Hi, you are logged in as , if you are not , please click here

Characterisation of Advanced Materials - 4 - 8 December 2017

Info
Location
Extras
Contact
More Info

Course Information

Characterisation of Advanced Materials - 4 - 8 December 2017

The aim of this five-day course is to introduce the principles of the most popular materials characterisation methods based on microscopy, chemical, physical and structural analysis and thermal techniques. Consideration will also be given to the analysis of particulate materials and coatings. The basic principles used for the physical characterisation of materials will be outlined; microscopy by light, electrons and scanned probes will be introduced; and the readily available bulk characterisation methods such as diffraction, X-ray analysis and vibrational spectroscopies will be described.

 

Course Code

CAM2017

Course Dates

4th December 2017 – 8th December 2017

Course Leader

Prof John Watts
Course Description

 

The course will be staffed by lecturers with considerable experience in materials characterisation. The programme will comprise lectures, laboratory demonstrations, computer simulations and exercise classes with the course tutors.

Surface analysis by electron and ion spectroscopies will also form an important part of the course. Particular emphasis will be paid to the use of a variety of methods in multi-technique approaches for the characterisation of advanced materials.

Topics covered include: thermal analysis; spectroscopy; electron energy loss and ion beam analysis.

 

Module Aims

This course aims to:

  • Provide a systematic understanding of the principles, equipment and practices of the most popular materials characterisation methods based on microscopy, chemical, physical and structural analysis and thermal techniques.
  • Equip students with the knowledge of a broad range of characterisation techniques, such that they clearly understand the capabilities of such methods and their role in completing the process-structure-property relationship

Learning Outcomes

Upon successful completion of the module, students should:

  • have an understanding of the principles and a knowledge of the capabilities and limitations of the different types of analysis covered in the course
  • be able to recommend appropriate methods for particular problems and have a good understanding of the data obtained

How would you rate your experience today?

How can we contact you?

What could we do better?

   Change Code