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This review highlights recent advancements in the zinc oxide electron transport layer for PbS colloidal quantum dot solar cells. 

Colloidal quantum dots (CQDs) are promising materials for photovoltaic applications due to their solution processibility and size-dependent band gap tunability. The electron transport layer (ETL) is an important component of PbS CQD solar cells, and the quality of the zinc oxide nanoparticle (ZnO NP) ETL film significantly impacts both the power conversion efficiency (PCE) and fabrication yield of CQD solar cells. We report on multiple methods to improve the quality of ZnO NP ETL films and demonstrate increased PCE and device yield in standard CQD solar cells employing optimized ZnO NP films. We also discuss the application of these methods in an inverted CQD solar cell architecture. 

Colloidal quantum dots are a promising candidate material for solar energy generation because of their band gap tunability and solution-based processing flexibility. However, conventional colloidal quantum dot solar cell fabrication techniques are still limited by their lack of scalability, environment conditions, and difficult installation scenarios. Here, we develop spray-casting manufacturing methods for fabricating thin film solar cells, discuss the trade-off between conductivity and transmittance in transparent contact materials, and demonstrate the feasibility of spray-casting colloidal quantum dot layers. This work on flexible manufacturing methods paves the way for installing solar energy devices in a variety of novel scenarios. 

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. 

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. 

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.