Open AccessNETWORKS OF NANOPARTICLES IN ORGANIC – INORGANIC COMPOSITES: ALGORITHMIC EXTRACTION AND STATISTICAL ANALYSIS
Author(s) -
Ralf Thiedmann,
Aaron Spettl,
Ole Stenzel,
Thomas Zeibig,
James C. Hindson,
Zineb Saghi,
Neil C. Greenham,
Paul A. Midgley,
Volker Schmidt
Publication year - 2012
Publication title -
image analysis and stereology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.237
H-Index - 27
eISSN - 1854-5165
pISSN - 1580-3139
DOI - 10.5566/ias.v31.p27-42
Subject(s) - materials science , nanoparticle , grayscale , flexibility (engineering) , polymer solar cell , process engineering , solar cell , renewable energy , nanotechnology , computer science , polymer , composite material , image (mathematics) , artificial intelligence , optoelectronics , engineering , mathematics , electrical engineering , statistics
The rising global demand in energy and the limited resources in fossil fuels require new technologies in renewable energies like solar cells. Silicon solar cells offer a good efficiency but suffer from high production costs. A promising alternative are polymer solar cells, due to potentially low production costs and high flexibility of the panels. In this paper, the nanostructure of organic–inorganic composites is investigated, which can be used as photoactive layers in hybrid–polymer solar cells. These materials consist of a polymeric (OC1C10-PPV) phase with CdSe nanoparticles embedded therein. On the basis of 3D image data with high spatial resolution, gained by electron tomography, an algorithm is developed to automatically extract the CdSe nanoparticles from grayscale images, where we assume them as spheres. The algorithm is based on a modified version of the Hough transform, where a watershed algorithm is used to separate the image data into basins such that each basin contains exactly one nanoparticle. After their extraction, neighboring nanoparticles are connected to form a 3D network that is related to the transport of electrons in polymer solar cells. A detailed statistical analysis of the CdSe network morphology is accomplished, which allows deeper insight into the hopping percolation pathways of electrons