Regulating electronic environment on alkali metal-doped Cu@NS-SiO2 for selective anisole hydrodeoxygenation

IF 9.1 Q1 ENGINEERING, CHEMICAL Green Chemical Engineering Pub Date : 2023-09-01 DOI:10.1016/j.gce.2022.06.003
Xiaofei Wang , Xiaoxue Han , Li Kang , Shixiang Feng , Meiyan Wang , Yue Wang , Shouying Huang , Yujun Zhao , Shengping Wang , Xinbin Ma
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引用次数: 2

Abstract

Lignin utilization is a potential approach for replacing fossil energy and releasing the environment pressure. Herein, we synthesized a series of novel Cu-based catalysts, Cu@NS-SiO2 (NS = nano sphere) and alkali metals (Na, K, Rb, and Cs) doped Cu@NS-SiO2, and applied them in hydrodeoxygenation reaction of anisole. High Cu dispersion was presented on all catalysts. The modification of alkali metals on Cu@NS-SiO2 significantly enhanced the electron density of Cu sites in the following order: Cs > Rb > K > Na, among which Cs decreased the Cu 2p3/2 binding energy most (by 0.7 eV). Moreover, the modification did not substantially affect the geometric structure of Cu species. This regulable electronic environment of Cu sites was crucial for selective deoxygenation and inhibiting the hydrogenation of aromatic rings in anisole, and thus promoted the selectivity of benzene. Compared with Cu@NS-SiO2 (∼59%), the highest benzene selectivity was obtained on Cs/10Cu@NS-SiO2 at ∼83%.

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碱金属掺杂对电子环境的调控Cu@NS-SiO2用于选择性苯甲醚加氢脱氧
木质素利用是一种替代化石能源、释放环境压力的潜在途径。本文合成了一系列新型的铜基催化剂,Cu@NS-SiO2(NS=纳米球)和掺杂的碱金属(Na、K、Rb和Cs)Cu@NS-SiO2,并将其应用于苯甲醚的加氢脱氧反应中。在所有催化剂上都呈现出高Cu分散性。碱金属对Cu@NS-SiO2显著增强了Cu位点的电子密度,其顺序如下:Cs>;Rb>;K>;Na,其中Cs使Cu2p3/2结合能降低最多(降低了0.7eV)。此外,修饰对Cu物种的几何结构没有实质性影响。铜位的这种可调节的电子环境对于选择性脱氧和抑制苯甲醚中芳环的氢化至关重要,从而提高了苯的选择性。和…比起来Cu@NS-SiO2(~59%),在Cs上获得了最高的苯选择性/10Cu@NS-SiO2约83%。
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来源期刊
Green Chemical Engineering
Green Chemical Engineering Process Chemistry and Technology, Catalysis, Filtration and Separation
CiteScore
11.60
自引率
0.00%
发文量
58
审稿时长
51 days
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OFC: Outside Front Cover Outside Back Cover Outside Back Cover OFC: Outside Front Cover Integration of physical information and reaction mechanism data for surrogate prediction model and multi-objective optimization of glycolic acid production
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