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Organic–inorganic halide perovskites (OIHPs) have emerged as one of the most efficient photovoltaic materials due to their superior properties. However, improving their stability remains a key challenge. Herein, we investigate the thermal decomposition properties of OIHP FAxMA1−xPbI3 with mixed cations of formamidinium (FA) and methylammonium (MA). Using thermogravimetric analysis together with Fourier transform infrared spectroscopy, we identify and monitor the gaseous decomposition products as a function of temperature and cation composition. Thermal decomposition products of both MA and FA cations were observed at all stages of the thermal decomposition process, contrary to previous expectations. The yield, release sequence and kinetics of the organic gaseous products were found to depend strongly on the ratio between FA and MA cations. Furthermore, cesium ion doping was investigated as a potential strategy to improve the thermal stability of mixed cation perovskites. These results provide new insights into the effect of cation mixing on perovskite stability, suggesting that optimizing the cation ratios and decomposition pathways can guide approaches to boost the stability and performance of mixed cation perovskites. [ABSTRACT FROM AUTHOR]

Background Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children. Methods and findings Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE [less than or equal to] -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records ([greater than or equal to]3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered. Conclusions To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia., Author(s): Haotian Lin 1, Erping Long 1, Xiaohu Ding 1, Hongxing Diao 1, Zicong Chen 2, Runzhong Liu 2, Jialing Huang 2, Jingheng Cai 3, Shuangjuan Xu 3, Xiayin Zhang [...]

Tosyloxy- and mesyloxyflavones derived from abundant and biologically important hydroxyflavones were used to synthesize a series of functionalized flavones through versatile palladium-catalyzed cross-coupling reactions. A Pd(OAc)2 /2-[2-(dicyclohexylphosphino)phenyl]-1-methyl-1 H -indole system effectively catalyzed the reactions of a broad range of tosyloxy- and mesyloxyflavones as electrophilic coupling partners with various nucleophiles to give the corresponding products in good to excellent yields. Catalyst loadings of as little as 0.1 mol% Pd were successfully used. Importantly, we demonstrated that this protocol provided a significantly improved efficiency in the synthesis of a potential chromen-4-one-based analogue of a potent inhibitor of DNA-dependent protein kinase. [ABSTRACT FROM AUTHOR]

The first general palladium-catalyzed amination of aryl phosphates is described. The combination of MorDalPhos with [Pd(π-cinnamyl)Cl] 2 enables the amination of electron-rich, electron-neutral, and electron-poor aryl phosphates with a board range of aromatic, aliphatic, and heterocyclic amines. Common functional groups such as ether, keto, ester, and nitrile show an excellent compatibility in this reaction condition. The solvent-free amination reactions are also successful in both solid coupling partners. The gram-scale cross-coupling is achieved by this catalytic system.