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Two-dimensional transition metal dichalcogenides are of growing interest for flexible optoelectronics and power applications, due to their tunable optical properties, lightweight nature, and mechanical pliability. However, their thin nature inherently limits their optical absorption and, therefore, efficiency. Here, we propose a few-layer WSe₂optoelectronic device that achieves near perfect absorption through a combination of optical effects. The WSe₂can be scalably grown below an Al₂O₃superstrate. Our device includes a corrugated back reflector, modeled as a plasmonic nanowire array. We investigate the entire range of widths of the corrugations in the back reflector, including the edge cases of a simple back mirror (width equal to period) and a Fabry-Perot cavity (zero width). We demonstrate the zero-mode enhancement arising from the back reflector, the weakly coupled enhancement arising from the Fabry-Perot cavity, and the strongly coupled enhancement arising from the localized surface plasmon resonance of the nanowires, explain the physical nature of the spectral peaks, and theoretically model the hybridization of these phenomena using a coupled oscillator model. Our champion device exhibits 82% peak absorptance in the WSe₂alone, 92% in the WSe₂plus nanowires, and 98% total absorptance. Thus, we achieve a near-perfect absorber in which most of the absorption is in the few-layer WSe₂, with a desirable device framework for integration with scalable growth of the WSe₂, thereby making our designs applicable to a range of practical optoelectronic devices. 

We present a method for designing spectrally- selective optoelectronic films with a finite absorption bandwidth. We demonstrate the process by designing a film composed of lead sulfide colloidal quantum dots (PbS-CQDs). Designs incorporate the patterning of absorbing PbS-CQD films into photonic crystal- like slabs which couple incident light into leaky modes within the plane of the absorbing films, modulating the absorption spectrum. Computational times required to calculate optical spectra are drastically decreased by implementing the Fourier Modal Method. Furthermore, a supervised machine-learning-based inverse design methodology is presented which allows tailoring of the PbS-CQD film optical properties for use in a variety of photovoltaic applications, such as tandem cells in which spectral tailoring can enable current-matching flexibility. 

Lead Sulfide (PbS) colloidal quantum dots (CQDs) are promising materials for flexible and wearable photovoltaic devices and technologies due to their low cost, solution processibility and bandgap tunability with quantum dot size. However, PbS CQD solar cells have limitations on performance efficiency due to charge transport losses in the CQD layers and hole transport layer (HTL). This study pursues two promising techniques in parallel to address these challenges. Solution-phase annealing of the absorbing PbS-PbX2 (X = Br, I) layer can reduce charge transport losses by removing oleic acid and parasitic hydroxyl ligands. Additionally, optoelectronic simulations are used to show that HTL performance can be improved by the addition of a 2D transition metal dichalcogenide (TMD) layer to the PbS CQD-based HTL. We use solution-phase exfoliation to produce and incorporate 2D WSe2 nanoflakes into the HTL. We report a power conversion efficiency (PCE) increase of up to 3.4% for the solution-phase-annealed devices and up to 1% for the 2D WSe2 HTL augmented devices. A combination of these two techniques should result in high-performing PbS CQD solar cells, paving the way for further advancements in flexible photovoltaics. 

Recent advances in machine learning (ML) have enabled predictive programs for photovoltaic characterization, optimization, and materials discovery. Despite these advances, the standard photovoltaic materials development workflow still involves manually performing multiple characterization techniques on every new device, requiring significant time and expenditures. One barrier to ML implementation is that most models reported to date are trained on computer simulated data, due to the difficulty in experimentally collecting the massive data sets needed for model training, limiting the ability to assess the limitations and validity of these methods, as well as to access new potential physical mechanisms absent in simulations. Herein, several neural networks trained on experimental data from PbS colloidal quantum dot thin‐film solar cells are introduced. These models predict multiple, complex materials properties, including carrier mobility, relative photoluminescence intensity, and electronic trap‐state density, from a single, simple measurement: illuminated current–voltage curves. The measurement system considers the spatial distribution of the materials parameters to gather and predict large amounts of data by treating an inhomogeneous device as a series of thousands of micro‐devices, a novel feature compared to existing solutions. This model can be extended to other materials and devices, accelerating development times for new optoelectronic technologies.