Summary of Teaching Activities
Description of Courses Taught
CH154 Chemical Engineering Laboratory (Fall 2010, 2011, 2012)
This required senior undergraduate course provides students with practical training in the application of chemical engineering principles using real operating equipment. Laboratory exercises are directed at the acquisition and analysis of experimental data to be used for the design of chemical engineering processes. These exercises require a combination of judiciously chosen experiments with process modeling, in a manner that is typical of modern industrial practice. Some experiments provide experimental confirmation of process relationships that are derived theoretically in chemical engineering coursework and textbooks. All process operations covered involve combinations of transport phenomena acting together rather than the individual transfer of momentum, heat, and mass transfer that students have encountered previously. Students are thus required to synthesize information learned in different courses and apply it to problems that arise in the laboratory. Students also practice and become proficient in the professional reporting of results, because oral presentations and written reports are significant elements of the course.
Laboratories covered by Balsara in Fall 2010:
1. Ethanol distillation
2. Carbon dioxide adsorption and reaction
3. Centrifugal pump
4. Membrane separation
5. Heat transfer
CH162 Process Control (Spring 2010, 2011, 2012)
This is a required undergraduate chemical engineering graduate course taken in the senior year. It is the first time that chemical engineering students are exposed to unsteady state processes. Systems that students have studied at steady-state, are perturbed and the effect of the perturbations are studied within the scope of linear response theory. A variety of process control strategies are implemented to ensure that the system returns to the desired steady-state in spite of the perturbations. The students learn about process control concepts in class lectures (3 per week) and implement some of the control strategies in a laboratory which is held once a week.
Problems are solved in Laplace transform space. In most problems of practical importance, the steps of converting the problem from the time domain to Laplace space and taking the Laplace solution and converting it back into the time domain consume most of the time, if done using paper and pencil. I was fortunate to learn about a new textbook for this subject written by Wayne Bequette, where all of the problems are solved using a computer package (Matlab). This was very convenient because our students learn Matlab in E77 (a required course) and have long complained that they do not use this knowledge in subsequent courses. My lectures were entirely computer-based. The students (and I) did solve a few simple problems using paper and pencil, so that they had a clear concept of algorithms that the programs were executing. However, most of the assignments required computer solutions. This required reconfiguring the undergraduate computer lab. In most lectures, about half the time was spent demonstrating how the problems could be set-up in Matlab and discussing the solutions. It would have been very nice if the Berkeley campus had classrooms where each of the 50 students had access to a computer, but such a class does not exist (at least I was unable to access such a class). As an experiment, one of the lectures was help in the undergraduate computer lab. I gave a handout that described the problem of interest and the class was told to work out the solution. The GSI and I circulated from one student group to another (each group was composed of 2 or 3 students due to computer limitations) during the class. The feedback I got was that this was the best class of the semester. The assignments and examples covered in class were much more complex than those covered in a traditional process control class. The class concluded with the design and computer implementation of automatic control of insulin release in a diabetic patient. I got the idea from a chemical engineering departmental colloquium given at Berkeley by Professor Adam Heller from the University of Texas. He described the progress that his company had made in automatic sensing of glucose levels and release of insulin based on the reading of the sensor. The students first developed a model for the effect of food intake and insulin release on blood glucose levels in humans. They then used their knowledge of CH162 to design a cascade control scheme for ensuring that the blood glucose level was within acceptable bounds. They thus learned that control strategies that work for controlling petrochemical plants can be used to control the production of chemicals in humans.
Unsteady state models for heat, mass, and momentum transfer, constitutive equations, linear models and deviation variables, state-space models, Laplace transforms, transfer functions, first-order systems, integrating systems, second-order systems, numerator dynammics, lead-lag behavior, poles and zeros of transfer functions, processes with dead time, converting state-space models to transfer functions, control block diagrams, feed-back control, proportional control, differential and integral control, stability of control strategy, responses to changes in set point and load disturbances, control of unstable systems, tuning controllers, frequency-response analysis, Bode and Nyquist plots, feed-forward control, cascade control, internal model control.
CH295N, Polymer Physics (Spring 2010)
This is a graduate elective that I created in 2001. It is based on a book titled "Polymer Physics" written by Masao Doi, a leading theorist in the field. This is a rigorous advanced course that covers the statistical description of polymeric systems. The course is aimed at a broad audience and students from a variety of departments such as Bioengineeirng, Chemistry, and Materials Science and Engineering take the course.
The textbook develops a unified framework for describing all of the systems and phenomena given in the syllabus. My main original contribution was to embellish the framework with recent experimental results. For example, the theoretical treatment of isolated polymer coils is an old subject that date back to the 1940s. However, only recently have scientists been able to visualize individual polymer coils due to the development of molecular traps and tweezers. These modern experimental results are in remarkable agreement with the established theories. Similarly, the theory of rod-like polymer chains was developed in the 1950s, but only recently have their properties been explored. This is due to the availability of rod-like viruses with lengths that can be tuned by genetic engineering. It turns our rod-like molecules with uniform and controlled lengths cannot yet be made synthetically. In this case, the experimental data are not at all in agreement with theory. It is actually not clear if this is due to a breakdown of the theory or due to the lack of proper characterization of the stiffness of the rod-like viruses. I believe it is essential to present students, especially graduate students, with solutions as well as dilemmas.
Properties of an isolated polymer molecule, polymer solutions and melts, polymer blends and block copolymers, the crystalline state of polymers, polymer gels, molecular motion in dilute solutions, molecular motion in entangled systems, linear and non-linear mechanical properties, properties of liquid crystalline polymers, related systems: colloidal suspensions and surfactant solutions.
CH 240 Advanced Chemical Engineering Thermodynamics (Fall 2006)
This is a required chemical engineering graduate course taken by first year graduate students. The course emphasizes the statistical foundation of thermodynamic concepts that are relevant in modern chemical engineering. The course was based on "Introduction to Modern Statistical Mechanics" by David Chandler. I chose this textbook because it demonstrates the broad scope of thermodynamics and statistical mechanics. However, the textbook is written from the point of view of a physical chemist rather than a chemical engineer. My objective was to weave traditional chemical engineering thermodynamics and modern statistical mechanics into a seamless and coherent fabric. Here is an example. One of the lectures began with an experimental demonstration-the fact that rubber bands get stiffer when you heat them up. The experiment is simple, and consists of a rubber band connected to a load cell. A thermocouple measures temperature and a heat gun heats the rubber band. This is counter-intuitive because most materials become soft when you heat them. We then classical thermodynamics to obtain the temperature dependence of entropy, based entirely on the sign of the temperature coefficient of the tensile modulus. This exercise demonstrates both the power and limitation of classical thermodynamics. The power is that quantities such as entropy that are difficult to measure directly can be obtained by making much simpler measurements. The limitation is that classical thermodynamics only provides inter-relations between state variables. Models based on statistical mechanics are needed to predict the behavior of materials from first principles. In the statistical mechanics portion of the course we return to this problem and build statistical models of polymer chains. We develop a variety of statistical mechanical models that describe gasses, pure liquids, liquid mixtures, solids, and magnets. The students quickly realize that computer simulations are needed to solve most problems correctly. They write and execute a computer code for solving the Ising magnet using Monte Carlo simulations. Most of the current theoretical research the chemical engineering thermodynamics is focused on computer simulations.
Fundamental thermodynamics, first law, work, heat, energy, the second law, entropy, variational statement of the second law, thermal equilibrium and temperature, approach to equilibrium, auxiliary functions, Legendre transforms, enthalpy, Helmoltz energy, Gibbs energy, Grand Potential, Maxwell and Gibbs-Duhem relations, intensive and extensive variables, multiphase equilibrium and stability, phase equilibria, interfaces between equilibrium phases, fugacity of gasses, equations of state, intermolecular potentials, molecular dynamics, fugacity of mixtures, gas-liquid and liquid-liquid equilibrium, solubility of gasses and solids in liquids, osmotic pressure, charged species, solutions of polymers, colloids, proteins, introduction to stat. mech. and ensembles, microcanonical ensemble, ideal gases, entropy, canonical ensemble, fluctuations in energy, probabilistic interpretation of heat and work, 2-state model, Generalized Ensembles, partition function for non-interacting systems, ideal mixtures, Flory-Huggins theory, ideal crystalline solids, correlations in ideal liquids, phase transitions, the Ising magnet, Monte-Carlo simulations, renormalization group theory, Brownian motion, and Langevin and Fokker-Planck equations.
NSE201, Introduction to Nanoscience and Nanoengineering (Spring 2006)
The Berkeley Nanosciences and Nanoengineering Institute (BNNI) is a multidisciplinary umbrella organization for expanding and coordinating research and education in the emerging field of nanoscale science and engineering. BNNI graduate students obtain PhDs from a 10 campus departments (Physics, Chemistry, Chemical Engineering, Materials Science and Engineering, Mechanical Engineering, Civil Engineering, Bioengineering, Earth an Planetary Sciences, Molecular and Cell Biology, and Electrical Engineering and Computer Science) with a designated emphasis in nanoscale science and engineering. In 2005, the BNNI executive committee decided to institute a course titled "Introduction to Nanoscience and Nanoengineering" that would serve as the only requirement that all of the students in the program. Professors Peter Yu, Ronald Gronsky, and I were asked to lead the effort to develop this course. The course was organized into 4 modules titled Nanoscale Physics, Hard Nanomaterials, Soft Nanomaterials, and Nanoengineering. I taught the Soft Nanomaterials module and all three instructors taught the Nanengineering module. The Soft Nanomaterials began with an introduction to thermodynamics and statistical mechanics. My main objective in these lectures was to emphasize the importance of entropy and disorder, which dominates the behavior of soft structures on all length scales. We then covered the synthesis and basic physics of soft materials including polymers, colloidal systems, liquid crystals, and membranes. We then applied the principles of thermodynamics and statistical mechanics to model nanoscale patterning and organization using soft materials. This naturally led into a discussion of nanoengineering applications in the field of electronics, photonics, and energy storage. This last discussion was part of the Nanoengineering module. The main challenge in teaching this course was that it had to be tailored for students from three very different colleges. (No graduate student instructors were assigned to this course.)
CH 178, Polymer Science and Technology (Spring 2004)
This is an introductory course on polymer materials and soft condensed matter for both graduate and senior undergraduate students. It is taught using traditional lectures and 5 hands-on laboratory sessions (each is 3 hours long). I have worked toward improving both lecture and lab components of the course.
Polymers are now used in almost all disciplines from electrical engineering to chemistry, from medicine to materials science. I thought that it would thus be efficient if my course were designed to cover topics of interest to students within and outside chemical engineering. The most important constraint was to ensure the course was rigorous and cohesive. The course thus covered a range of modern topics such as polyelectrolyte thermodynamics (relevant to biology because most natural polymers fall in this category), microphase separation (relevant to nanotechnology), and electrically conducting and semiconducting polymers (future materials applications of polymers).
Under my guidance, the laboratory sessions are now conducted in a chemistry laboratory (302 Latimer) where individual student groups can work safely in a chemical hood. I also raised funds to purchase additional equipment for the lab so that all the groups (typically we teach 5 or 6 groups of 3 students in one session) do the same experiment on each day. When I took over teaching the course, each group did a different experiment each week, due to lack of hood space and equipment. In my view, having all of the students conducting the same experiment is extremely important because I was then able to coordinate the topics covered in the labs and lectures. I experimented with the idea of letting the students do the experiments before covering the material in the lecture. This way, the students observed phenomena before they understood the theory behind the observation. I found this to be a more exciting method of teaching, rather than the more traditional method of teaching the students concepts so that they are prepared to make measurements in the laboratory. I have instituted several new experiments. In particular I have introduced synergy between the different lab sessions. The students synthesize two kinds of polymers: polystyrene, which is amorphous, and nylon, which is crystalline. In the final lab they study the polymers they synthesized using X-ray crystallography, differential scanning calorimetry, gel permeation chromatography, and infrared spectroscopy. I designed the characterization lab, and most of the equipment is in my research laboratory. The students thus have the opportunity work with state-of-the-art equipment.
Thus far, the students taking the course have come from five different disciplines: chemical engineers, bioengineers, materials science and engineering, electrical engineering, and mathematics. We have thus begun the process of establishing a common interdisciplinary course for teaching polymer science. We hope to include more disciplines in the future, especially chemistry. The students have generally found this course to be tough but useful.
Polymer Synthesis: linear step growth, non-linear step growth, free radical polymerization, copolymerization, emulsion polymerization, ionic polymerization, ring-opening, metathesis, atom transfer, and genetic engineering. Physical Chemistry of Polymers: freely jointed chains, Flory-Huggins theory, phase behavior of polymer solutions, osmotic pressure, solvent quality, chain swelling, viscosity, crosslinked rubbers, swelling of rubbers, polymer characterization (gel permeation chromatography, gel electrophoresis, viscometry, osmometry), polymer blends and block copolymers, thermodynamics of charged (polyelectrolyte) and uncharged networks. Physical Properties: viscoelasticity, deformation of elastomers, plateau modulus and entanglement, response to sinusoidal oscillations, yielding, crazing, fracture, electric, dielectric, and electronic properties (including conducting polymers, semi-conducting polymers, and light-emitting polymers).