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Perovskite solar cells (PSCs) have become a research hotspot since their dramatic increase in power conversion efficiency (PCE), surpassing 26% due to advances in cell engineering and interfacial layers. Within the last factor, hole transporting materials play a crucial role in enhancing device performance and stability. Among several molecular building blocks, BODIPYs are attractive for the design of novel hole transporting material (HTMs) due to their outstanding photophysical and charge transport properties easily tuned by synthetic modifications. Herein, the synthesis of five new BODIPY‐based HTMs PyBDP 1–5 are reported, functionalized at the meso‐ and α‐ positions with pyrenyl and arylamino units, respectively. The resulting compounds exhibit broad absorption in the visible region, remarkable thermal stability, narrow bandgaps, suitable energy levels, and good hole extraction capability, as subtracted from experimental and computational characterizations. The performance of the BODIPY derivatives as HTMs is evaluated in planar inverted (p‐i‐n) PSCs and compared to commonly used PTAA, resulting in highly efficient systems, reaching PCEs very close to that obtained with the reference polymer (21.51%). The incorporation of these BODIPY‐based HTMs result in an outstanding PCE of 20.37% for devices including PyBDP‐1 and 19.97% for devises containing PyBDP‐3, thus demonstrating that BODIPY derivatives are a promising alternative to obtain simple and efficient organic HTMs. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols have important impacts on global radiative forcing, air pollution, and human health. This study investigated the optical and physical properties of aerosol layers over Australia from 2007 to 2019 using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 aerosol products. Australia was divided into three sub-regions (western highlands, central plains, and eastern ranges). Interannual and seasonal optical property variations in aerosol layers in the three sub-regions were analyzed and compared. Results showed that annual mean values of AODL (lowest aerosol layer AOD) and AODT (total AOD of all aerosol layers) were always higher in the eastern ranges region than the other two regions from 2007 to 2019. The reason could be that Australian population was predominantly located in the eastern ranges region, where more human activities could bring significant aerosol loadings. BL (base height of the lowest aerosol layer), HL (top height of the lowest aerosol layer), and HH (top height of the highest aerosol layer) all showed trends of "western highlands > eastern mountains > central plains", indicating that the higher the elevation, the higher the BL, HL, and HH. TL (thickness of the lowest aerosol layer) was higher during the day than at night, which might account for increased diurnal atmospheric convection and nocturnal aerosol deposition. DRL (depolarization ratio of the lowest aerosol layer) was higher in the western highlands and central plains than the eastern mountains, probably because these two regions have large deserts with more irregularly shaped dust aerosols. CRL (color ratio of the lowest aerosol layer) had slightly higher values in the eastern ranges than the other two regions, probably due to the wet climate of the eastern ranges, where aerosols were more hygroscopic and had larger particle sizes. This study can provide technical support for the control and management of regional air pollutants. [ABSTRACT FROM AUTHOR]

Introduction: The elevated mortality and hospitalization rates among hemodialysis (HD) patients underscore the necessity for the development of accurate predictive tools. This study developed two models for predicting all-cause mortality and time to death-one using a comprehensive database and another simpler model based on demographic and clinical data without laboratory tests., Method: A retrospective cohort study was conducted from January 2017 to June 2023. Two models were created: Model A with 85 variables and Model B with 22 variables. We assessed the models using random forest (RF), support vector machine, and logistic regression, comparing their performance via the AU-ROC. The RF regression model was used to predict time to death. To identify the most relevant factors for prediction, the Shapley value method was used., Results: Among 359 HD patients, the RF model provided the most reliable prediction. The optimized Model A showed an AU-ROC of 0.86 ± 0.07, a sensitivity of 0.86, and a specificity of 0.75 for predicting all-cause mortality. It also had an R 2 of 0.59 for predicting time to death. The optimized Model B had an AU-ROC of 0.80 ± 0.06, a sensitivity of 0.81, and a specificity of 0.70 for predicting all-cause mortality. In addition, it had an R 2 of 0.81 for predicting time to death., Conclusion: Two new interpretable clinical tools have been proposed to predict all-cause mortality and time to death in HD patients using machine learning models. The minimal and readily accessible data on which Model B is based makes it a valuable tool for integrating into clinical decision-making processes., (© 2024 The Author(s). Therapeutic Apheresis and Dialysis published by John Wiley & Sons Australia, Ltd on behalf of International Society for Apheresis and Japanese Society for Apheresis.)

Competing Interests: The authors declare no conflict of interest.