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The electron transport layer (ETL) with excellent charge extraction and transport ability is one of the key components of high-performance perovskite solar cells (PSCs). SnO2 has been considered as a more promising ETL for the future commercialization of PSCs due to its excellent photoelectric properties and easy processing. Herein, we propose a facile and effective ETL modification strategy based on the incorporation of methylenediammonium dichloride (MDACl2) into the SnO2 precursor colloidal solution. The effects of MDACl2 incorporation on charge transport, defect passivation, perovskite crystallization, and PSC performance are systematically investigated. First, the surface defects of the SnO2 film are effectively passivated, resulting in the increased conductivity of the SnO2 film, which is conducive to electron extraction and transport. Second, the MDACl2 modification contributes to the formation of high-quality perovskite films with improved crystallinity and reduced defect density. Furthermore, a more suitable energy level alignment is achieved at the ETL/perovskite interface, which facilitates the charge transport due to the lower energy barrier. Consequently, the MDACl2-modified PSCs exhibit a champion efficiency of 22.30% compared with 19.62% of the control device, and the device stability is also significantly improved. [ABSTRACT FROM AUTHOR]

Non-traumatic subarachnoid hemorrhage (SAH) is a critical neurosurgical emergency with a high mortality rate, imposing a significant burden on both society and families. Accurate prediction of the risk of death within 7 days in SAH patients can provide valuable information for clinicians, enabling them to make better-informed medical decisions. In this study, we developed six machine learning models using the MIMIC III database and data collected at our institution. These models include Logistic Regression (LR), AdaBoosting (AB), Multilayer Perceptron (MLP), Bagging (BAG), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGB). The primary objective was to identify predictors of death within 7 days in SAH patients admitted to intensive care units. We employed univariate and multivariate logistic regression as well as Pearson correlation analysis to screen the clinical variables of the patients. The initially screened variables were then incorporated into the machine learning models, and the performance of these models was evaluated. Furthermore, we compared the performance differences among the six models and found that the MLP model exhibited the highest performance with an AUC of 0.913. In this study, we conducted risk factor analysis using Shapley values to identify the factors associated with death within 7 days in patients with SAH. The risk factors we identified include Gcsmotor, bicarbonate, wbc, spo2, heartrate, age, nely, glucose, aniongap, GCS, rbc, sysbp, sodium, and gcseys. To provide clinicians with a useful tool for assessing the risk of death within 7 days in SAH patients, we developed a web calculator based on the MLP machine learning model., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Ltd.)