RESUMO
Understanding the valency and structural variations of metal centers during reactions is important for mechanistic studies of single-atom catalysis, which could be beneficial for optimizing reactions and designing new protocols. Herein, we precisely developed a single-atom Cu(I)-N4 site catalyst via a photoinduced ligand exchange (PILE) strategy. The low-valent and electron-rich copper species could catalyze hydrophosphinylation via a novel single-electron oxidative addition (OA) pathway under light irradiation, which could considerably decrease the energy barrier compared with the well-known hydrogen atom transfer (HAT) and single electron transfer (SET) processes. The Cu(I)-Cu(II)-Cu(I) catalytic cycle, via single-electron oxidative addition and photoreduction, has been proven by multiple in situ or operando techniques. This catalytic system demonstrates high efficiency and requires room temperature conditions and no additives, which improves the turnover frequency (TOF) to 1507 h-1. In particular, this unique mechanism has broken through the substrate limitation and shows a broad scope for different electronic effects of alkenes and alkynes.
RESUMO
Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.
Assuntos
Inundações , Modelos Logísticos , Aprendizado de Máquina , China , Modelos Teóricos , Algoritmo Florestas AleatóriasRESUMO
High-valent iron-oxo species are one of the common intermediates in both biological and biomimetic catalytic oxidation reactions. Recently, hydrogen-bonding (H-bonding) has been proved to be critical in determining the selectivity and reactivity. However, few examples have been established for mechanistic insights into the H-bonding effect. Moreover, intramolecular H-bonding effect on both C-H activation and oxygen atom transfer (OAT) reactions in synthetic porphyrin model system has not been investigated yet. In this study, a series of heme-containing iron(IV)-oxo porphyrin species with or without intramolecular H-bonding are synthesized and characterized. Kinetic studies revealed that intramolecular H-bonding can significantly enhance the reactivity of iron(IV)-oxo species in OAT, C-H activation, and electron-transfer reactions. This unprecedented unified H-bonding effect is elucidated by theoretical calculations, which showed that intramolecular H-bonding interactions lower the energy of the anti-bonding orbital of iron(IV)-oxo porphyrin species, resulting in the enhanced reactivities in oxidation reactions irrespective of the reaction type. To the best of the knowledge, this is the first extensive investigation on the intramolecular H-bonding effect in heme system. The results show that H-bonding interactions have a unified effect with iron(IV)-oxo porphyrin species in all three investigated reactions.