Computational Studies on Novel Atomic Force Microscopy Techniques and Plasmonic-Enhanced Organic Photovoltaics

Abstract

In physical and life sciences, employing computational approaches to understand the nature of studied materials and subjects and to analyze immense amount of data is increasingly becoming more popular. Using existing methods and techniques is surprisingly easy, however these are not always well-suited for the problem at hand. Developing new methods specific to a problem requires a deep understanding of the problem, the underlying physics and relevant computational methods. Atomic Force Microscopy (AFM) is a unique tool for studying different properties of materials, such as conductivity, surface topography and forces. It also is a flexible tool for developing novel techniques that enable new studies in many fields of science. Although AFM is versatile, it can still be challenging to implement novel methods. Here, we describe in detail the hardware and the software implementation of simultaneous data acquisition and analysis in fast time-resolved electrostatic force microscopy (trEFM). Fast trEFM can measure nanosecond-scale local dynamics using widely available AFM hardware. We computationally and experimentally investigate the limits of trEFM and show that we can discriminate signal rise times with time constants as fast as 10 ns. Dynamic Force Spectroscopy is another method that is based on AFM and widely used in life sciences to measure mechanical properties of tissues, cells and DNA. We investigate computational methods to analyze force curves resulting from DNA pulling experiments in order to understand the unbinding kinetics of DNA under mechanical loading. We demonstrate that we can automate analysis of force curves using modern machine learning algorithms with an accuracy rate of 94%, which is approximately equal to human experimenters’ scores. Another area to which one can apply computational methods is modeling organic photovoltaic devices. Organic photovoltaics (OPVs) are one potential solution to growing demand for clean and inexpensive energy. Fabricating organic solar cells is a relatively easy process; the challenge is to make them as efficient as their inorganic counterparts. To increase the performance of an OPV device, one can use many different approaches, such as changing the device architecture or incorporating metal nanoparticles. Here, we study performance limits of plasmon-enhanced organic photovoltaics using a combination of computational modeling and experiment. We model modern low-bandgap donor polymer-fullerene blends with variety of colloidal silver nanoparticles to understand how sensitive the device performance is to nanoparticles. We show that it is possible to achieve an enhancement of 31% in short-circuit current density.