Lucio Isa: Catalogue data in Autumn Semester 2022 |  |
| Name | Prof. Dr. Lucio Isa |
| Field | Soft Materials and Interfaces |
| Address | Weiche Materialien u. Grenzflächen ETH Zürich, HCI H 525 Vladimir-Prelog-Weg 1-5/10 8093 Zürich SWITZERLAND |
| Telephone | +41 44 633 63 76 |
| lucio.isa@mat.ethz.ch | |
| URL | http://www.isa.mat.ethz.ch/ |
| Department | Materials |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 327-0113-00L | Foundations of Materials Science I | 2 credits | 2G | L. Isa | |||||||||||||||||||||||
| Abstract | The basic physical concepts for the description of materials are taught, partly in self-study, and applied in exercises. Basic atomistic and macroscopic concepts (e.g. phase diagrams, phase transformations, response functions) are introduced through examples. Selected topics are deepened in classroom lectures. | ||||||||||||||||||||||||||
| Learning objective | Students are able to - name the basic concepts of materials science. (remember, 1) - describe simple relations between atomic structure and macroscopic properties. (understand, 2) - calculate basic material-specific quantities. (apply, 3) - read and interpret phase diagrams, material characteristic (e.g. stress-strain) diagrams and Ashby plots (analyse, 4) | ||||||||||||||||||||||||||
| Content | Atomic structure Crystalline structure and defects Thermodynamics, phase diagrams and phase transformations Diffusion Mechanical and thermal properties of materials | ||||||||||||||||||||||||||
| Literature | Main textbook: William D. Callister, Jr., David G. Rethwisch Materials Science and Engineering - An Introduction 8th Ed., Wiley, Hoboken NJ, 2011 Alternatives: Milton Ohring Engineering Materials Science Academic Press, 1995, https://doi.org/10.1016/B978-0-12-524995-9.X5023-5 James F. Shackelford Introduction to Materials Science for Engineers 5th Ed., Prentice Hall, New Jersey, 2000 | ||||||||||||||||||||||||||
| 327-0505-00L | Surfaces, Interfaces and their Applications I | 3 credits | 2V + 1U | N. Spencer, M. P. Heuberger, L. Isa | |||||||||||||||||||||||
| Abstract | After being introduced to the physical/chemical principles and importance of surfaces and interfaces, the student is introduced to the most important techniques that can be used to characterize surfaces. Later, liquid interfaces are treated, followed by an introduction to the fields of tribology (friction, lubrication, and wear) and corrosion. | ||||||||||||||||||||||||||
| Learning objective | To gain an understanding of the physical and chemical principles, as well as the tools and applications of surface science, and to be able to choose appropriate surface-analytical approaches for solving problems. | ||||||||||||||||||||||||||
| Content | Introduction to Surface Science Physical Structure of Surfaces Surface Forces (static and dynamic) Adsorbates on Surfaces Surface Thermodynamics and Kinetics The Solid-Liquid Interface Electron Spectroscopy Vibrational Spectroscopy on Surfaces Scanning Probe Microscopy Introduction to Tribology Introduction to Corrosion Science | ||||||||||||||||||||||||||
| Lecture notes | Script Download: https://moodle-app2.let.ethz.ch/course/view.php?id=17455 | ||||||||||||||||||||||||||
| Literature | Script Download: https://moodle-app2.let.ethz.ch/course/view.php?id=17455 Book: "Surface Analysis--The Principal Techniques", Ed. J.C. Vickerman, Wiley, ISBN 0-471-97292 | ||||||||||||||||||||||||||
| Prerequisites / Notice | Chemistry: General undergraduate chemistry including basic chemical kinetics and thermodynamics Physics: General undergraduate physics including basic theory of diffraction and basic knowledge of crystal structures | ||||||||||||||||||||||||||
Competencies |
| ||||||||||||||||||||||||||
| 327-1207-00L | Engineering with Soft Materials | 5 credits | 4G | J. Vermant, L. Isa | |||||||||||||||||||||||
| Abstract | In this course the engineering with soft materials is discussed. First, scaling principles to design structural and functional properties are introduced a. Second, the characterisation techniques to interrogate the structure property relations are introduced, which include rheology, advanced optical microscopies, static and dynamic scattering and techniques for liquid interfaces. | ||||||||||||||||||||||||||
| Learning objective | The learning goals of the course are to introduce the students to soft matter and its technological applications, to see how the structure property relations depend on fundamental formulation properties and processing steps. Students should also be able to select a measurement technique to evaluate the properties. | ||||||||||||||||||||||||||
| Lecture notes | slides with text notes accompanying each slide are presented. | ||||||||||||||||||||||||||
| 327-2227-00L | Machine Learning (MaP Doctoral School)  Number of participants limited to 15. Only for doctoral students of the MaP Doctoral School. Priority is given to doctoral students affiliated with the “Soft Materials” thematic track. All applicants must additionally register by email: map-ds@ethz.ch | 6 credits | 13P | L. Isa | |||||||||||||||||||||||
| Abstract | Microscopy images, irrespective of the specific imaging technique, e.g. optical, electron or atomic force microscopy, are an extremely rich source of quantitative data. With the ever increasing push to enhance spatial and temporal resolution, as well as with the increase of storage and computing power, very large amounts of data are easily generated and require automation for data extraction. From | ||||||||||||||||||||||||||
| Learning objective | This course, aimed at doctoral students, has the goal to guide attendees through a progression from basic machine learning (ML) methods, through the extension of those to increasingly complex analyses all the way to offering the students the possibility to directly apply the concepts learned during the course to their own data. | ||||||||||||||||||||||||||
| Content | The course will combine lectures with hands-on exercises in concentrated blocks across the semester. Students have the possibility to select different blocks, for instance if they already have basic ML programming knowledge. The students will also be able to work on a project related to their research where they apply ML to some imaging data. | ||||||||||||||||||||||||||
| Prerequisites / Notice | Basic programming knowledge in Python is required. | ||||||||||||||||||||||||||