Filtered CHCHE Courses (2025-26)

Instruction in synthesis, separation, purification, and physical and spectroscopic characterization procedures of model organic compounds. Specifically looking into dye synthesis, methods of isomer identification, and an independent project relating to engineering and organic chemistry. Enrollment priority given to chemical engineering majors.

This course will introduce the Chemical Engineering discipline, career options and research opportunities through lectures and panel discussions by faculty and alumni. Graded pass/fail.

Introduction to computational methods and tools with emphasis on their application to chemical engineering. Basic knowledge of Python is assumed. The course will focus on writing Python code to solve and present results of scientific and engineering computations and data analysis, and the use of methods in Python libraries, including NumPy, SciPy, and MatPlotLib . Students are expected to attend a lecture/discussion session and two computational laboratories weekly.

Fundamentals of chemical engineering material balances and separation process. First half: analysis of single and multi-unit processes with reactive and non-reactive components. Second half: liquid-liquid extraction, flash and column distillation, membrane separation, and additional separation process of student choice.

A comprehensive treatment of classical thermodynamics with engineering and chemical applications. First and second laws. Applications to closed and open systems. Equations of state. Thermochemical calculations. Properties of real fluids. Power generation and refrigeration cycles. Multicomponent systems, excess properties, fugacities, activity coefficients, and models of nonideal solutions. Chemical potential. Phase and chemical reaction equilibria.

Special problems or courses arranged to meet the emerging needs of undergraduate students. Topics have included AIChE's annual Chem-E-Car Competition. May be repeated for credit, as content may vary. Grading scheme at instructor's discretion.

Research in chemical engineering offered as an elective in any term. Graded pass/fail.

A research project carried out under the mentorship of an approved faculty member. Before the beginning of the first term of the thesis, students must submit a proposal - with project details and significant design component clearly defined - for review and approval by the thesis mentor and chemical engineering Undergrad Option Rep. In addition, students must submit the following to the thesis mentor and chemical engineering senior thesis coordinator: a midterm progress report in each term; end-of-term progress reports at the end of the first two terms; and a thesis draft in the third term. A grade will not be assigned prior to completion of the thesis, which normally takes three terms. A P grade will be given for the first two terms and then changed to the appropriate letter grade at the end of the course.

Training in the writing of scientific research papers for chemists and chemical engineers. Fulfills the Institute scientific writing requirement.

Elements of chemical kinetics and chemically reacting systems. Chemical reactor analysis. Homogeneous and heterogeneous catalysis. Biological and environmental reaction engineering. Enzyme kinetics. Lectures include case studies from real world applications of various chemical engineering subfields. Problem sets contain computational approaches to chemical kinetics and reactor design.

A rigorous development of the basic differential equations of conservation of momentum, energy, and mass in fluid systems. Solution of problems involving fluid flow, heat transfer, and mass transfer.

An introduction to analysis and design of feedback control systems in the time and frequency domain, with an emphasis on state space methods, robustness, and design tradeoffs. Linear input/output systems, including input/output response via convolution, reachability, and observability. State feedback methods, including eigenvalue placement, linear quadratic regulators, and model predictive control. Output feedback including estimators and two-degree of freedom design. Input/output modeling via transfer functions and frequency domain analysis of performance and robustness, including the use of Bode and Nyquist plots. Robustness, tradeoffs and fundamental limits, including the effects of external disturbances and unmodeled dynamics, sensitivity functions, and the Bode integral formula.

Examines the Earth's resources including fresh water, nitrogen, carbon and other biogeochemical cycles that impose planetary constraints on engineering; systems approaches to sustainable development goals; fossil fuel formation, chemical composition, production and use; engineering challenges and opportunities in decarbonizing energy, transportation and industry; global flows of critical elements used in zero-carbon energy systems; food-water-energy nexus and effects of human on air, water and soil.

The goal of the course is to teach students technical creativity (ideas that solve problems) and how to use artificial intelligence tools developed in the course to enhance their creativity further. Because technical creativity needs a specific context, we selected the context of microfluidic technologies and global health challenges. This course combines three parts. First, students will dive deeply into human and AI augmented technical creativity. Second, students will dive into the physics, kinetics, and transport fundamentals that underpin microfluidic technologies and explore examples of translation of technologies into practice. Finally, students will collaborate in teams to apply their creativity to global health challenges. AI tools will be introduced to aid students in generating and evaluating ideas. Students will be encouraged and helped, but not required, to develop their inventions further by working with OTTCP and entrepreneurial resources on campus. The course benefits from the enrollment of students with diverse backgrounds and interests. Students are encouraged to contact the instructor to discuss enrollment.

The milk we drink in the morning (a colloidal dispersion), the gel we put into our hair (a polymer network), and the plaque that we try to scrub off our teeth (a biofilm)are all familiar examples of soft materials. Such materials also hold great promise in helping to solve engineering challenges like drug delivery, water remediation, and sustainable agriculture, as well as the development of new coatings, displays, formulations, food, and biomaterials. This class will cover fundamental aspects of the science of soft materials, presented within the context of these challenges. We will also have guest speakers describe new applications of soft materials.

This course covers the principles and applications of solid-state NMR spectroscopy, with a focus on structural and dynamic characterization of organic and inorganic solids. Key applications include the analysis of heterogeneous catalysts, battery materials, and other energy storage systems. Topics include fundamental and advanced solid-state NMR techniques such as magic angle spinning (MAS), cross-polarization (CP), NMR of quadrupolar nuclei, multiple-pulse sequences, multi-dimensional experiments, sensitivity enhancement methods, and NMR methods for probing molecular dynamics. Recent advances in the field such as the integration of machine learning and artificial intelligence (AI) for automated spectral analysis, structural prediction, and data-driven materials discovery will also be briefly reviewed. Hands-on laboratory sessions using solid-state NMR spectrometers at the Caltech Solid State NMR Facility provide practical experience.

A broad introduction into the gas-phase techniques used to process semiconductor surfaces, from etching to deposition and surface modification. Topics include: Kinetic theory of gases. Surface chemistry and gas-surface interaction dynamics. Physical and chemical vapor deposition of amorphous, polycrystalline and epitaxial layers. Introduction into processing plasmas and their unique ability to drive non-thermal chemistry on surfaces for precisely etch patterns and deposit layers in confined spaces. Role of ions and determination of key parameters that control the ion energy and flux to surfaces.

Student groups complete high-level design of a chemical process or product, while learning about and using the engineering design cycle, methods of creativity, technology evaluation, and entrepreneurship. Each team generates, filters, and refines project concepts; identifies stakeholders, needs, and requirements; ideates and evaluates technologies, selecting suitable options based on readiness, benefits, and strategy; develops a project budget and schedule for a proof of concept; and writes a proposal. Each project must meet requirements for societal impact, budget, duration, person hours, environmental impact, safety, and ethics.

After selecting a project from a collection of proposals, student groups design, build, and test a proof of concept for the proposed chemical process or product, subject to schedule, financial, and other constraints. Students are encouraged and helped, but not required, to collaborate with on-campus subject-matter experts, laboratories/centers, and OTT/entrepreneurial resources.

In this capstone course, students design, build, and test a proof of concept for a chemical process or product, subject to constraints related to finances, schedule, environmental impact, safety, and ethics. Students learn about and use methods of creativity, the engineering design cycle, and rapid prototyping.

Design, construction, and characterization of engineered biological systems. Students propose and execute research projects in biomolecular engineering, synthetic biology, and genetic engineering fields. Projects will cover a broad range of molecular and cell biology, and genetics and genomics lab techniques.

Through lectures, in-class activities, and problem sets, students learn and use methods in data science to execute a project focused on a Quantitative Structure Property Relationship (QSPR). Students complete a typical research-based data science pipeline, including project definition, metric evaluation, data collection, data cleaning, exploratory data analysis, model selection, visualization, and reporting. During data cleaning and exploratory data analysis, students learn key concepts about univariate and multivariate statistics. Throughout the project, students learn about bias and fairness, the reproducibility crisis, statistical paradoxes, and more. Python is the programming language of instruction.

Student groups complete a one-term, data-science project that addresses an instructor-approved chemical engineering challenge. The project may be an original research idea; related to work by a research group at the Institute; an entry in a relevant national/regional contest; a response to an industry relationship; or other meaningful opportunity. There is no lecture, but students participate in weekly progress updates. A student may not select a project too similar to research completed to fulfill requirements for ChE 80 or ChE 90 abc.

The properties and photoelectrochemistry of semiconductors and semiconductor/liquid junction solar cells will be discussed. Topics include optical and electronic properties of semiconductors; electronic properties of semiconductor junctions with metals, liquids, and other semiconductors, in the dark and under illumination, with emphasis on semiconductor/liquid junctions in aqueous and nonaqueous media. Problems currently facing semiconductor/liquid junctions and practical applications of these systems will be highlighted. Part a not offered 2025-26.

The foundations of heat, mass, and momentum transfer for single and multiphase fluids will be developed. Governing differential equations; laminar flow of incompressible fluids at low and high Reynolds numbers; forced and free convective heat and mass transfer, diffusion, and dispersion. Emphasis will be placed on physical understanding, scaling, and formulation and solution of boundary-value problems. Applied mathematical techniques will be developed and used throughout the course.

Course covers advanced topics in kinetics and reaction engineering, in the context of electrically-driven processes. The course is organized into several modules, which cover 1) the basics of electrocatalytic systems, 2) the thermodynamic basis of electrifying chemical reactions, 3) the kinetic underpinnings of electron transfer reactions, 4) the coupling of transport and kinetics in electrocatalytic systems, and 5) special topics.

Fundamentals of aerosol dynamics and their description using population balance equations: particle size and property distributions in aerosol systems. Physical and chemical mechanisms that govern the aerosol dynamics from the continuum through free-molecular regimes: condensation and evaporation; nucleation; particle aerodynamics and diffusion. Thermodynamic, physical, and optical properties of aerosol particles.

The course introduces rational design and evolutionary methods for engineering functional protein and nucleic acid systems. Rational design topics include molecular modeling, positive and negative design paradigms, simulation and optimization of equilibrium and kinetic properties, design of catalysts, sensors, motors, and circuits. Evolutionary design topics include evolutionary mechanisms and tradeoffs, fitness landscapes and directed evolution of proteins. Some assignments require programming (Python is the language of instruction).

An introduction to the fundamentals and simple applications of statistical thermodynamics. Foundation of statistical mechanics; partition functions for various ensembles and their connection to thermodynamics; fluctuations; noninteracting quantum and classical gases; heat capacity of solids; adsorption; phase transitions and order parameters; linear response theory; structure of classical fluids; computer simulation methods.

An advanced course emphasizing the conceptual structure of modern thermodynamics and its applications. Review of the laws of thermodynamics; thermodynamic potentials and Legendre transform; equilibrium and stability conditions; metastability and phase separation kinetics; thermodynamics of single-component fluid and binary mixtures; models for solutions; phase and chemical equilibria; surface and interface thermodynamics; electrolytes and polymeric liquids.

Climate change has already begun to impact life on the planet, and will continue in the coming decades. This class will explore particular causes and impacts of climate change, technologies to mitigate or adapt to those impacts, and the economic and social costs associated with them - particular focus will be paid to distributional issues, environmental and racial justice and equity intersections. The course will consist of 3-4 topical modules, each focused on a specific impact or sector (e.g. the electricity or transportation sector, climate impacts of food and agriculture, increasing fires and floods). Each module will contain lectures/content on the associated climate science background, engineering/technological developments to combat the issue, and an exploration of the economics and the inequities that exacerbate the situation, followed by group discussion and synthesis of the different perspectives.

Microbes, the most diverse and abundant organisms on Earth, are critical to the daily functioning of humans as well as the life-sustaining biogeochemical cycles. This course provides an in-depth exploration of the fascinating world of environmental microbiology and genomics, with a special emphasis on computational approaches for systems-level analysis of microbial communities and their interactions. The course will delve into the diverse roles of microorganisms in environmental processes ranging from nutrient and biogeochemical cycling to predicting the impacts of climate change. It will introduce students to a wide range of computational tools and techniques used in the analysis of microbial genomic data. Topics covered include: microbial community structure and functioning; interactions among microbes and their environment; and the influence of the environment in shaping and driving microbial evolution. Through a combination of lectures, discussions, and hands-on computational exercises, students will gain skills in analyzing, interpreting and visualizing large scale community metagenomic (DNA) and metatranscriptomic (RNA) data from environmental ecosystems. Students will also explore how these computational approaches can be applied to address real-world environmental challenges, broaden understanding of the genetic and metabolic diversity of the microorganisms to better manage ecosystem function, the value of this biodiversity for adaptation to natural and anthropogenic perturbations.

This course will cover the basic principles of biological and medical imaging technologies including magnetic resonance, ultrasound, nuclear imaging, fluorescence, bioluminescence and photoacoustics, and the design of chemical and biological probes to obtain molecular information about living systems using these modalities. Topics will include nuclear spin behavior, sound wave propagation, radioactive decay, photon absorption and scattering, spatial encoding, image reconstruction, statistical analysis, and molecular contrast mechanisms. The design of molecular imaging agents for biomarker detection, cell tracking, and dynamic imaging of cellular signals will be analyzed in terms of detection limits, kinetics, and biological effects. Participants in the course will develop proposals for new molecular imaging agents for applications such as functional brain imaging, cancer diagnosis, and cell therapy.

Special courses of readings or laboratory instruction. The student should consult a member of the faculty and prepare a definite program of reading, computation, theory and/or experiment. The student must submit a summary of progress at midterm and, at the end of the quarter, a final assignment designed in consultation with the instructor. This course may be credited only once. Grading: either grades or pass/fail, as arranged with the instructor.