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Course Descriptions

CHEM 521: Analytical Electrochemistry

Credits: 3

This course provides students with an introduction to the theory and practice of modern electrochemistry, with emphasis on instrumentation and applications in chemical analysis. The main elements of this course include fundamental electrochemistry theories, basic electrochemical methods and current topics in electroanalytical chemistry focusing on state-of-the-art research in the field. This course will help students establish a solid foundation in electrochemistry and electrochemical analysis.

Topics covered include.

  • Thermodynamics, structure of the electrode/solution interface and electrical double layer, electrode kinetics and mass transport in an electrochemical cell
  • Two popular electrochemical methods: potential step and cyclic voltammetry.
  • Current electrochemistry topics such as electrochemiluminescence (ECL), ultramicroelectrode (UME), scanning electrochemical microscopy (SECM), nanopore-based methods, bipolar electrochemistry, nanoparticle electrochemistry, single-molecule detection and chemically modified electrode

CHEM 545: Data Science Methods for Clean Energy Research

Credits: 3

This course is a survey of modern data science methods taught in the context of materials for clean energy (e.g., batteries and solar energy). It covers data visualization, statistics, machine learning and data management. The programming language used will be Python.

 By the end of the course, you’ll be able to:

  • Develop software in a way that it can be used by others, including writing documentation, installing packages, automating setup and running computational studies
  • Create technical specifications for what a program should do (its use cases) and how this is accomplished (software design). Create, update and share a project using version control (specifically GitHub) for collaborative software development
  • Program using the Python scientific stack, including Numpy, Pandas and Matplotlib
  • Develop unit tests that validate important aspects of the project implementation, and, more broadly, use test-driven development to build software. Search, evaluate and integrate an externally developed Python package into a project as well as create your own Python packages.

CHEM 522 Atomic & Molecular Analytical Spectroscopy

Credits: 3

This course provides students with practical understanding of the principle and implementation of various spectroscopy techniques, with a focus on laser spectroscopy and quantitative analysis. Types of analytical spectroscopy techniques covered include atomic, UV-vis, fluorescence, FRET/FLIM/FCS, IR, raman, Fourier-transform spectroscopy, nonlinear optical spectroscopy, ultrafast spectroscopy and more.

Areas of focus include:

  • The fundamental principles of spectroscopy, basics of electromagnetic wave, optics, and lasers, principles and applications of quantitative electronic and vibrational spectroscopy techniques, Fourier-transform analysis, correlation analysis and principles of spectroscopic imaging
  • Current research topics such as fluorescence anisotropy, fluorescence correlation spectroscopy and pump probe spectroscopy

CHEM 524: Analytical Mass Spectrometry

Credits: 3

This course provides students with an introduction into the theory and practice of mass spectrometry of organic compounds and biomolecules, including spectra interpretation. It aims to teach students theoretical foundations of modern mass spectrometry and develop skills in spectra interpretation.

Topics covered include:

  • Theory and figures of merit of ionization methods (electron impact, chemical ionization, photoionization, electrospray, matrix-assisted laser desorption)
  • Ionization and ion thermodynamics (proton affinities, gas-phase basicities and acidities).
  • Theory and performance of mass analyzers (time-of-flight, quadrupole filter, quadrupole ion traps, Orbitrap, ion cyclotron resonance).
  • Hyphenated methods (gas chromatography-mass spectrometry, tandem mass spectrometry)
  • Methods for ion activation and dissociation (collision-induced dissociation, photodissociation, electron-based methods)
  • Special topics (resonant multiphoton ionization, quantitative tandem-MS assays, ion imaging)
  • Spectra interpretation: The rules and hands-on interpretation of electron-ionization mass spectra of unknown organic compounds and de-novo peptide sequencing

CHEM 525: Meso & Microfluidics in Chemical Analysis

Credits: 3

This course is recommended for students with a strong interest in learning about the latest technologies in fluidics. It covers the fundamentals of meso and microfluidics, with a focus on topics such as laminar flow, surface tension, viscosity, diffusion, partitioning and wetting. We’ll discuss droplet-based microfluidics, high-throughput assays, cell-based assays and “organ on a chip” models, among other techniques. You’ll explore analytical methods using microfluidics for separation and detection-based assays, and work on reasoning through microfluidic problems, taking into account device design, calculations, and potential pitfalls and alternative approaches.  

CHEM 528: Bioinstrumental Analysis

Credits: 3

This course introduces students to modern instrumental methods of chemical analysis using examples from the analysis of biological molecules in the context of biomedical research and medical diagnostics. Topics include the principles of operation of the major classes of chemical instrumentation, figures-of-merit for evaluating chemical measurements, and how to use data from chemical measurements to inform decisions in research and medicine. Weekly laboratory projects will train students to operate modern instruments that make use of molecular recognition, separations, spectroscopy, mass spectrometry and other principles, as well as the associated sample preparation and data analysis.

By the end of the course, you’ll be able to:

  • Describe the principles of operation of the major classes of modern chemical instrumentation.
  • Use modern chemical instrumentation in practical settings to analyze real samples

Evaluate the performance of chemical measurement in terms of figures of merit including limit of detection, linear dynamic range, and resolution

Compare and contrast different measurement approaches for specific analytical situations; and select a measurement approach to guide a decision in the context of biomedical research and medical diagnostics

CHEM 529: Chemical Separation Techniques

Credits: 3

This course covers the fundamental principles, major advances and recent hot topics of chemical separation techniques and separation science. We’ll introduce the fundamental principles of chromatographic and electrophoretic separation theory and processes, and explore how these processes relate to the field of analytical chemistry. Although modern chemical separation techniques are routinely practiced, there continues to be fundamental advances, which are continually integrated into the course.

Topics covered include:

  • Fundamental principles of separation science to understand analyte peak broadening, with integration of mass transfer and partitioning dynamics, flow dynamics (hydrodynamics), material science and chemical interactions within distinct phases and at phase boundaries. These fundamental principles form a foundation for discussing practical issues such as analysis time, resolution of chemicals in a complex separation and novel instrumentation design.
  • The techniques of liquid chromatography, gas chromatography and supercritical fluid chromatography focus upon the partitioning and separation of neutral analytes. We’ll discuss stationary phase design and separation mechanisms. We’ll also introduce the concept of gradient elution and temperature programming and the  principles of flow-through detection, along with the related instrumental issues and constraints.
  • Separations of ionic analytes, and separations based upon the physical size of the analytes. The techniques of ion chromatography, capillary electrophoresis, SDS-PAGE,and recent developments in the micro-fabrication of separation systems such as "capillary electrophoresis on a chip" that produce high-speed protein separations.

CHEM 546 Software Engineering for Molecular Data Scientists

Credits: 3

The course introduces basic principles of scientific software development in Python in the context of molecular data science. It covers command line tools, Python from the perspective of molecular data science methods, software development and collaboration principles (e.g. version control).

Areas of focus include:

  • Statistical reasoning and methods, including distributions, hypothesis testing and error analysis for multiple data types
  • Data visualization methods
  • A wide range of machine learning methods with direct applications for problems in the design, synthesis and characterization of materials for clean energy
  • Hands-on experience with the Python library scikit learn to apply ML methods on real-world data sets related to design of materials for energy storage and conversion
  • Data management strategies

CHEM 567: Computers in Data Acquisition & Analysis

Credits: 3

This course provides students with the tools they need to use computers to control their experiments and to acquire and analyze data. Students learn to use LabVIEW programming software in order to successfully carry out computer-controlled experiments in the laboratory. You’ll be able to integrate individual skills and techniques into a complete system for experimental control, data acquisition and analysis.

By the end of the course, you’ll be able to:

  • Use the transfer function model to understand the basis of data acquisition. This model is closely based on Fourier transform methods.Write code in LabVIEW, using the task model to set up the steps in the data acquisition, and come away with an understanding of how to properly, and improperly, synchronize data acquisition
  • Write code in LabVIEW to acquire data and analyze the data, and compare their results with theoretical models for the data, and extract model parameters
  • As a final project, design in LabVIEW a data acquisition and analysis system on your own. You’ll be measured by the quality of your code, and the answers to the following questions. Does it work? Does it work correctly? Does it properly compare results and theory, or model?