as the reaction activator (Raji and Sobhan 2013). Numerical models provided useful information about similar experimental setups. Therefore a CFD simulation is suggested to provide insight on the fundamentals of the TB-CVD process being run in NBIL and to predict the carbon deposition rate. 9
10. 10. 10 Schematics of The Problem Pore size bigger than 50nm = continuum regime Inlet flow rates=20 to 300 sccm
11. 11. Heated Walls 1 = 668.160 Mass Flow Inlet = 3.93 07 to 5.9 06 Static pressure = 0 D=3.88 mm and 4.88 mm above the bottom wall 1 = 688.160 1 = 668.160 = 0 5 304.8 = 0 = 0 No slip condition on tube wall: = 0 Boundary Conditions of the Furnace 11
12. 12. Velocity and temperature profiles or Deposition rate as Output 3D Steady state and/or transient Simulation of the Processes Iterating continuity, momentum, energy and species conservation eq. Defining Species Transport Model With Reactions for gas decomposition and deposition of substances Initial Velocity, Species Concentration Creating the Meshed Model UDF 3 Development Creating a Numerical Model for Reactions and/or velocity in the model to be able to code them Defining Boundary Conditions Velocity, Temperature, Concentration Field and Reaction Rates 3. UDF: User Defined Function for implementing in FLUENT code Repeating the process for different conditions of the furnace and parameters Simulation Steps 12
13. 13. Trend of the Model Development 13 v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 2D 3D Coarse mesh Fine mesh Adapted Mesh Residuals 10e-3 Residuals 10e-4 Residuals 10e-6 SIMPLE Solver SIMPLEC Solver PISO Solver COUPLED Solver COUPLED Solver - Psuedo Transient Laminar Turbulent - standard k-epsilon Constant Fluid Properties Energy equation on Ideal gas-temp dep Cp Temp dep Visc, Thermal cond. Shell Conduction Tube Furnace Just 60 SCCM 20, 40, 60, 80, 100, 150, 300 SCCM Flow Rate 20, 40, 60, 80, 100, 200, 300 Flow Rate axisymmetric scaling fixed Temp fixed based on exp data fixed inlet Diameter Sample Boat 2D Boat 3D VERSION SPECS talkabout whythis semi-transientsolver has beenusedand whyit isnot needed in 2D meshstudy, meshadaption Solversettings ResidualStudy Odd V# Even V# Tried parameters Semi- transient solver is needed for 3D but not in 2D Mesh study, mesh adaption dimensionality Mesh quality and adaption Residual study Solver study for best convergence Flow regime Properties, Equations, boundary conditions Different Flow rates Modification on model Sample and boat model
14. 14. Expected Flow Conditions Inside the Tube 14 (Fotiadis and Jensen 1990) - Smoke-test - Interference holography(Giling 1982) = = 3968.4063 (In 60 sccm 4000>Reynolds at inlet>2300, therefore Laminar ) Re inside tube = = 220 States Laminar flow inside the tube = 3 2 = 1.212980888 8, = 0.00113, = . = 4.75 106 > 3 105 Turbulent (Chiu et al. 2000) What we would expect to see in simulation Cross Flows Recirculation
15. 15. 2D Vs. 3D 15 Boltzmann number =16.875 so Radiation is neglected 2D does not capture the uniform Temperature region correctly 3D model is able to capture the cross flows in YZ plane
16. 16. Comparing Mesh and Geometry of Two Modelled Cases w and w/out Boat (Holder) 16 Mesh Report - CASE 2 : BOAT AND TEMPLATES Mesh Information Domain Nodes Elements gas 1711039 1367244 Mesh Report - CASE 1 : ONLY TEMPLATES Mesh Information Domain Nodes Elements gas 516040 498575 Boat Two solid circles as templates 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0 500000 1000000 1500000 2000000 Velocitymagnitude(m/s) Number of mesh elements Finding Mesh Independent Solution Boundary layer meshing
17. 17. 17 Temperature Data from Simulation showing the same trend with Experimental Results 645 650 655 660 665 670 675 680 685 690 695 2 2.25 2.5 2.75 3 Temperature(C) Length (ft) Temperature vs Length (Flow Rate = 500 sccm experiment and simulation) Radial Position = 0 (inch) Data from simulation 500 SCCM near wall Radial Position = 1 (inch) Data from simulation 500 SCCM 1 Inch Radial Position = 1.5 (inch) Data from simulation 500 SCCM 1.5 inch Boat 0.47582 ft Potential reasons for data disagreement piece-wise linear temperature dependent properties. Tube considered to be isolated from outside(in simulation). Thermal resistivity of the tube has been neglected and temperature considered to be uniform on each zone. Accuracy of the sensors may create mistakes. Shape of the sensor which will affect the flow has not been considered in the CFD model
18. 18. Properties on cross-section along longitude:20 and 300sccm Recirculation is the main cause for having a curve shaped temperature distribution along the tube. Recirculation regions Recirculation regions
19. 19. Temperature Contours on Mid Cross-Section of the Tube Furnace 0.025m scale 20 sccm 40 sccm 60 sccm 80 sccm 300 sccm200 sccm100 sccm =0.025 Nearly symmetric distribution Sample & Boat TurbulentLaminar = 13228.021 = 733.33 =4.75 106 = 6614 = 366.66 = 4.75 106
20. 20. Temperature distribution on middle-cross section 961.550961.547961.544961.541961.538961.535961.532 Median Mean 961.54300961.54275961.54250961.54225961.54200961.54175961.54150 1st Q uartile 961.54 Median 961.54 3rd Q uartile 961.55 Maximum 961.55 961.54 961.54 961.54 961.54 0.01 0.01 A -Squared 36.97 P-V alue < 0.005 Mean 961.54 StDev 0.01 V ariance 0.00 Skewness -0.32227 Kurtosis -1.06591 N 2327 Minimum 961.53 A nderson-Darling Normality Test 95% C onfidence Interv al for Mean 95% C onfidence Interv al for Median 95% C onfidence Interv al for StDev 95% Confidence Intervals Summary for Temperature [K]-80sccm
21. 21. Temperature distribution on middle-cross section Linear increase in temperature range in Laminar phase. Temperature range change is (0.025 K) and can be considered constant for CVD By getting to turbulent phase, Temperature range becomes narrower in 200 sccm
22. 22. Contours of Velocity Components (u[x],v[y],w[z]) 20 sccm 300 sccm200 sccm100 sccm =0.0089/ TurbulentLaminar =0.0356/ velocity>00 velocity =0.0247/ 0.025m scale Highest velocity value between 3 components but not near the Templates
23. 23. U-velocity distribution on middle-cross section Linear increase in range and mean velocity with increasing flow rate. Does not depend on change from Laminar to turbulent. Compared to v and w component, u velocity has the highest change by increasing flow rate ( = 0.001156 /)
24. 24. V-velocity distribution on middle-cross section Change in mean value is in scale of 0.000001 m/s in Laminar phase. Outliers in box plot and std of .004854 are the outcome of cross flows due to natural convection & buoyancy effects (sudden changes in density) =.000615 m/s between laminar and turbulent This drop is due to having more disturbed flow
25. 25. W-velocity distribution on middle-cross section Very small decrease in mean velocity by changing from Laminar phase to turbulent. = .000004 m/s Outliers in the boxplot, demonstrate the disturbance which exist here duo to buoyancy driven flow. By contour plots and statistical data, flow conditions around sample can be extracted
26. 26. Conclusions from preliminary work Buoyancy and flow regime v and w vel. Threshold of change to turbulent 200 sccm and the deposition change observed in experiments can be caused by this. Symmetric and nearly constant temperature. Tube furnace model with boat and template as final model. Using Peclet number, effect of small changes in velocity components on diffusion and reaction can be tested. A statistical method for relating the data in different cross sections, flow conditions and furnace temperatures is required. 26 Diffusion Characteristic length Characteristic velocity
27. 27. Research Questions and Plan 27 How can temperature profile and flow characteristics near the templates be identified? Is it possible to simulate the deposition due to CVD process for a single (>50nm) nano-pore in a template and to predict the deposition rate of carbon for different flow rates and temperatures? Study reaction kinetics and reaction-diffusion systems Study how to simulate species transport, reaction and porous media Develop the user defined functions (UDF) for reactions Create model for carbon deposition in one pore Compare carbon deposition rate with experiment to modify the model Is it possible to create a more flexible and comprehensive model that can predict deposition in different flow, temperature and furnace conditions? Check requirements to achieve a general model applicable to different furnaces Modify the numerical model to match requirements
28. 28. Introducing diffusion-reaction system to FLUENT 28 Generalized Source term (constant and linear) Generalized diffusion coefficient Generalized transport variable By taking divergence of these two, they can be transformed into volume integrals. + . = . + Rate of increase of mole of the species Net rate of additions of mole of the species per unit volume by convection Molar-averaged velocity Net rate of mole of the species per unit volume by diffusion in a binary system of components, otherwise xa is replaced by The molar rate of production of species by chemical reaction. = = =1 If the number of chemical reactions taking place in the system is , the mass production rate is: stoichiometric coefficient Difference between the forward and backward reactions
29. 29. Diffusion AAO Membrane Deposited carbon Outlet boundary condition from macro-scale model and gas spectrometry Reaction zone Diffusion of remaining gas and by- products of reactions Control Volume for model Schematics of the Micro-Scale Model 29 Inlet boundary condition from Macro-scale model Dehydrogenization or Coking? By trying both and comparing the results with experimental data for carbon deposition We anticipate the results to be presented 1. Concentration [vol ppm] plot for each substance versus position along axis of Tube 2. Carbon deposition rate[mgc m-2h-1] plot for different flow rates versus position[X] After conducting gas chromatography, the substances that exist here will be revealed
30. 30. Timetable for Work Plan 30
31. 31. Required Facilities 31 High-end computer with these specs is required for decreasing the simulation time: Processor: Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz (2 processors) Enabled Processor Count: 12 Total Memory: 16 GB Local Storage: 931.51 GB (1 drives) Graphics Card & Driver: FirePro W7000 3 stage ZTF CARBOLITE furnace. - Already Exist. AAO membranes (Whatman Anodisc 13, nominal pore diameter: 200 nm, nominal thickness: 60 m), Ethylene-helium gas mixture and Argon, in already set CVD setup. - Already Exist. Gas chromatography - Cooperating with chemical engineering department of RIT High performance research computing Cooperating with research computing Section of RIT
32. 32. Acknowledgments I would like to thank my advisor, Professor Michael G. Schrlau and PHD candidate Masoud Golshadi,, Dr. Taboada-Serrano, Dr. Robert Parody Dr. Steven Day and Dr. Agamemnon Crassidis for their valued advice and support of this work at Rochester Institute of Technology. Furthermore, I would like to thank Ms. Brenda Mastrangelo and Mr. Thomas Allston for their helps on conducting gas chromatography and Mr. William Finch for his kind cooperation in the process of buying the required hardware. Ayomipo Ayowosola, Ryan Dunn Karen De Souza Martins and all the other lab members who helped and supported my work. 32
33. 33. Thanks for listening to my presentation, Questions? 33