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Angelo Bozzola

Researcher at Istituto Italiano di Tecnologia

Publications -  44
Citations -  1171

Angelo Bozzola is an academic researcher from Istituto Italiano di Tecnologia. The author has contributed to research in topics: Plasmonic solar cell & Crystalline silicon. The author has an hindex of 15, co-authored 44 publications receiving 918 citations. Previous affiliations of Angelo Bozzola include University of Pavia & Vanderbilt University.

Papers
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Silicon solar cells: toward the efficiency limits

TL;DR: The photovoltaic conversion of solar energy starts to give an appreciable contribution to power generation in many countries, with more than 90% of the global PV market relying on solar cells base.
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Photonic light-trapping versus Lambertian limits in thin film silicon solar cells with 1D and 2D periodic patterns.

TL;DR: The main physical effects of patterning are focused on, namely a reduction of reflection losses, diffraction of light in air or inside the cell, and coupling of incident radiation into quasi-guided optical modes of the structure, which is characteristic of photonic light-trapping.
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Optimising apodized grating couplers in a pure SOI platform to -0.5 dB coupling efficiency.

TL;DR: These new apodized designs are the first pure SOI couplers compatible with deep-UV lithography to offer better than -1 dB insertion losses, and they are compatible with the multi-project wafer runs already offered by Si-Photonics foundries.
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Broadband light trapping with disordered photonic structures in thin‐film silicon solar cells

TL;DR: In this paper, the role of the Fourier transform of the photonic pattern in light trapping is investigated, and the optimal correlation between size and position disorder is found, which can be used to optimize the disorder in a more effective way, using a single parameter.
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Hybrid plasmonic–photonic whispering gallery mode resonators for sensing: a critical review

TL;DR: A comparative analysis of the different types of optical sensors specifically designed for bulk refractive index sensing, molecular binding and single object detection identifies the most promising hybrid architectures.