Thin film science and technology addresses the fundamentals behind the synthesis, characterization, and application of thin film materials. For the purpose of this course, we will define as “thin films” materials which are homogeneous, have one dimension comparable in size to the wavelength of the visible light, and much smaller than the other two dimensions.

Throughout the course we will explore two classes of methods for the synthesis of thin films: chemical and physical methods. Here, the difference between chemical and physical methods depicts whether a chemical transformation occurs (chemical methods) or not (physical methods) during the thin film preparation. These methods will focus on the preparation of dielectric materials, such as linear, non-conjugated polymers, and semiconductors, such as conjugated polymers, 2D materials and more.

After a brief overview of the principal spectroscopic, microscopic, and surface characterization methods, we will dive into classical and forthcoming applications of thin film materials. Here, we will explore the advantages of exploiting thin films and multilayered materials and their application in optics, photonics, and energy devices.

In light of the importance of preparing thin films, two sessions will be dedicated to the synthesis of thin films in our laboratories. In these sessions we will address the synthesis of polymer thin films by spin coating and chemical vapor deposition. The assessment will be in form of a report on these practical lab experiences. The course and the reports are in English language.

Total hours: 30, Lab: 4 hours, Class: 26 hours, Grades: passed/non-passed

Please send an email to Paolo Giusto for more information.

This course will start with an introduction to fluorescence and gradually advance towards using it for modern-day research, with a maximum emphasis on biophysical studies. We will talk about the different applications of fluorescence that can be used for experiments in bulk studies (as in a fluorimeter) and on individual objects (as in microscopy). The attendants will be welcome to see our microscope facilities for hands-on experience with the acquired knowledge.

This would be an interactive course using slides and hand drawings on boards.

Covered topics:

·       Introduction to fluorescence and how to harness for biophysics

·       Lasers (types and principle of work); Optical tweezers; Light-matter interaction

·       Confocal microscope; Two-photon microscope; Second harmonic generation

·      -Different types of super resolution microscopy

·       General polarization; Dye orientation; Fluorescence life-time imaging microscopy (FLIM)

·       Recap after holidays; Light-sheet microscopy

·       Fluorescence correlation spectroscopy (FCS); Raster image correlation spectroscopy (RICS); Fluorescence recovery after photobleaching (FRAP)

·       Förster resonance energy transfer (FRET); Quenching; Total internal reflection fluorescence (TIRF); Mass interferometry

·       Other kinds of imaging microscopy: differential interference contrast (DIC), phase contrast, dark field, bright field

·       Recap of whole course + discussions of latest papers on the techniques learnt

Please contact Shreya Pramanik Shreya.Pramanik@mpikg.mpg.de and Agustin Mangiarotti Agustin.Mangiarotti@mpikg.mpg.de for more information.

After much developments in machine learning, Python has become one of the most used open-source programming tools. Despite the simplicity of this programming language, the large variety of tools and packages available make it difficult for the novice to find what is just right for you.

With this course you will learn some of the very basic aspects of Python in relation to the most common tools and tests in statistics.

After having taken this course, you will be able to move quickly to other aspects of Python and learn by yourself what is needed for your personal and professional development.

Before taking this course, you have to install Python and the Jupyter notebook on your computer. You should make sure that numpy, scipy, matplotlib, and pandas are installed.

We will be working entirely online with the Jupyter notebook open. The course will be a mixture of power point presentation, to cover some elementary statistics concept, and programming to solve some problems and exercises using Python.

This course is designed for people with little or no knowledge of Python. Nevertheless, elementary programming in any other language and elementary statistics knowledge are a prerequisite to attend this course.

At the end, you will learn to upload csv files, plot your data, perform descriptive statistics, parameter estimation, z-test and t-test, correlation and linear regression modeling.

Registration to attend this course is compulsory. Please follow this link to register for the course.

This course covers different aspects of glycobiology and the biological roles of glycans and lectins.

Please contact Oren Moskovitz or Christian Roth for more information.