Research

Kinetic Monte Carlo modelling of Charge Transport in Organic Semiconductors and Photovoltaics

Simulation of current from an organic film with 100meV energetic disorder. Scale bar shows the amount of current gathered at each point (a.u.).

We develop and use Monte Carlo models to investigate the relationship between the energetic and morphological structure of a device and the performance of devices, and photovoltaics in particular.  In particular this allows us to make links between properties of organic materials on the nano-scale to bulk, measureable properties.  Across shows one example of this, where we plot the distribution of current emerging from a polymer film.  It can be seen that the current is extremely heterogeneous as a result of energetic disorder in the polymer.

The arrangement of course-grained P3HT chains in a solid film

Recently we have extended this work to use course-grained models to simulate how conjugated polymers arrange themselves in thin films, as shown opposite.  We can use these more detailed descriptions of molecular structure to  examine the links between molecular arrangement and charge transport as a function of regio-regularity, molecular weight and poly-dispersity – allowing greater precision of charge transport simulations than is possible using traditional KMC methods.

Key papers:

Groves C 2017.  Simulating charge transport in organic semiconductors and devices: a review Reports on Progress in Physics 80(2):026502. (review)

Jones ML, Huang DM, Charkrabarti B, Groves C 2016.  Relating Molecular Morphology to Charge Mobility in Semicrystalline Conjugated PolymersJournal of Chemical Physics C 120(8):4240-4250.

Ternary Blend Organic Photovoltaics

Organic solar cells are obliged to use at least two components to efficiently convert incoming photons into useful electrical charges.  Incorporating a third component offers the opportunity to engineer the performance of these devices further, either by optimising the charge generation efficiency, or slowing degradation of the device.  In our lab we investigate the use of such ternary blends to improve the efficiency and lifetime of organic solar cells.

Key paper:

Al-Busaidi Z, Pearson C, Groves C and Petty MC 2017. Enhanced lifetime of organic photovoltaic diodes utilizing a ternary blend including an insulating polymer, Solar Energy Materials and Solar Cells, 160: 101-106.

Noise Spectroscopy

Noise spectra of annealed and non-annealed P3HT:PCBM photovoltaic devices.

Measuring the fluctuation of electrical current can provide detailed information about the energetic and morphological structure of organic materials. For example, the image below presents the noise spectra of annealed and non-annealed P3HT:PCBM photovoltaic devices. The higher flicker noise level of the annealed device is attributed to increased heterogeneity in the current flowing though the device, related to a more heterogeneous P3HT:PCBM bulk heterojunction (BHJ) morphology.

Key papers: 

Kaku K, Williams AT, Mendis BG, Groves C 2015. Examining Charge Transport Networks in Organic Bulk Heterojunction Photovoltaic Diodes using 1/f Noise SpectroscopyJournal of Materials Chemistry C 3:6077-6085

Unconventional Computing

This collaborative project within the School of Engineering and Computing Sciences investigates the use of dynamic liquid-crystal/Carbon Nanotube blends as computers.  These materials can be trained autonomously using evolutionary algorithms to perform specific functions, such as data classification.  As with organic electronic devices, the electronic function of the blend depends upon the charge conduction network – only here the network is dynamic and malleable depending on the application.

Key papers:

Massey MK, Kotsialos A, Volpati D, Vissol-Gaudin E, Pearson C, Bowen L, Obara B, Zeze DA, Groves C, Petty MC 2016. Evolution of Electronic Circuits using Carbon Nanotube CompositesScientific Reports 6:32197

Vissol-Gaudin E, Kotsialos A, Groves C, Pearson C, Zeze DA, and Petty MC 2017. Computing Based on Material Training: Application to Binary Classification Problems, IEEE International Conference on Rebooting Computing (ICRC).