Nanostructured Materials

TU Dresden | Sommersemester Nanostructured Materials

Technical University of Dresden | Summer semester 2025  Nanostructured Materials Tutor: Gianaurelio Cuniberti | Shirong Huang | Leonardo Medrano Sandonas | Huijing Li 

 | Chenchen Wang

NOTES for the organization of the lecture:

·         After each lecture, material for self-study will be available.

SCHEDULE

EXAM: 21.07.2026, at BER/105/H. Strictly start at 9:20, please be there a few minutes before. 

Tuesday 09:20-10:50 (2nd DS, Lecture) in BER/105/H + Wednesday 13:00-14:30 (4th DS, Exercise class) in MER/02/H                                       

Scientific Project Report Submission Deadline: 26.06.2026 

BEGIN

14.04.2026

ROOM

BER/105/H (lecture), MER/02/H (exercise)

LECTURER

Prof. Gianaurelio Cuniberti, Dr. Shirong Huang, Dr. Leonardo Medrano Sandonas, Dr. Huijing Li

TARGET GROUP

Students of the course "Materials Science", 8th semester; Master students of "Physics of Life", "Organic and Molecular Electronics", "Nano-Electronic Systems", and "Physics"; PhD students of "Materials Science", and "Physics".

LANGUAGE

English

CONTENTS

The course deals with the physical properties of nanostructured materials and their synthesis and applications. A general introduction to AI and its applications in nanostructured materials-based electronic devices will also be presented. The course is accompanied by journal club and scientific projects. The following topics are discussed:

·         Introduction to nanostructured materials and nanotechnology

·         General introduction to low-dimensional systems

·         Nanostructuring via top-down and bottom-up techniques (electron beam lithography, optical lithography, scanning probe techniques, vapor-liquid-solid...)

·         Nano/microelectronic devices and microelectromechanical systems

·         Novel low-dimensional materials and their application in sensors

·         Novel gas sensors and biosensors

·         Imaging and characterization techniques 

·         Principles of scanning probe microscopy (SPM) 

·         Machine learning and their applications

·         Journal club 

·         Scientific projects

·         Lab courses

 

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