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Photo-accelerated oxidization enables fast production of a Spiro-OMeTAD hole transport layer in perovskite solar cells. This strategy delivers high power conversion efficiency and overcomes barriers to mass manufacturing. 

The tandem solar cell presents a potential solution to surpass the Shockley–Queisser limit observed in single-junction solar cells. However, creating a tandem device that is both cost-effective and highly efficient poses a significant challenge. In this study, we present proof of concept for a four-terminal (4T) tandem solar cell utilizing a wide bandgap (1.6–1.8 eV) perovskite top cell and a narrow bandgap (1.2 eV) antimony selenide (Sb2Se3) bottom cell. Using a one-dimensional (1D) solar cell capacitance simulator (SCAPS), our calculations indicate the feasibility of this architecture, projecting a simulated device performance of 23% for the perovskite/Sb2Se3 4T tandem device. To validate this, we fabricated two wide bandgap semitransparent perovskite cells with bandgaps of 1.6 eV and 1.77 eV, respectively. These were then mechanically stacked with a narrow bandgap antimony selenide (1.2 eV) to create a tandem structure, resulting in experimental efficiencies exceeding 15%. The obtained results demonstrate promising device performance, showcasing the potential of combining perovskite top cells with the emerging, earth-abundant antimony selenide thin film solar technology to enhance overall device efficiency. 

We present a novel self-supervised approach for hierarchical representation learning and segmentation of perceptual inputs in a streaming fashion. Our research addresses how to semantically group streaming inputs into chunks at various levels of a hierarchy while simultaneously learning, for each chunk, robust global representations throughout the domain. To achieve this, we propose STREAMER, an architecture that is trained layer-by-layer, adapting to the complexity of the input domain. In our approach, each layer is trained with two primary objectives: making accurate predictions into the future and providing necessary information to other levels for achieving the same objective. The event hierarchy is constructed by detecting prediction error peaks at different levels, where a detected boundary triggers a bottom-up information flow. At an event boundary, the encoded representation of inputs at one layer becomes the input to a higher-level layer. Additionally, we design a communication module that facilitates top-down and bottom-up exchange of information during the prediction process. Notably, our model is fully self-supervised and trained in a streaming manner, enabling a single pass on the training data. This means that the model encounters each input only once and does not store the data. We evaluate the performance of our model on the egocentric EPIC-KITCHENS dataset, specifically focusing on temporal event segmentation. Furthermore, we conduct event retrieval experiments using the learned representations to demonstrate the high quality of our video event representations. Illustration videos and code are available on our project page: https://ramymounir.com/publications/streamer 

Solar thermal techniques provide a promising method for the direct conversion of solar energy to thermal energy for applications, such as water desalination. To effectively realize the optimal potential of solar thermal conversion, it is desirable to construct an assembly with localized heating. Specifically, photoactive semiconducting nanoparticles, when utilized as independent light absorbers, have successfully demonstrated the ability to increase solar vapor efficiency. Additionally, bio-based fibers have shown low thermal conductive photocorrosion. In this work, cellulose acetate (CA) fibers were loaded with cadmium selenide (CdSe) nanoparticles to be employed for solar thermal conversion and then subsequently evaluated for both their resulting morphology and conversion potential and efficiency. Electrospinning was employed to fabricate the CdSe-loaded CA fibers by adjusting the CA/CdSe ratio for increased solar conversion efficiency. The microstructural and chemical composition of the CdSe-loaded CA fibers were characterized. Additionally, the optical sunlight absorption performance was evaluated, and it was demonstrated that the CdSe nanoparticles-loaded CA fibers have the potential to significantly improve solar energy absorption. The photothermal conversion under 1 sun (100 mW/cm2) demonstrated that the CdSe nanoparticles could increase the temperature up to 43 °C. The CdSe-loaded CA fibers were shown as a feasible and promising hybrid material for achieving efficient solar thermal conversion. 

Abstract Antimony selenide (Sb₂Se₃) has excellent directional optical and electronic behaviors due to its quasi‐1D nanoribbons structure. The photovoltaic performance of Sb₂Se₃solar cells largely depends on the orientation of the nanoribbons. It is desired to grow these Sb₂Se₃ribbons normal to the substrate to enhance photoexcited carrier transport. Therefore, it is necessary to develop a strategy for the vertical growth of Sb₂Se₃nanoribbons to achieve high‐efficiency solar cells. Since antimony sulfide (Sb₂S₃) and Sb₂Se₃are from the same space group (Pbnm) and have the same crystal structure, herein an ultrathin layer (≈20 nm) of Sb₂S₃has been used to assist the vertical growth of Sb₂Se₃nanoribbons to improve the overall efficiency of Sb₂Se₃solar cell. The Sb₂S₃thin layer deposited by the hydrothermal process helps the Sb₂Se₃ribbons grow normal to the substrate and increases the efficiency from 5.65% to 7.44% through the improvement of all solar cell parameters. This work is expected to open a new direction to tailor the Sb₂Se₃grain growth and further develop the Sb₂Se₃solar cell in the future.