Introduction
Quasi Elastic Neutron Scattering (QENS) experiments are performed to get microscopic knowledge of dynamics of atoms in a material. This technique has wide range of applications from energy materials, ionic liquids, polymers and soft materials, ferroelectrics and catalysis.
We use Mantid software to reduce and analyse QENS data obtained from IRIS and OSIRIS instruments at ISIS. Mantid is a open source software available for Windows, Linux and Mac platforms. The current version of Mantid can be downloaded from here.
To get up-to-date features relevant to QENS data analysis, download the latest released version of Mantid, currently Mantid 5.1.
General Overview of QENS Analysis
Data handling of QENS experiments can be divided into three parts: reductions, corrections and analysis.
Data reductions involve converting the raw time-of-flight data to an instrument independent function called dynamic structure factor, S(Q, E),or scattering law. This S(Q,E) depends only on the dynamics of the sample measured. The operation includes:
- Converting from time-of-flight data to energy transfer unit (E)
- Converting from detector angles to momentum transfer (Q) values
Data corrections involve:
- Correcting for container scattering
- Correcting for absorption (from sample and container) and multiple scattering In the package the data after these processes are saved in
After reductions and corrections, subsequent analysis involves operations on intermediate files. Analysis will be taken to refer to science based interpretation of the reduced data. In QENS analysis this is dominated by methods of measuring the peak shapes and widths, and relating these to some theoretical model for the dynamics. The reduced dynamic structure factor, S(Q,E), can be transformed to intermediate scattering function, I(Q,t). Following steps can be taken for analysis:
- Fitting quasielastic peaks using a convolution method
- Calculating peak parameters using Bayesian techniques
- Performing transformations from S(Q,E) to I(Q,t)
- Fitting dependences of peak widths or I(Q,t) with Q using various diffusion models
Other optional steps also can be taken, such as finding out best fitting parameters using Bayesian methods.
A detailed description of available functions for analysis can be found in the following article:
S Mukhopadhyay, B Hewer, S Howells, A Markvardsen, Phys B 563 41 (2019).
Next page Content
