2016-2017 2017-2018 2018-2019 2019-2020 2020-2021
Browse
by subject...
    Schedule
view...
 
  COVID-19 Scheduling Updates!
See Stanford's HealthAlerts website for latest updates concerning COVID-19 and academic policies.

1 - 10 of 37 results for: MATSCI ; Currently searching spring courses. You can expand your search to include all quarters

MATSCI 90Q: Resilience, Transformation, and Equilibrium: the Science of Materials

In this course, we will explore the fundamentals of the kinetics of materials while relating them to different phenomena that we observe in our everyday lives. We will study the mechanisms and processes by which materials obtain the mechanical, electronic, and other properties that make them so useful to us. How can we cool water below freezing and keep it from turning into ice? Why is it that ice cream that has been in the freezer for too long does not taste as good? What are crystal defects and why do they help create some of the most useful (semiconductors) and beautiful (gemstones) things we have? This introductory seminar is open to all students, and prior exposure to chemistry, physics, or calculus is NOT required.
Terms: Spr, Sum | Units: 3-4 | UG Reqs: WAY-SMA
Instructors: Patta, Y. (PI)

MATSCI 100: Undergraduate Independent Study

Independent study in materials science under supervision of a faculty member.
Terms: Aut, Win, Spr | Units: 1-3 | Repeatable for credit

MATSCI 144: Thermodynamic Evaluation of Green Energy Technologies

Understand the thermodynamics and efficiency limits of modern green technologies such as carbon dioxide capture from air, fuel cells, batteries, and solar-thermal power. Recommended: ENGR 50 or equivalent introductory materials science course. (Formerly 154)
Terms: Spr | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-SMA

MATSCI 150: Undergraduate Research

Participation in a research project.
Terms: Aut, Win, Spr | Units: 3-6 | Repeatable for credit

MATSCI 156: Solar Cells, Fuel Cells, and Batteries: Materials for the Energy Solution

Terms: Spr | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci
Instructors: Clemens, B. (PI)

MATSCI 159Q: Japanese Companies and Japanese Society (ENGR 159Q)

Preference to sophomores. The structure of a Japanese company from the point of view of Japanese society. Visiting researchers from Japanese companies give presentations on their research enterprise. The Japanese research ethic. The home campus equivalent of a Kyoto SCTI course.
Terms: Spr | Units: 3 | UG Reqs: GER:DB-SocSci
Instructors: Sinclair, R. (PI)

MATSCI 161: Energy Materials Laboratory (MATSCI 171)

From early church architecture through modern housing, windows are passages of energy and matter in the forms of light, sound and air. By letting in heat during the summer and releasing it in winter, windows can place huge demands on air conditioning and heating systems, thereby increasing energy consumption and raising greenhouse gas levels in the atmosphere. Latest advances in materials science have enabled precise and on-demand control of electromagnetic radiation through `smart¿ dynamic windows with photochromic and electrochromic materials that change color and optical density in response to light radiance and electrical potential. In this course, we will spend the whole quarter on a project to make and characterize dynamic windows based on one of the electrochromic material systems, the reversible electroplating of metal alloys. There will be an emphasis in this course on characterization methods such as scanning electron microscopy, x-ray photoelectron spectroscopy, optical spec more »
From early church architecture through modern housing, windows are passages of energy and matter in the forms of light, sound and air. By letting in heat during the summer and releasing it in winter, windows can place huge demands on air conditioning and heating systems, thereby increasing energy consumption and raising greenhouse gas levels in the atmosphere. Latest advances in materials science have enabled precise and on-demand control of electromagnetic radiation through `smart¿ dynamic windows with photochromic and electrochromic materials that change color and optical density in response to light radiance and electrical potential. In this course, we will spend the whole quarter on a project to make and characterize dynamic windows based on one of the electrochromic material systems, the reversible electroplating of metal alloys. There will be an emphasis in this course on characterization methods such as scanning electron microscopy, x-ray photoelectron spectroscopy, optical spectroscopy, four-point probe measurements of conductivity and electrochemical measurements (cyclic voltammetry). The course will finish with students giving presentations on the prospects of using dynamic windows and generic radiation control in cars, homes, commercial buildings or airplanes. Undergraduates register for 161 for 4 units; graduates register for 171 for 3 units.
Terms: Spr | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-SMA
Instructors: Hong, G. (PI)

MATSCI 163: Mechanical Behavior Laboratory (MATSCI 173)

Technologically relevant experimental techniques for the study of the mechanical behavior of engineering materials in bulk and thin film form, including tension testing, nanoindentation, and wafer curvature stress analysis. Metallic and polymeric systems. Register for lecture section in addition to one lab section. Undergraduates register for 163 in 4 units; graduates register in 173 for 3 units
Terms: Spr | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci
Instructors: Kumar, R. (PI)

MATSCI 166: Data Science and Machine Learning Approaches in Chemical and Materials Engineering (CHEMENG 177, CHEMENG 277, MATSCI 176)

Application of Data Science, Statistical Learning, and Machine Learning approaches to modern problems in Chemical and Materials Engineering. This course develops data science approaches, including their foundational mathematical and statistical basis, and applies these methods to data sets of limited size and precision. Methods for regression and clustering will be developed and applied, with an emphasis on validation and error quantification. Techniques that will be developed include linear and nonlinear regression, clustering and logistic regression, dimensionality reduction, unsupervised learning, neural networks, and hidden Markov models. These methods will be applied to a range of engineering problems, including conducting polymers, water purification membranes, battery materials, disease outcome prediction, genomic analysis, organic synthesis, and quality control in manufacturing. Prerequisites: CS 106A or permission from instructor.
Terms: Spr | Units: 3
Filter Results:
term offered
updating results...
teaching presence
updating results...
number of units
updating results...
time offered
updating results...
days
updating results...
UG Requirements (GERs)
updating results...
component
updating results...
career
updating results...
© Stanford University | Terms of Use | Copyright Complaints