Abstract
Graphene-metal nanocomposites have been found to remarkably enhance the catalytic performance of metal nanoparticle-based catalysts. In continuation of our previous report, in which highly reduced graphene oxide (HRG)-based nanocomposites were synthesized and evaluated, we present nanocomposites of graphene oxide (GRO) and ZnO nanoparticle-doped MnCO3 ([ZnO-MnCO3/(1%)GRO]) synthesized via a facile, straightforward co-precipitation technique. Interestingly, it was noticed that the incorporation of GRO in the catalytic system could noticeably improve the catalytic efficiency compared to a catalyst (ZnO-MnCO3) without GRO, for aerial oxidation of benzyl alcohol (BzOH) employing O-2 as a nature-friendly oxidant under base-free conditions. The impacts of various reaction factors were thoroughly explored to optimize reaction conditions using oxidation of BzOH to benzaldehyde (BzH) as a model substrate. The catalysts were characterized using X-ray diffraction, thermogravimetric analysis, Fourier transform infrared spectroscopy, field-emission scanning electron microscopy, Energy dispersive X-ray spectroscopy (EDX), Brunauer-Emmett-Teller (BET), and Raman spectroscopy. The (1%)ZnO-MnCO3/(1%)GRO exhibited significant specific activity (67 mmol.g(-1).hr(-1)) with full convversion of BzOH and >99% BzH selectivity within just 6 min. The catalytic efficiency of the (1%)ZnO-MnCO3/(1%)GRO nanocomposite was significantly better than the (1%)ZnO-MnCO3/(1%)HRG and (1%)ZnO-MnCO3 catalysts, presumably due to the existence of oxygen-possessing groups on the GRO surface and as well as a very high surface area that could have been instrumental in uniformly dispersing the active sites of the catalyst, i.e., ZnO-MnCO3. Under optimum circumstances, various kinds of alcohols were selectively transformed to respective carbonyls with full convertibility over the (1%)ZnO-MnCO3/(1%)GRO catalyst. Furthermore, the highly effective (1%)ZnO-MnCO3/(1%)GRO catalyst could be successfully reused and recycled over five consecutive runs with a marginal reduction in its performance and selectivity.