Search result: Catalogue data in Autumn Semester 2022

Biomedical Engineering Master Information
Track Courses
Bioelectronics
Track Core Courses
During the Master programme, a minimum of 12 CP must be obtained from track core courses.
NumberTitleTypeECTSHoursLecturers
151-0604-00LMicrorobotics Information W4 credits3GB. Nelson
AbstractMicrorobotics is an interdisciplinary field that combines aspects of robotics, micro and nanotechnology, biomedical engineering, and materials science. The aim of this course is to expose students to the fundamentals of this emerging field. Throughout the course, the students apply these concepts in assignments. The course concludes with an end-of-semester examination.
ObjectiveThe objective of this course is to expose students to the fundamental aspects of the emerging field of microrobotics. This includes a focus on physical laws that predominate at the microscale, technologies for fabricating small devices, bio-inspired design, and applications of the field.
ContentMain topics of the course include:
- Scaling laws at micro/nano scales
- Electrostatics
- Electromagnetism
- Low Reynolds number flows
- Observation tools
- Materials and fabrication methods
- Applications of biomedical microrobots
Lecture notesThe powerpoint slides presented in the lectures will be made available as pdf files. Several readings will also be made available electronically.
Prerequisites / NoticeThe lecture will be taught in English.
151-0605-00LNanosystemsW4 credits4GA. Stemmer
AbstractFrom atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles.
Intermolecular forces, their macroscopic manifestations, and ways to control such interactions.
Self-assembly and directed assembly of 2D and 3D structures.
Special emphasis on the emerging field of molecular electronic devices.
ObjectiveFamiliarize students with basic science and engineering principles governing the nano domain.
ContentThe course addresses basic science and engineering principles ruling the nano domain. We particularly work out the links between topics that are traditionally taught separately. Familiarity with basic concepts of quantum mechanics is expected.

Special emphasis is placed on the emerging field of molecular electronic devices, their working principles, applications, and how they may be assembled.

Topics are treated in 2 blocks:

(I) From Quantum to Continuum
From atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles.

(II) Interaction Forces on the Micro and Nano Scale
Intermolecular forces, their macroscopic manifestations, and ways to control such interactions.
Self-assembly and directed assembly of 2D and 3D structures.
Literature- Kuhn, Hans; Försterling, H.D.: Principles of Physical Chemistry. Understanding Molecules, Molecular Assemblies, Supramolecular Machines. 1999, Wiley, ISBN: 0-471-95902-2
- Chen, Gang: Nanoscale Energy Transport and Conversion. 2005, Oxford University Press, ISBN: 978-0-19-515942-4
- Ouisse, Thierry: Electron Transport in Nanostructures and Mesoscopic Devices. 2008, Wiley, ISBN: 978-1-84821-050-9
- Wolf, Edward L.: Nanophysics and Nanotechnology. 2004, Wiley-VCH, ISBN: 3-527-40407-4

- Israelachvili, Jacob N.: Intermolecular and Surface Forces. 2nd ed., 1992, Academic Press,ISBN: 0-12-375181-0
- Evans, D.F.; Wennerstrom, H.: The Colloidal Domain. Where Physics, Chemistry, Biology, and Technology Meet. Advances in Interfacial Engineering Series. 2nd ed., 1999, Wiley, ISBN: 0-471-24247-0
- Hunter, Robert J.: Foundations of Colloid Science. 2nd ed., 2001, Oxford, ISBN: 0-19-850502-7
Prerequisites / NoticeCourse format:

Lectures and Mini-Review presentations: Thursday 10-13

Homework: Mini-Review
(compulsory continuous performance assessment)

Each student selects a paper (list distributed in class) and expands the topic into a Mini-Review that illuminates the particular field beyond the immediate results reported in the paper. Each Mini-Review will be presented both orally and as a written paper.
151-0621-00LMicrosystems I: Process Technology and IntegrationW6 credits3V + 3UM. Haluska, C. Hierold
AbstractStudents are introduced to the fundamentals of semiconductors, the basics of micromachining and silicon process technology and will learn about the fabrication of microsystems and -devices by a sequence of defined processing steps (process flow).
ObjectiveStudents are introduced to the basics of micromachining and silicon process technology and will understand the fabrication of microsystem devices by the combination of unit process steps ( = process flow).
Content- Introduction to microsystems technology (MST) and micro electro mechanical systems (MEMS)
- Basic silicon technologies: Thermal oxidation, photolithography and etching, diffusion and ion implantation, thin film deposition.
- Specific microsystems technologies: Bulk and surface micromachining, dry and wet etching, isotropic and anisotropic etching, beam and membrane formation, wafer bonding, thin film mechanical properties.
Application of selected technologies will be demonstrated on case studies.
Lecture notesHandouts (available online)
Literature- S.M. Sze: Semiconductor Devices, Physics and Technology
- W. Menz, J. Mohr, O.Paul: Microsystem Technology
- Hong Xiao: Introduction to Semiconductor Manufacturing Technology
- M. J. Madou: Fundamentals of Microfabrication and Nanotechnology, 3rd ed.
- T. M. Adams, R. A. Layton: Introductory MEMS, Fabrication and Applications
Prerequisites / NoticePrerequisites: Physics I and II
227-0105-00LIntroduction to Estimation and Machine Learning Information Restricted registration - show details W6 credits4GH.‑A. Loeliger
AbstractMathematical basics of estimation and machine learning, with a view towards applications in signal processing.
ObjectiveStudents master the basic mathematical concepts and algorithms of estimation and machine learning.
ContentReview of probability theory;
basics of statistical estimation;
least squares and linear learning;
Hilbert spaces;
singular-value decomposition;
kernel methods, neural networks, and more
Lecture notesLecture notes will be handed out as the course progresses.
Prerequisites / Noticesolid basics in linear algebra and probability theory
227-0311-00LQubits, Electrons, PhotonsW6 credits3V + 2UT. Zambelli
AbstractIn-depth analysis of the quantum mechanics origin of nuclear magnetic resonance (qubits, two-level systems), of LASER (quantization of the electromagnetic field, photons), and of electron transfer (from electrochemistry to photosynthesis).
ObjectiveBeside electronics nanodevices, D-ITET is pushing its research in the fields of NMR (MRI), electrochemistry, bioelectronics, nano-optics, and quantum information, which are all rationalized in terms of quantum mechanics.

Starting from the axioms of quantum mechanics, we will derive the fascinating theory describing spin and qubits, electron transitions and transfer, photons and LASER: quantum mechanics is different because it mocks our daily Euclidean intuition!

In this way, students will work out a robust quantum mechanics (theoretical!!!) basis which will help them in their advanced studies of the following masters: EEIT (batteries), Biomedical Engineering (NMR, bioelectronics), Quantum Engineering, Micro- and Nanosystems.

IMPORTANT: "qubits" from the point of view of NMR (and NOT from that of quantum computing!).
Content• Lagrangian and Hamiltonian: Symmetries and Poisson Brackets
• Postulates of QM: Hilbert Spaces and Operators
• Heisenberg’s Matrix Mechanics: Hamiltonian and Time Evolution Operator
• Density Operator
• Spin: Qubits, Bloch Equations, and NMR
• Entanglement
• Symmetries and Corresponding Operators
• Schrödinger's Wave Mechanics: Electrons in a Periodic Potential and Energy Bands
• Harmonic Oscillator: Creation and Annihilation Operators
• Identical Particles: Bosons and Fermions
• Quantization of the Electromagnetic Field: Photons, Absorption and Emission, LASER
• Electron Transfer: Marcus Theory via Born-Oppenheimer, Franck-Condon, Landau-Zener
Lecture notesNo lecture notes because the proposed textbooks together with the provided supplementary material are more than exhaustive!

!!!!! I am using OneNote. All lectures and exercises will be broadcast via ZOOM and correspondingly recorded (link in Moodle) !!!!!
Literature• J.S. Townsend, "A Modern Approach to Quantum Mechanics", Second Edition, 2012, University Science Books

• M. Le Bellac, "Quantum Physics", 2011, Cambridge University Press

• (Lagrangian and Hamiltonian) L. Susskind, G. Hrabovsky, "Theoretical Minimum: What You Need to Know to Start Doing Physics", 2014, Hachette Book Group USA

Supplementary material will be uploaded in Moodle.

_ _ _ _ _ _ _

+ (as rigorous and profound presentation of the mathematical framework) G. Dell'Antonio, "Lectures on the Mathematics of Quantum Mechanics I", 2015, Springer

+ (as account of those formidable years) G. Gamow, "Thirty Years that Shook Physics", 1985, Dover Publications Inc.
Prerequisites / NoticeThe course has been intentionally conceived to be self-consistent with respect to QM for those master students not having encountered it in their track yet. Therefore, a presumably large overlapping has to be expected with a (welcome!) QM introduction course like the D-ITET "Physics II".

A solid base of Analysis I & II as well as of Linear Algebra is really helpful.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesfostered
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesfostered
Problem-solvingassessed
Project Managementassessed
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityassessed
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityassessed
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsassessed
Self-awareness and Self-reflection assessed
Self-direction and Self-management assessed
227-0385-10LBiomedical ImagingW6 credits5GS. Kozerke, K. P. Prüssmann
AbstractIntroduction to diagnostic medical imaging based on electromagnetic and acoustic fields including X-ray planar and tomographic imaging, radio-tracer based nuclear imaging techniques, magnetic resonance imaging and ultrasound-based procedures.
ObjectiveUpon completion of the course students are able to:

• Explain the physical and mathematical foundations of diagnostic medical imaging systems
• Characterize system performance based on signal-to-noise ratio, contrast-to-noise ratio and transfer function
• Design a basic diagnostic imaging system chain including data acquisition and data reconstruction
• Identify advantages and limitations of different imaging methods in relation to medical diagnostic applications
Content• Introduction (intro, overview, history)
• Signal theory and processing (foundations, transforms, filtering, signal-to-noise ratio)
• X-rays (production, tissue interaction, contrast, modular transfer function)
• X-rays (resolution, detection, digital subtraction angiography, Radon transform)
• X-rays (filtered back-projection, spiral computed tomography, image quality, dose)
• Nuclear imaging (radioactive tracer, collimation, point spread function, SPECT/PET)
• Nuclear imaging (detection principles, image reconstruction, kinetic modelling)
• Magnetic Resonance (magnetic moment, spin transitions, excitation, relaxation, detection)
• Magnetic Resonance (plane wave encoding, Fourier reconstruction, pulse sequences)
• Magnetic Resonance (contrast mechanisms, gradient- and spin-echo, applications)
• Ultrasound (mechanical wave generation, propagation in tissue, reflection, transmission)
• Ultrasound (spatial and temporal resolution, phased arrays)
• Ultrasound (Doppler shift, implementations, applications)
• Summary, example exam questions
Lecture notesLecture notes and handouts
LiteratureWebb A, Smith N.B. Introduction to Medical Imaging: Physics, Engineering and Clinical Applications; Cambridge University Press 2011
Prerequisites / NoticeAnalysis, Linear algebra, Physics, Basics of signal theory, Basic skills in Matlab/Python programming
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingfostered
Media and Digital Technologiesfostered
Problem-solvingassessed
Social CompetenciesCommunicationassessed
Cooperation and Teamworkassessed
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsfostered
Self-direction and Self-management fostered
227-0386-00LBiomedical Engineering Information W4 credits3GJ. Vörös, S. J. Ferguson, S. Kozerke, M. P. Wolf, M. Zenobi-Wong
AbstractIntroduction into selected topics of biomedical engineering as well as their relationship with physics and physiology. The focus is on learning the basic vocabulary of biomedical engineering and getting familiar with concepts that govern common medical instruments and the most important organs from an engineering point of view.
ObjectiveIntroduction into selected topics of biomedical engineering as well as their relationship with physics and physiology. The course provides an overview of the various topics of the different tracks of the biomedical engineering master course and helps orienting the students in selecting their specialized classes and project locations. It also serves as an introduction to the field for students of the ITET, MAVT, HEST and other bachelor programs.
In addition, the most recent achievements and trends of the field of biomedical engineering are also outlined.
ContentHistory of BME and the role of biomedical engineers. Ethical issues related to BME.
Biomedical sensors both wearable and also biochemical sensors.
Bioelectronics: Nernst equation, Donnan equilibrium, equivalent circuits of biological membranes and bioelectronic devices.
Bioinformatics: genomic and proteomic tools, databases and basic calculations.
Equations describing basic reactions and enzyme kinetics.
Medical optics: Optical components and systems used in hospitals.
Basic concepts of tissue engineering and organ printing.
Biomaterials and their medical applications.
Function of the heart and the circulatory system.
Transport and exchange of substances in the human body, compartment modeling.
The respiratory system.
Bioimaging.
Orthopedic biomechanics.
Lectures (2h), discussion of practical exercises (1h) and homework exercises.
Lecture notesIntroduction to Biomedical Engineering
by Enderle, Banchard, and Bronzino

AND

moodle page of the course
Prerequisites / NoticeNo specific requirements, BUT
ITET, MAVT, PHYS students will have to learn a lot of new words related to biochemistry, biology and medicine, while
HEST and BIOL students will have to grasp basic engineering concepts (circuits, equations, etc.).
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesfostered
Problem-solvingfostered
Project Managementfostered
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingfostered
Critical Thinkingfostered
Integrity and Work Ethicsfostered
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
227-0393-10LBioelectronics and Biosensors Information W6 credits2V + 2UJ. Vörös, M. F. Yanik
AbstractThe course introduces bioelectricity and the sensing concepts that enable obtaining information about neurons and their networks. The sources of electrical fields and currents in the context of biological systems are discussed. The fundamental concepts and challenges of measuring bioelectronic signals and the basic concepts to record optogenetically modified organisms are introduced.
ObjectiveDuring this course the students will:
- learn the basic concepts in bioelectronics including the sources of bioelectronic signals and the methods to measure them
- be able to solve typical problems in bioelectronics
- learn about the remaining challenges in this field
ContentLecture topics:

1. Introduction

Sources of bioelectronic signals
2. Membrane and Transport
3-4. Action potential and Hodgkin-Huxley

Measuring bioelectronic signals
5. Detection and Noise
6. Measuring currents in solutions, nanopore sensing and patch clamp pipettes
7. Measuring potentials in solution and core conductance model
8. Measuring electronic signals with wearable electronics, ECG, EEG
9. Measuring mechanical signals with bioelectronics

In vivo stimulation and recording
10. Functional electric stimulation
11. In vivo electrophysiology

Optical recording and control of neurons (optogenetics)
12. Measuring neurons optically, fundamentals of optical microscopy
13. Fluorescent probes and scanning microscopy, optogenetics, in vivo microscopy

14. Measuring biochemical signals
Lecture notesA detailed script is provided to each lecture including the exercises and their solutions.
LiteraturePlonsey and Barr, Bioelectricity: A Quantitative Approach (Third edition)
Prerequisites / NoticeThe course requires an open attitude to the interdisciplinary approach of bioelectronics.
In addition, it requires undergraduate entry-level familiarity with electric & magnetic fields/forces, resistors, capacitors, electric circuits, differential equations, calculus, probability calculus, Fourier transformation & frequency domain, lenses / light propagation / refractive index, pressure, diffusion AND basic knowledge of biology and chemistry (e.g. understanding the concepts of concentration, valence, reactants-products, etc.).
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingfostered
Media and Digital Technologiesfostered
Problem-solvingassessed
Project Managementfostered
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsfostered
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
227-0421-00LDeep Learning in Artificial and Biological Neuronal NetworksW4 credits3GB. Grewe
AbstractDeep-Learning (DL) a brain-inspired weak for of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. However, DL is far from being understood and investigating learning in biological networks might serve again as a compelling inspiration to think differently about state-of-the-art ANN training methods.
ObjectiveThe main goal of this lecture is to provide a comprehensive overview into the learning principles neuronal networks as well as to introduce a diverse skill set (e.g. simulating a spiking neuronal network) that is required to understand learning in large, hierarchical neuronal networks. To achieve this the lectures and exercises will merge ideas, concepts and methods from machine learning and neuroscience. These will include training basic ANNs, simulating spiking neuronal networks as well as being able to read and understand the main ideas presented in today’s neuroscience papers.
After this course students will be able to:
- read and understand the main ideas and methods that are presented in today’s neuroscience papers
- explain the basic ideas and concepts of plasticity in the mammalian brain
- implement alternative ANN learning algorithms to ‘error backpropagation’ in order to train deep neuronal networks.
- use a diverse set of ANN regularization methods to improve learning
- simulate spiking neuronal networks that learn simple (e.g. digit classification) tasks in a supervised manner.
ContentDeep-learning a brain-inspired weak form of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. The origins of deep hierarchical learning can be traced back to early neuroscience research by Hubel and Wiesel in the 1960s, who first described the neuronal processing of visual inputs in the mammalian neocortex. Similar to their neocortical counterparts ANNs seem to learn by interpreting and structuring the data provided by the external world. However, while on specific tasks such as playing (video) games deep ANNs outperform humans (Minh et al, 2015, Silver et al., 2018), ANNs are still not performing on par when it comes to recognizing actions in movie data and their ability to act as generalizable problem solvers is still far behind of what the human brain seems to achieve effortlessly. Moreover, biological neuronal networks can learn far more effectively with fewer training examples, they achieve a much higher performance in recognizing complex patterns in time series data (e.g. recognizing actions in movies), they dynamically adapt to new tasks without losing performance and they achieve unmatched performance to detect and integrate out-of-domain data examples (data they have not been trained with). In other words, many of the big challenges and unknowns that have emerged in the field of deep learning over the last years are already mastered exceptionally well by biological neuronal networks in our brain. On the other hand, many facets of typical ANN design and training algorithms seem biologically implausible, such as the non-local weight updates, discrete processing of time, and scalar communication between neurons. Recent evidence suggests that learning in biological systems is the result of the complex interplay of diverse error feedback signaling processes acting at multiple scales, ranging from single synapses to entire networks.
Lecture notesThe lecture slides will be provided as a PDF after each lecture.
Prerequisites / NoticeThis advanced level lecture requires some basic background in machine/deep learning. Thus, students are expected to have a basic mathematical foundation, including linear algebra, multivariate calculus, and probability. The course is not to be meant as an extended tutorial of how to train deep networks in PyTorch or Tensorflow, although these tools used.
The participation in the course is subject to the following conditions:

1) The number of participants is limited to 120 students (MSc and PhDs).

2) Students must have taken the exam in Deep Learning (263-3210-00L) or have acquired equivalent knowledge.
227-1037-00LIntroduction to Neuroinformatics Information W6 credits2V + 1U + 1AV. Mante, M. Cook, B. Grewe, G. Indiveri, D. Kiper, W. von der Behrens
AbstractThe course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented.
ObjectiveUnderstanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions.
ContentThis course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, simple neural architectures of feedforward and recurrent networks are discussed in the context of co-ordination, control, and integration of sensory and motor information.

Connections to computer science and artificial intelligence are discussed, but the main focus of the course is on establishing the biological basis of computations in neurons.
376-1714-00LBiocompatible MaterialsW4 credits3VK. Maniura, M. Rottmar, M. Zenobi-Wong
AbstractIntroduction to molecules used for biomaterials, molecular interactions between different materials and biological systems (molecules, cells, tissues). The concept of biocompatibility is discussed and important techniques from biomaterials research and development are introduced.
ObjectiveThe course covers the follwing topics:
1. Introdcution into molecular characteristics of molecules involved in the materials-to-biology interface. Molecular design of biomaterials.
2. The concept of biocompatibility.
3. Introduction into methodology used in biomaterials research and application.
4. Introduction to different material classes in use for medical applications.
ContentIntroduction into natural and polymeric biomaterials used for medical applications. The concepts of biocompatibility, biodegradation and the consequences of degradation products are discussed on the molecular level. Different classes of materials with respect to potential applications in tissue engineering, drug delivery and for medical devices are introduced. Strong focus lies on the molecular interactions between materials having very different bulk and/or surface chemistry with living cells, tissues and organs. In particular the interface between the materials surfaces and the eukaryotic cell surface and possible reactions of the cells with an implant material are elucidated. Techniques to design, produce and characterize materials in vitro as well as in vivo analysis of implanted and explanted materials are discussed.
A link between academic research and industrial entrepreneurship is demonstrated by external guest speakers, who present their current research topics.
Lecture notesHandouts are deposited online (moodle).
LiteratureLiterature:
- Biomaterials Science: An Introduction to Materials in Medicine, Ratner B.D. et al, 3rd Edition, 2013
- Comprehensive Biomaterials, Ducheyne P. et al., 1st Edition, 2011

(available online via ETH library)

Handouts and references therin.
402-0674-00LPhysics in Medical Research: From Atoms to Cells Information W6 credits2V + 1UB. K. R. Müller
AbstractScanning probe and diffraction techniques allow studying activated atomic processes during early stages of epitaxial growth. For quantitative description, rate equation analysis, mean-field nucleation and scaling theories are applied on systems ranging from simple metallic to complex organic materials. The knowledge is expanded to optical and electronic properties as well as to proteins and cells.
ObjectiveThe lecture series is motivated by an overview covering the skin of the crystals, roughness analysis, contact angle measurements, protein absorption/activity and monocyte behaviour.

As the first step, real structures on clean surfaces including surface reconstructions and surface relaxations, defects in crystals are presented, before the preparation of clean metallic, semiconducting, oxidic and organic surfaces are introduced.

The atomic processes on surfaces are activated by the increase of the substrate temperature. They can be studied using scanning tunneling microscopy (STM) and atomic force microscopy (AFM). The combination with molecular beam epitaxy (MBE) allows determining the sizes of the critical nuclei and the other activated processes in a hierarchical fashion. The evolution of the surface morphology is characterized by the density and size distribution of the nanostructures that could be quantified by means of the rate equation analysis, the mean-field nucleation theory, as well as the scaling theory. The surface morphology is further characterized by defects and nanostructure's shapes, which are based on the strain relieving mechanisms and kinetic growth processes.

High-resolution electron diffraction is complementary to scanning probe techniques and provides exact mean values. Some phenomena are quantitatively described by the kinematic theory and perfectly understood by means of the Ewald construction. Other phenomena need to be described by the more complex dynamical theory. Electron diffraction is not only associated with elastic scattering but also inelastic excitation mechanisms that reflect the electronic structure of the surfaces studied. Low-energy electrons lead to phonon and high-energy electrons to plasmon excitations. Both effects are perfectly described by dipole and impact scattering.

Thin-films of rather complex organic materials are often quantitatively characterized by photons with a broad range of wavelengths from ultra-violet to infra-red light. Asymmetries and preferential orientations of the (anisotropic) molecules are verified using the optical dichroism and second harmonic generation measurements. Recently, ellipsometry has been introduced to on-line monitor film thickness, and roughness with sub-nanometer precision. These characterisation techniques are vital for optimising the preparation of medical implants.

Cell-surface interactions are related to the cell adhesion and the contractile cellular forces. Physical means have been developed to quantify these interactions. Other physical techniques are introduced in cell biology, namely to count and sort cells, to study cell proliferation and metabolism and to determine the relation between cell morphology and function.

X rays are more and more often used to characterise the human tissues down to the nanometer level. The combination of highly intense beams only some micrometers in diameter with scanning enables spatially resolved measurements and the determination of tissue's anisotropies of biopsies.
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