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Abstract The morphology, chemical composition, and electronic uniformity of thin‐film solution‐processed optoelectronics are believed to greatly affect device performance. Although scanning probe microscopies can address variations on the micrometer scale, the field of view is still limited to well under the typical device area, as well as the size of extrinsic defects introduced during fabrication. Herein, a micrometer‐resolution 2D characterization method with millimeter‐scale field of view is demonstrated, which simultaneously collects photoluminescence spectra, photocurrent transients, and photovoltage transients. This high‐resolution morphology mapping is used to quantify the distribution and strength of the local optoelectronic property variations in colloidal quantum dot solar cells due to film defects, physical damage, and contaminants across nearly the entire test device area, and the extent to which these variations account for overall performance losses. It is found that macroscopic defects have effects that are confined to their localized areas, rarely prove fatal for device performance, and are largely not responsible for device shunting. Moreover, quantitative analysis based on statistical partitioning methods of such data is used to show how defect identification can be automated while identifying variations in underlying properties such as mobilities and recombination strengths and the mechanisms by which they govern device behavior. 

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. 

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.