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.

Application of adaptive-network-based fuzzy inference systems to the parameter optimization of a biochemical rule-based model.

In this study, the binding of allergens to antibody-receptor complexes was investigated. This process is important in understanding the allergic response. A BioNetGen model that simulates this process, combined with a novel method for encoding steric effects via the optimization of the cutoff distance and the rule binding rate, was previously developed. These parameters were optimized by fitting the model output to the output of a 3D simulation that explicitly represents molecular geometry. In this work, the parameters for the BioNetGen model were optimized using an adaptive-network-based fuzzy inference system in order to predict the rule rate and cutoff distance given a residual-sum-of-squares value or a probability distribution. The fuzzy systems were constructed using fuzzy c-means clustering with existing data from BioNetGen model parameter scans used as the training data. Fuzzy systems with various input data and number of clusters were created and tested. Their performance was analyzed with regard to the effective optimization of the rule-based model. The study found 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 performed inconsistently in the tests and need further development. This methodology could potentially be modified for use in fitting other biological models.

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

BACKGROUND: 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 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. RESULTS: We 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. CONCLUSIONS: We 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.

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.