Oxidative Desulfurization Catalyzed by Phosphotungstic Acid Supported on Hierarchical Porous Carbons

Recent Progress on Functionalized Nanoporous Heteropoly Acids: From Synthesis to Applications

Functionalized nanoporous heteropoly acids (HPAs) have garnered significant attention in recent years due to their enhanced surface area and porosity, as well as their potential for low-cost regeneration compared to bulk materials. This review aims to provide an overview of the recent advancements in the synthesis and applications of functionalized HPAs. We begin by introducing the fundamental properties of HPAs and their unique structure, followed by a comprehensive overview of the various approaches employed for the synthesis of functionalized HPAs, including salts, anchoring onto supports, and implementing mesoporous silica sieves. The potential applications of functionalized HPAs in various fields are also discussed, highlighting their boosted performance in a wide range of applications. Finally, we address the current challenges and present future prospects in the development of functionalized HPAs, particularly in the context of mesoporous HPAs. This review aims to provide a comprehensive summary of the recent progress in the field, highlighting the significant advancements made in the synthesis and applications of functionalized HPAs.

Advanced Carbon Nanostructures: Synthesis, Properties, and Applications

Carbon nanomaterials are a class of materials that include allotropic modifications of carbon [...].

Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods

A nano-ZnS-decorated hierarchically porous carbon (ZSHPC) was mixed with MWCNTs to obtain ZSHPC/MWCNT nanocomposites. Then, ZSHPC/MWCNTs were used to modify a screen-printed electrode, and a portable electrochemical detection system combined with machine learning methods was used to investigate carbendazim (CBZ) residues in rice and tea. The electrochemical performance of the constructed electrode showed that the electrode had good electrocatalytic ability, large effective surface area, strong stability and anti-interference ability. Support Vector Machine (SVM), Least Square Support Vector Machine (LS-SVM) and Back Propagation-Artificial Neural Network (BP-ANN) were used to establish the prediction model for CBZ residues in rice and tea, and the traditional linear regression was developed. The investigated results showed that the LS-SVM model had the best prediction performance and the lowest prediction error compared with the traditional linear regression, BP-ANN and SVM models. The R², RMSE, and MAE for the training set samples were 0.9969, 0.3605 and 0.2968, respectively. The R², RMSE, MAE and RPD for the prediction set samples were 0.9924, 0.6190, 0.5360 and 10.3097, respectively. The average recovery range of CBZ in tea and rice was 98.77-109.32% and that of RSD was 0.47-2.58%, indicating that the rapid analysis of CBZ pesticide residues in agricultural products based on a portable electrochemical detection system combined with machine learning was feasible.