New deposition methods of halide perovskites are being developed with the aim of improving solar cell power conversion efficiency by controlling the physiochemical properties of the perovskite film. In the case of methylammonium lead iodide (MAPbI), deep level traps limit efficiency by participating in charge carrier recombination. Prior work has shown that the solar cell efficiency of MAPbI solar cells varied significantly with deposition method; specifically, efficiencies of 13.5 and 17.7% were observed for MAPbI processed with a one- and two-step method, respectively. However, the origin of the difference in efficiency remains unclear. In this study, we analyze the interplay between deep level traps and efficiency by simulating the photoexcited charge carrier pathway across solar cells processed via the one- and two-step method using finite-element drift-diffusion modeling. We determined that in the case of one-step processing, the traps propagate throughout the bulk, while for two-step, the traps congregate at the interface where the MAPbI was grown (mesoporous TiO). Composition and structural analysis are used to propose a plausible explanation as to why the difference in processing changes the spatial distribution of the traps.
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Folia Morphol (Warsz)
January 2025
Department of Anatomy, Faculty of Medicine, Jagiellonian University Medical College, Kraków, Poland.
Background: The rapid growth of aesthetic medicine has led to an increased demand for non-surgical cosmetic procedures in the frontal region of the face. However, alongside this rise in popularity, there is a growing awareness of the potential complications associated with these procedures especially connected with fillers. The intricate vascular anatomy of the forehead, specifically the supratrochlear (STA) and supraorbital (SOA) arteries, poses significant risks if not thoroughly understood.
View Article and Find Full Text PDFHum Reprod Open
November 2024
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?
Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.
What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.
EClinicalMedicine
December 2024
Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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View Article and Find Full Text PDFDrugs must accumulate at their target site to be effective, and inadequate uptake of drugs is a substantial barrier to the design of potent therapies. This is particularly true in the development of antibiotics, as bacteria possess numerous barriers to prevent chemical uptake. Designing compounds that circumvent bacterial barriers and accumulate to high levels in cells could dramatically improve the success rate of antibiotic candidates.
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