苯甲醇
石墨烯
氧化物
催化作用
纳米颗粒
酒精氧化
溶剂
无机化学
材料科学
化学工程
化学
纳米技术
有机化学
工程类
作者
Seyedsaeed Mehrabi-Kalajahi,Ahmad Ostovari Moghaddam,Fahimeh Hadavimoghaddam,Rana Salari,Mikhail A. Varfolomeev,Almaz L. Zinnatullin,Kamil R. Minnebaev,Dmitrii A. Emelianov,Д.А. Учаев,Rahele Fereidonnejad,O.V. Zaitseva,Gulgena R. Khasanova,Evgeny Trofimov,Alexei Rozhenko,Andreu Cabot,F. G. Vagizov
标识
DOI:10.1021/acsanm.4c00103
摘要
The development of noble metal-free heterogeneous catalysts holds promise for the solvent-free and selective aerobic oxidation of organic compounds. However, the moderate activity of these catalysts under atmospheric conditions limits their industrial use. In this work, the synthesis of noble metal-free (CoFeMnCuNiCr)3O4 high-entropy oxide (HEO) nanoparticles and their grafting on reduced graphene oxide (rGO) to produce a HEO–rGO nanocomposite is detailed. X-ray diffraction (XRD), scanning electron microscopy (SEM), and Raman and Mössbauer spectroscopy analyses confirm the formation of the spinel HEO phase. HEO–rGO nanocomposites are used for aerobic and solvent-free oxidation of benzyl alcohol, displaying excellent catalytic performance. Up to 10.36% conversion and 78.5% selectivity of benzaldehyde can be achieved in only 4 h. An extensive analytical study shows that the excellent performance of HEO–rGO nanocomposites is attributed to the synergistic effect between the rGO active sites and the abundant oxygen vacancies within the HEO nanoparticles. Moreover, four robust machine learning models including Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) are applied to predict the selectivity of the oxidation reactions. The XGBoost is demonstrated as the best-fitting model for all data with an error of less than 2.5%. Overall, both experimental and numerical data suggest the potential application of the HEO–rGO nanocomposites in chemical industries for the selective oxidation of alcohols to added-value products.
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