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The reduced graphene oxide (rGO) exhibits outstanding electrical conductivity and a high specific surface area, making it a promising material for various applications. Fe2O3 is highly desirable due to its significant theoretical capacity and cost-effectiveness, high abundance, and environmental friendliness. However, the performance of these r-GO/Fe2O3 composite electrodes still needs to be further improved, especially in terms of cycle stability. The composite of Fe2O3 anchored on N-doped graphene with inside micro-channels (Fe2O3@N-GIMC) was used to be efficiently prepared. Because the inside channels can furnish extra transmission pathways and absorption websites and the interconnected structure can efficaciously forestall pulverization and aggregation of electrode materials. In addition, N doping is also beneficial to improve its electrochemical performance. Thus, it demonstrates exceptional sodium storage characteristics, including notable electrochemical activity, impressive initial Coulombic efficiency, and favorable rate performance. The optimized Fe2O3@N-GIMC indicates outstanding discharge capacity (573.5 mAh g−1 at 1 A g−1), significant rate performance (333.6 mAh g−1 at 8 A g−1), and stable long-term cycle durability (308.9 mAh g−1 after 1000 cycles at 1 A g−1, 200.8 mAh g−1 after 4000 cycles at 1 A g−1) as a sodium-ion battery anode. This presents a new approach for preparing graphene-based high-functional composites and lays a stable basis for further expanding its application field. [ABSTRACT FROM AUTHOR]

Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). The DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the multivariate linear (MLQR) and the Bayesian model averaging (BMAQ) techniques, respectively, and with two traditional merging methods – the simple modeling average (SMA) and the one-outlier-removed average (OORA) – using descriptive and categorical statistics. Four SPPs have been considered in this study, namely, Tropical Applications of Meteorology using SATellite (TAMSAT v3.1), the Climate Prediction Center MORPHing Product Climate Data Record (CMORPH-CDR), Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR). The bilinear (BIL) interpolation technique was applied to downscale SPPs from a coarse to a fine spatial resolution (1 km). The rugged-topography region of the upper Tekeze–Atbara Basin (UTAB) in Ethiopia was selected as the study area. The results indicate that the precipitation data estimates with the DVQR, MLQR, and BMAQ models and with traditional merging methods outperform the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. The DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over the UTAB. The DVQR model substantially improved all of the statistical metrics (CC = 0.80, NSE = 0.615, KGE = 0.785, MAE = 1.97 mm d -1 , RMSE = 2.86 mm d -1 , and PBIAS = 0.96 %) considered compared with the BMAQ and MLQR models. However, the DVQR model did not outperform the BMAQ and MLQR models with respect to the probability of detection (POD) and false-alarm ratio (FAR), although it had the best frequency bias index (FBI) and critical success index (CSI) among all of the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over regions with rugged topography such as the UTAB. [ABSTRACT FROM AUTHOR]