[English]

Feature Parameterisation for Grazing Incidence X-Ray Scattering

(supervisor: Tom Knight)

Nature of project: software, data analysis

Available to full-time physicists or joint students.

Project description and methodology

Polymer films can have a large range of morphologies depending on ambient conditions including temperature and chemical composition of the atmosphere as well as external stimuli such as applied stresses. Possible configurations include layered structures, micelles (spheres) or cylinders embedded in a matrix, and even complex gyroid patterns.

Using grazing-incidence small-angle x-ray scattering (GISAXS), it is possible to distinguish these morphologies from the characteristics of two-dimensional scattering patterns (detector images) taken in reflection geometry. For example, layered structures result in "wings" either side of the specular reflection with a characteristic vertical ripple pattern. We have obtained series of such patterns during synchrotron experiments while varying environmental parameters. The aim of this project is to identify sensitive

features in the patterns and track their evolution during the course of these data series.

The patterns of interest and those generated by other sources appear mapped together the resulting image data. The projects primary objective is to study the feasibility and effectiveness of identifying and extracting these component patterns from that image data.

Can the extracted information be used draw conclusions regarding the samples properties?

Can the image data be reconstructed from the identified components?

A successful project will develop beyond the above in one/some of the following directions:
The project will require software development and analysis.

Initially, two possible approaches present themselves:

1) The several expected patterns and features are defined, integrated together and fitted to the image data.

2) Working feature by feature (from most to least prominent); a feature is defined and fitted to the image data. That fit is used to remove the component and the process cycles to the next feature.

The definition may be by image segmentation techniques or explicit mathematical expressions.

By either method, the quantification of the patterns and features are refined by iteration. Characterisation of the rate accuracy and efficacy is challenging.

When considering where to take your project, please bear in mind the time available. It is preferable to do fewer things well than to try many and not get conclusive results on any of them. However, sometimes it is useful to have a couple of strands of investigation in parallel to work on in case delays occur.

Additional scope or challenge if taken as a Year-4 project: The difficult question 'Can the extracted information be used draw conclusions regarding the samples properties?' should be addressed.

Initial literature for students:

  1. polymer film morphologies: Xinchang Pang et al.; Nanoscale 5 (2013) 8695
  2. Alexander Mordvintsev & Abid K., 2013, OpenCV Python Tutorials
  3. 'Grazing incidence...'; P. Müller-Buschbaum; Anal Bioanal Chem (2003) 376
  4. John C. Russ 2011, The image processing handbook, 6th ed.

Novelty, degree of difficulty and amount of assistance required

Programming heavy project. Previous python (or other object orientated language) experience would be useful though not necessary.

This is a novel analytical approach and with that comes both interest and some difficulty.

Rudi Winter can give background information on the materials and experimental techniques used in generating the data sets used in this project.

Project milestones and deliverables (including timescale)

milestoneto be completed by
Familiarity with python and external librariesend of October
Initial application of feature definitions to image dataChristmas
Reasonable dominant feature parametrisationend of February
Feature/Parameter analysisEaster