Computational Studies of Organic Electronic Materials
The sun produces 1020 W m-2 of energy per day; that is, in one hour, the earth receives enough energy to power the earth for an entire year. This energy can be harvested for a range of processes, including water splitting, photocatalysis, and electricity. Photovoltaic technologies, in particular, harness the energy of the sun to produce electrical energy. Organic solar cells, or organic photovoltaics (OPVs), hold advantages over inorganic cells in scalability, range of functionalization, and potential in flexible applications. While the power conversion efficiency (PCE) of this relatively new type of solar cell has made tremendous progress over the last decade, OPVs must continue to improve over 15% PCE in order to become a viable competitor to inorganic cells.
Several factors in the active layer (donor and acceptor layers) of a solar cell can lead to low efficiency, including low carrier mobility, inadequate absorption of the electromagnetic spectrum, and disordered morphology. Therefore, control of morphology in the active layer and tuning of electronic levels of the donors and acceptors in organic photovoltaics are important parameters that need to be controlled for favorable device performance. One approach to improving efficiency is designing molecules that pack closely together to allow for efficient charge transport. These types of systems offer significant possibilities for controlled morphologies, high charge transport, and high efficiencies in solar cells. Towards these types of materials, this work aims to:
1. Understand the effect of torsional behavior in organic materials on the morphology of the active layer to guide the design of materials with low disorder.
2. Determine the effect of crystal packing on charge transport of organic small-molecules and oligomers to lend insight into mechanisms that govern high mobility, and
3. Characterize optical properties of trimeric oligomers to lend insight into potential electronic applications.
Motivated by the potential of new organic photovoltaic materials, this thesis begins with a focus on using computational methods to understand systems of relevance to organic electronics (Chapters 1-7). Early projects aim to use an iterative computational-experimental scheme to understand morphology and charge transport as relevant in organic electronic devices (Chapters 1-3). Molecular properties are calculated and correlated to bulk properties observed experimentally to derive design principles for new molecules that would give rise to interesting packing in solid state. Excited state calculations are used to understand the underlying properties that give rise to optical transitions and to predict new molecules that would absorb strongly in the visible light region (Chapters 4). Mechanistic studies of the degradation pathways of small molecules will also be discussed, and functional handles that would lead to more stable structures are identified (Chapter 5). Calculations are employed to understand the transformation of polydiacetylene polymers to graphene nanoribbons, which lends insight into tuning this reaction for other N-doped materials (Chapter 6). Later in the thesis, the focus diverges to the use of computations to understand the reactivity of a class of ruthenium-based olefin metathesis catalysts towards the design of a stable, E-selective catalyst (Chapter 7-8).
Various methods are employed in the chapters described in this dissertation. Density functional theory is used to characterize ground state geometry, conformational analysis, transition state geometry, molecular orbitals, reorganization energies. Time-dependent density functional theory is used to characterize excited state properties, including bandgap, absorption, emission, excited state geometry, charge transfer properties. ZINDO is used for other excited state properties such as charge transfer, electronic coupling. Molecular dynamics is used for time-scale properties, including electronic disorder, energetic disorder, ordered and disordered morphology. Kinetic Monte Carlo as implemented in the VOTCA program is used for charge transport calculations.