Lorenzo De Pietro: Catalogue data in Autumn Semester 2020 |
| Name | Dr. Lorenzo De Pietro |
| Address | Lehre Materialwissenschaft ETH Zürich, HCP F 33.2 Leopold-Ruzicka-Weg 4 8093 Zürich SWITZERLAND |
| Telephone | +41 44 633 47 90 |
| lorenzo.depietro@mat.ethz.ch | |
| Department | Materials |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 327-0111-00L | Projects and Lab Courses I   | 7 credits | 7P | M. B. Willeke, L. De Pietro, M. R. Dusseiller, S. Morgenthaler Kobas, T.‑B. Schweizer | |
| Abstract | Practical introduction to the basics of the scientific method, materials science, physics and chemistry in the form of laboratory experiments and projects, some of which are closely related to the lectures in the first year. Important chemical and physical methods are tested, project work is practiced and the basics of working safely in the laboratory are learned. | ||||
| Learning objective | The students - keep a laboratory journal independently, completely and appropriately. - can evaluate and display measurement data in a targeted manner. - are able to write laboratory reports appropriately. - know the communicative and rhetorical factors that are decisive for the success of an oral presentation. - create effective presentation documents. - know the general safety rules and disposal concepts for working in laboratories and apply them practically. - proceed correctly in case of accidents and evacuations. - learn practically how to fight a fire (fire protection course of the ETH). - apply the basic knowledge in analytics, chemistry, physics and materials science acquired in the base year in a practical way. - practice carrying out small experiments or small projects independently under supervision. | ||||
| Content | In the area of scientific work: Keeping lab journals, data analysis, writing reports, presentation techniques, Test preparation and introduction to safe working and behaviour in the lab. Lab experiments: Experiments from the fields of synthetic and analytical chemistry and experiments from the fields of physics and materials science, e.g: Mechanical/thermal properties (e.g. modulus of elasticity, fracture mechanics), thermodynamics, colloid chemistry, "particle tracking" with DLS and microscopy, surface technology, "wood, stone and metal" processing, and electrochemistry. Some practical experiments are organized as short projects (two afternoons), e.g. "Building a microscope from a webcam", etc. In the projects: Two "reverse engineering" projects with everyday objects: Analysis of construction and materials, functioning in the overall context, life cycle of materials, alternative materials, etc. | ||||
| Lecture notes | Instructions and further information on the individual experiments and projects (objectives, theory, experimental procedure, notes on evaluation) are available on the following website (https://praktikum.mat.ethz.ch). | ||||
| Prerequisites / Notice | Special students and auditors need a special permission from the lecturers | ||||
| 327-0114-00L | Programming I | 2 credits | 2G | L. De Pietro, C. Ederer | |
| Abstract | This course provides an introduction to the general computer and programming concepts, which are necessary to perform numerical calculations, representations and simulations in materials science. | ||||
| Learning objective | - Students independently develop programs to accomplish numerical calculations, representations and simulations. - They analyse and understand the functionality of existing programs and can supplement or adapt them according to their requirements. - They recognize basic computer science concepts and apply algorithmic thinking, i.e. they have the ability to solve problems systematically using developed algorithms. | ||||
| Content | The course contains a first introduction to Python and Matlab. It contains: • Basic programming concepts of structural programming like - Variables - Lists - Loops - Branches - Control structures • Input and output • Modular structure of programs with functions • Flowcharts • Numerical accuracy • Data evaluation and presentation - Regression - Interpolation - Curves fit • Complexity Theory • Sorting and searching • Dynamic programming • Recursion • Graph Algorithms | ||||
| Lecture notes | Moodle, Code Expert, ... | ||||
| Literature | https://wiki.python.org/moin/BeginnersGuide | ||||