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Metamaterials are complex structured mixed-material systems with tailored physical properties that have found applications in a variety of optical and electronic technologies. New methods for homogenizing the optical properties of metamaterials are of increasing importance, both to study their exotic properties and because the simulation of these complex structures is computationally expensive. We propose a method to extract a homogeneous refractive index and wave impedance for inhomogeneous materials. We examine effective medium models, where inhomogeneities are subwavelength, and equivalent models where features are larger. Homogenization is only physically justified in the former; however, it is still useful in the latter if only the reflection, transmission, and absorption are of interest. We introduce a resolution of the branching problem in the Nicolson-Ross-Weir method that involves starting from the branch of the complex logarithm beginning with the minimum absolute mean derivative and then enforcing continuity, and also determine an effective thickness. We demonstrate the proposed method on patterned PbS colloidal quantum dot films in the form of disks and birefringent gratings. We conclude that effective models are Kramers-Kronig compliant, whereas equivalent models may not be. This work illuminates the difference between the two types of models, allowing for better analysis and interpretation of the optical properties of complex metamaterials.

 

Numerous characterization techniques have been developed over the last century, which have advanced progress on the development of a variety of photovoltaic technologies. However, this multitude of techniques leads to increasing experimental costs and complexity. It would be useful to have an approach that does not require the time commitment or operation costs to directly learn and implement every new measurement technique. Herein, we explore several machine learning (ML) models that output complex materials parameters, such as electronic trap state density, solely using illuminated current-voltage curves. This greatly reduces both the complexity and cost of the characterization process. Current-voltage curves were chosen as the only input to our models because this type of measurement is relatively simple to perform and most photovoltaic research labs already collect this information on all devices. We compare several different ML network architectures, all of which are trained on experimental data from PbS colloidal quantum dot thin film solar cells. We predict values for underlying materials parameters and compare them to experimentally measured results. 

Colloidal Quantum Dot (CQD) thin films are ad- vantageous for solar energy generation because of their low- cost and size-tunable, solution-processable nature. However, their efficiency in solar cells is limited in part by the performance of the hole transport layer (HTL). Through Solar Cell Capacitance Simulations and Transfer Matrix Method calculations, we show that significant photogeneration occurs in the standard HTL of ethanedithiol-passivated lead sulfide CQDs which is a problem due to the sub-optimal carrier mobility in this material. We report new HTLs composed of chalcogenide-based materials to address these issues, and demonstrate an absolute power conversion efficiency improvement of 1.35% in the best device. 

The inverse design of photovoltaic 2D photonic crystals using machine learning will be presented. The technique bypasses calculation of photonic bandstructure in favor of directly computing designer-friendly properties such as spectral transmission. 

Colloidal quantum dots (CQDs) are of interest for photovoltaic applications such as flexible and multijunction solar cells, where solution processability and infrared absorption are crucial; however, current CQD solar cell performance is limited by the hole transport layers (HTLs) used in the cells. We report on a method to develop new HTLs for the highest-performing PbS CQD solar cell architecture by tuning the stoichiometry via sulfur infiltration of the p-type CQD HTL to increase its doping density and carrier mobility. Using SCAPS simulations, we predict that increased doping density and mobility should improve the performance of the solar cells. We show that sulfur doping of the current HTL is a facile and effective method to boost the performance of CQD photovoltaics. 

Colloidal quantum dots are a promising candidate material for thin film solar cells due to their size-dependent band gap tunability and solution-based processing flexibility. Spray-casting technology has the potential to reduce the strict environmental requirements associated with traditional fabrication procedures for colloidal quantum dot solar cells, potentially enabling installation-site solar cell fabrication. Here, we demonstrate spray-casting of silver nanowire electrodes and zinc oxide electron transport layers, demonstrate their use in colloidal quantum dot solar cells, analyze the existing challenges in current spray-casting procedures, and outline a path to producing fully spray-cast colloidal quantum dot solar cells. 

Spectral selectivity is of interest for many photovoltaic applications, such as in multijunction and transparent solar cells, where wavelength-selectivity of the photoactive material is necessary. We investigate using artificial photonic band engineering as a method for achieving spectral selectivity in an absorbing material such as PbS CQD thin films. Using FDTD simulations, we find that a CQD-based photonic crystal (CQD-PC) is able to maintain its photonic band structure, including the existence of a reduced photonic density of states, in the presence of weak material absorption. This shows that CQD-PCs are a promising material for photovoltaic applications that require spectral selectivity. 

The most common solution for achieving arbitrary spectral selectivity in optoelectronic devices is adding external filters. Here we propose using semiconductor thin film photonic crystals with relevant photonic bands that fall within the absorbing frequency range of the material for spectral selectivity. Optical simulations show that the in-plane photonic bands couple strongly to normal-incidence external fields, inducing tunable resonance features in the out-of-plane transmission and reflection spectra. Experimentally, we fabricate a proof-of-principle photonic structure with enhanced visible transparency, consisting of a self-assembled polystyrene bead array infiltrated with colloidal quantum dots, showing promise for multijunction and transparent photovoltaics.