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Fractal solar cell array for enhanced energy production: applying rules underlying tree shape to photovoltaics.

ABSTRACT: Plants and photovoltaics share the same purpose as harvesting sunlight. Therefore, botanical studies could lead to new breakthroughs in photovoltaics. However, the basic mechanism of photosynthesis is different to semiconductor-based photovoltaics and the gap between photosynthesis and solar cells must be bridged before we can apply the botanical principles to photovoltaics. In this study, we analysed the role of the fractal structures found in plants in light harvesting based on a simplified model, rotated the structures by 90° and applied them to fractal-structured photovoltaic Si solar cell arrays. Adoption of botanically inspired fractal structures can result in solar cell arrays with omnidirectional properties, and in this case, yielded a 25% enhancement in electric energy production. The fractal structure used in this study was two-dimensional and symmetric; investigating and optimizing three-dimensional asymmetric fractal structures would further enhance the performance of photovoltaics. Furthermore, this study represents only the first step towards the development of a new type of photovoltaics based on botanical principles, and points to further aspects of botanical knowledge that could be exploited, in addition to plant fractal structures. For example, leaf anatomy, phyllotaxis and chloroplastic mechanisms could be applied to the design of new types of photovoltaics.

SUBMITTER: Sim YH

PROVIDER: S-EPMC7426039 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHost population structure is a key determinant of pathogen and infectious disease transmission patterns. Pathogen phylogenetic trees are useful tools to reveal the population structure underlying an epidemic. Determining whether a population is structured or not is useful in informing the type of phylogenetic methods to be used in a given study. We employ tree statistics derived from phylogenetic trees and machine learning classification techniques to reveal an underlying population structure.ResultsIn this paper, we simulate phylogenetic trees from both structured and non-structured host populations. We compute eight statistics for the simulated trees, which are: the number of cherries; Sackin, Colless and total cophenetic indices; ladder length; maximum depth; maximum width, and width-to-depth ratio. Based on the estimated tree statistics, we classify the simulated trees as from either a non-structured or a structured population using the decision tree (DT), K-nearest neighbor (KNN) and support vector machine (SVM). We incorporate the basic reproductive number ([Formula: see text]) in our tree simulation procedure. Sensitivity analysis is done to investigate whether the classifiers are robust to different choice of model parameters and to size of trees. Cross-validated results for area under the curve (AUC) for receiver operating characteristic (ROC) curves yield mean values of over 0.9 for most of the classification models.ConclusionsOur classification procedure distinguishes well between trees from structured and non-structured populations using the classifiers, the two-sample Kolmogorov-Smirnov, Cucconi and Podgor-Gastwirth tests and the box plots. SVM models were more robust to changes in model parameters and tree size compared to KNN and DT classifiers. Our classification procedure was applied to real -world data and the structured population was revealed with high accuracy of [Formula: see text] using SVM-polynomial classifier.

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Fractal solar cell array for enhanced energy production: applying rules underlying tree shape to photovoltaics.

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