Brittany R. Hoard

15.7% Efficient 10‐μm‐Thick Crystalline Silicon Solar Cells Using Periodic Nanostructures

Only ten micrometer thick crystalline silicon solar cells deliver a short-circuit current of 34.5 mA cm(-2) and power conversion efficiency of 15.7%. The record performance for a crystalline silicon solar cell of such thinness is enabled by an advanced light-trapping design incorporating a 2D inverted pyramid photonic crystal and a rear dielectric/reflector stack.

Influence of model resolution on geometric simulations of antibody aggregation

It is estimated that allergies afflict up to 40% of the worlds population. A primary mediator for allergies is the aggregation of antigens and IgE antibodies bound to cell-surface receptors, FceRI. Antibody/antigen aggregate formation causes stimulation of mast cells and basophils, initiating cellular degranulation and releasing immune mediators which produce an allergic or anaphylactic response. Understanding the shape and structure of these aggregates can provide critical insights into the allergic response. We have previously developed methods to geometrically model, simulate and analyze antibody aggregation inspired by rigid body robotic motion simulations. Our technique handles the large size and number of molecules involved in aggregation, providing an advantage over traditional simulations such as molecular dynamics (MD) and coarse-grained energetic models. In this paper, we study the impact of model resolution on simulations of geometric structures using both our previously developed Monte Carlo simulation and a novel application of rule-based modeling. These methods complement each other, the former providing explicit geometric detail and the latter providing a generic representation where multiple resolutions can be captured. Our exploration is focused on two antigens, a man-made antigen with three binding sites, DF3, and a common shrimp allergen (antigen), Pen a 1. We find that impact of resolution is minimal for DF3, a small globular antigen, but has a larger impact on Pen a 1, a rod-shaped molecule. The volume reduction caused by the loss in resolution allows more binding site accessibility, which can be quantified using a rule-based model with implicit geometric input. Clustering analysis of our simulation shows good correlation when compared with available experimental results. Moreover, collisions in all-atom reconstructions are negligible, at around 0.2% at 90% reduction.

Symmetry-breaking nanostructures on crystalline silicon for enhanced light trapping in thin film solar cells.

We introduce a new approach to systematically break the symmetry in periodic nanostructures on a crystalline silicon surface. Our focus is inverted nanopyramid arrays with a prescribed symmetry. The arrangement and symmetry of nanopyramids are determined by etch mask design and its rotation with respect to the [110] orientation of the Si(001) substrate. This approach eliminates the need for using expensive off-cut silicon wafers. We also make use of low-cost, manufacturable, wet etching steps to fabricate the nanopyramids. Our experiment and computational modeling demonstrate that the symmetry breaking can increase the photovoltaic efficiency in thin-film silicon solar cells. For a 10-micron-thick active layer, the efficiency improves from 27.0 to 27.9% by enhanced light trapping over the broad sunlight spectrum. Our computation further reveals that this improvement would increase from 28.1 to 30.0% in the case of a 20-micron-thick active layer, when the unetched area between nanopyramids is minimized with over-etching. In addition to the immediate benefit to solar photovoltaics, our method of symmetry breaking provides a useful experimental platform to broadly study the effect of symmetry breaking on spectrally tuned light absorption and emission.

Extending rule-based methods to model molecular geometry

Computational modeling is an important tool for the study of complex biochemical processes associated with cell signaling networks. However, it is challenging to simulate processes that involve hundreds of large molecules due to the high computational cost of such simulations. Rule-based modeling is a computational method that can be used to model these processes with reasonably low computational cost, but traditional rule-based modeling approaches do not include details of molecular geometry. The incorporation of molecular geometry into biochemical models can more accurately capture details of these processes, and may lead to insights into how geometry affects the products that form. Furthermore, geometric rule-based modeling can be used to complement other computational methods that explicitly represent molecular geometry in order to quantify binding site accessibility and steric effects. In this work, we propose a novel implementation of rulebased modeling that encodes details of molecular geometry into the rules and the binding rate constant associated with each rule. We demonstrate how the set of rules is constructed according to the curvature of the molecule. We then perform a study of antigen-antibody aggregation using our proposed method. We first simulate the binding of IgE antibodies bound to cell surface receptors Fc RI to various binding regions of the shrimp allergen Pen a 1 using a previously developed 3D rigid-body Monte Carlo simulation, and we analyze the distribution of the sizes of the aggregates that form during the simulation. Then, using our novel rule-based approach, we optimize a rule-based model according to the geometry of the Pen a 1 molecule and the data from the Monte Carlo simulation. In particular, we use the distances between the binding regions of the Pen a 1 molecule to optimize the rules and associated binding rate constants. We perform this procedure for three molecular conformations of Pen a 1 and analyze the impact of conformation on the aggregate size distribution and the optimal rule-based model. We find that the optimized rule-based models provide information about the average steric hindrance between binding regions and the probability that IgE-Fc RI receptor complexes will bind to these regions. In addition, the optimized rule-based models provide a means of quantifying the variation in aggregate size distribution that results from differences in molecular geometry.

Application of fuzzy inference systems to parameter optimization of a biochemical rule-based model

Our main contribution is an efficient machine learning approach to fitting parameters of a biological model. We study the binding of the shrimp protein Pen a 1 with antibody-receptor complexes because this process is important in understanding the allergic response. Previously, we developed a BioNetGen model that simulates this process. We previously developed a method for encoding steric effects via the optimization of two parameters: the cutoff distance and the rule rate. We optimized these two parameters by fitting the output to that generated by a 3D robotics-inspired Monte Carlo simulation that explicitly represents molecular geometry. In this work, we aim to optimize the parameters for our BioNetGen model using an efficient method: an adaptive-network-based fuzzy inference system implemented in MAT-LAB. We want to develop fuzzy systems that can accurately predict the rule binding rate and cutoff distance given a residual-sum-of-squares value or a probability distribution. We construct the fuzzy systems using fuzzy c-means clustering with existing data from BioNetGen model parameter scans as the training data. We create and test fuzzy systems with various input data and number of clusters, and analyze their performance with regard to the effective optimization of our rule-based model. We find that the fuzzy system that uses a residual-sum-of-squares value as the input value performs acceptably well. However, the performance of the fuzzy systems that use probabilities as their input values perform inconsistently in our tests. The results of this study suggest that the system that uses a residual-sum-of-squares value as the input value could potentially be used to find an adequate fit for our biochemical model. However, the systems that use probabilities as their input values need further development to improve the consistency and reliability of their output. Testing more values for other clustering parameters other than the number of clusters may accomplish this. Further research could also include similar studies using other training or clustering algorithms. This methodology could be modified for use with fitting other biological models.

Light trapping enhancement in thin film solar cells by breaking symmetry in nanostructures

We experimentally demonstrate highly efficient light-trapping structures that is achieved by breaking the symmetry in inverted nanopyramids on c-Si. The fabrication of these structures is cost-effective and scalable. Our optical measurement for the structures on 10-μm-thick c-Si cells shows the Shockley-Queisser efficiency of 27.9%. We further fabricate plasmonic metal structures on the symmetry-breaking inverted nanopyramids. When a light-absorbing polymer layer is deposited on top of the plasmonic structures, we observe that the plasmonic light trapping exceeds the Lambertian limit. The remarkable light trapping increases the short circuit current by 2.5 times. We expect the symmetry-breaking structures to be broadly applicable to thin-film solar cells.

Extending rule-based methods to model molecular geometry and 3D model resolution

BackgroundComputational modeling is an important tool for the study of complex biochemical processes associated with cell signaling networks. However, it is challenging to simulate processes that involve hundreds of large molecules due to the high computational cost of such simulations. Rule-based modeling is a method that can be used to simulate these processes with reasonably low computational cost, but traditional rule-based modeling approaches do not include details of molecular geometry. The incorporation of geometry into biochemical models can more accurately capture details of these processes, and may lead to insights into how geometry affects the products that form. Furthermore, geometric rule-based modeling can be used to complement other computational methods that explicitly represent molecular geometry in order to quantify binding site accessibility and steric effects.ResultsWe propose a novel implementation of rule-based modeling that encodes details of molecular geometry into the rules and binding rates. We demonstrate how rules are constructed according to the molecular curvature. We then perform a study of antigen-antibody aggregation using our proposed method. We simulate the binding of antibody complexes to binding regions of the shrimp allergen Pen a 1 using a previously developed 3D rigid-body Monte Carlo simulation, and we analyze the aggregate sizes. Then, using our novel approach, we optimize a rule-based model according to the geometry of the Pen a 1 molecule and the data from the Monte Carlo simulation. We use the distances between the binding regions of Pen a 1 to optimize the rules and binding rates. We perform this procedure for multiple conformations of Pen a 1 and analyze the impact of conformation and resolution on the optimal rule-based model.ConclusionsWe find that the optimized rule-based models provide information about the average steric hindrance between binding regions and the probability that antibodies will bind to these regions. These optimized models quantify the variation in aggregate size that results from differences in molecular geometry and from model resolution.

Symmetry-breaking nanostructures for enhanced light-trapping in thin film solar cells

We introduce a manufacturable method to break the symmetry in inverted nanopyramids on c-Si. This method broadly enhances light trapping and would increase the efficiency from 25 to 26.4% for thick c-Si cells. We further use the nanopyramids as a template to deposit plasmonic metal structures and demonstrate enhanced light absorption in organic solar cells. The enhancement exceeds 100% in some cases by concentrating the plasmonic bands tuned to the polymer absorption. The result agrees well with our measured surface plasmon polariton band structures. We expect our approach to be broadly applicable to thin-film solar cells.