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Register a new userNanowire array photovoltaics: Radial disorder versus design for optimal efficiency
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Added by
Bj\\\"orn C. P. Sturmberg
Publication date
2014
fields
Physics
and research's language is
English
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Solar cell designs based on disordered nanostructures tend to have higher efficiencies than structures with uniform absorbers, though the reason is poorly understood. To resolve this, we use a semi-analytic approach to determine the physical mechanism leading to enhanced efficiency in arrays containing nanowires with a variety of radii. We use our findings to systematically design arrays that outperform randomly composed structures. An ultimate efficiency of 23.75% is achieved with an array containing 30% silicon, an increase of almost 10% over a homogeneous film of equal thickness.
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Semi-transparent photovoltaics (ST-PV) provide smart spatial solutions to integrate solar cells into already-built areas. Here, we study the potential of semiconductor nanowires (NWs) as promising ST-PV. We perform FDTD simulations for different PV materials in a wide range of array geometries, from which we compute PV performance next to perceived appearance. Surprisingly we find an unusual compromise between photocurrent and transmittance as a function of NW diameter that enables NW-based PV to outperform theoretical limits of non-wavelength selective ST-PV. We theoretically and experimentally demonstrate the robustness of NW arrays to different illumination conditions. We provide the origin behind the outperforming NW array geometries, which is crucial for designing NW-based ST-PV systems based on specific needs.
While the basic principles and limitations of conventional solar cells are well understood, relatively little attention has gone toward maximizing the potential efficiency of photovoltaic devices based on shift currents. In this work, we outline simple design principles for the optimization of shift currents for frequencies near the band gap, derived from the analysis of a general effective model. The use of a novel sum rule allows us to express the band edge shift current in terms of a few model parameters and to show it depends explicitly on wavefunctions via Berry connections in addition to standard band structure. We use our approach to identify two new classes of shift current photovoltaics, ferroelectric polymer films and single-layer orthorhombic monochalcogenides such as GeS. We introduce tight-binding models for these systems, and show that they exhibit the largest shift current responsivities at the band edge reported so far. Moreover, exploring the parameter space of these models we find photoresponsivities that can exceed $100$ mA/W. Our results show how the study of the shift current via effective models allows one to improve the possible efficiency of devices based on this mechanism and better grasp their potential to compete with conventional solar cells.
Majorana zero modes in a superconductor-semiconductor nanowire have been extensively studied during the past decade. Disorder remains a serious problem, preventing the definitive observation of topological Majorana bound states. Thus, it is worthwhile to revisit the simple model, the Kitaev chain, and study the effects of weak and strong disorder on the Kitaev chain. By comparing the role of disorder in a Kitaev chain with that in a nanowire, we find that disorder affects both systems but in a nonuniversal manner. In general, disorder has a much stronger effect on the nanowire than the Kitaev chain, particularly for weak to intermediate disorder. For strong disorder, both the Kitaev chain and nanowire manifest random featureless behavior due to universal Anderson localization. Only the vanishing and strong disorder regimes are thus universal, manifesting respectively topological superconductivity and Anderson localization, but the experimentally relevant intermediate disorder regime is nonuniversal with the details dependent on the disorder realization in the system.
Nanowires (NWs) with a unique one-dimensional structure can monolithically integrate high-quality III-V semiconductors onto Si platform, which is highly promising to build lasers for Si photonics. However, the lasing from vertically-standing NWs on silicon is much more difficult to achieve compared with NWs broken off from substrates, causing significant challenges in the integration. Here, the challenge of achieving vertically-standing NW lasers is systematically analyzed. The poor optical reflectivity at the NW/Si interface results severe optical field leakage to the substrate, and the commonly used SiO2 or Si2N3 dielectric mask at the interface can only improve it to ~10%, which is the major obstacle for achieving low-threshold lasing. A NW super lattice distributed Bragg reflector is therefore proposed, which is able to greatly improve the reflectivity to >97%. This study provides a highly-feasible method to greatly improve the performance of vertically-standing NW lasers, which can boost the rapid development of Si photonics.
With the emergence of new photonic and plasmonic materials with optimized properties as well as advanced nanofabrication techniques, nanophotonic devices are now capable of providing solutions to global challenges in energy conversion, information technologies, chemical/biological sensing, space exploration, quantum computing, and secure communication. Addressing grand challenges poses inherently complex, multi-disciplinary problems with a manifold of stringent constraints in conjunction with the required systems performance. Conventional optimization techniques have long been utilized as powerful tools to address multi-constrained design tasks. One example is so-called topology optimization that has emerged as a highly successful architect for the advanced design of non-intuitive photonic structures. Despite many advantages, this technique requires substantial computational resources and thus has very limited applicability to highly constrained optimization problems within high-dimensions parametric space. In our approach, we merge the topology optimization method with machine learning algorithms such as adversarial autoencoders and show substantial improvement of the optimization process by providing unparalleled control of the compact design space representations. By enabling efficient, global optimization searches within complex landscapes, the proposed compact hyperparametric representations could become crucial for multi-constrained problems. The proposed approach could enable a much broader scope of the optimal designs and data-driven materials synthesis that goes beyond photonic and optoelectronic applications.
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