Spatial modeling of the 3D morphology of hybrid polymer-ZnO solar cells, based on electron tomography data

TL;DR

This paper introduces a stochastic spatial model for the 3D nanomorphology of hybrid polymer-ZnO solar cells, fitted to electron tomography data, to better understand and predict their efficiency in charge generation.

Contribution

It develops a novel 3D spatial stochastic model based on electron tomography data, combining stochastic geometry and spatial statistics for hybrid solar cell morphology.

Findings

Model accurately represents 3D morphology of hybrid solar cells.

Validated by comparing exciton quenching efficiency between real and simulated data.

Provides a tool for predicting and optimizing solar cell performance.

Abstract

A spatial stochastic model is developed which describes the 3D nanomorphology of composite materials, being blends of two different (organic and inorganic) solid phases. Such materials are used, for example, in photoactive layers of hybrid polymer zinc oxide solar cells. The model is based on ideas from stochastic geometry and spatial statistics. Its parameters are fitted to image data gained by electron tomography (ET), where adaptive thresholding and stochastic segmentation have been used to represent morphological features of the considered ET data by unions of overlapping spheres. Their midpoints are modeled by a stack of 2D point processes with a suitably chosen correlation structure, whereas a moving-average procedure is used to add the radii of spheres. The model is validated by comparing physically relevant characteristics of real and simulated data, like the efficiency of exciton quenching, which is important for the generation of charges and their transport toward the electrodes.