New research paper published in the Journal of Materials Science

01 February 2018

CNIE’s new paper on ‘optimization of mesoporous titanosilicate catalysts for cyclohexene epoxidation via statistically guided synthesis’, has been published in the Journal of Materials Science.    

Synthetic porous titanosilicates are widely applied as active and selective heterogeneous catalysts to produce industrially relevant organic precursors. Since their introduction in 1983, various categories, such as microporous, mesoporous and layered types, have been synthesized. These target different applications, such as ion exchange, adsorption, membrane separation, pillaring templating and oxidation catalysis.

Despite the diversity and versatility of titanosilicates, there remain challenges in synthesizing the preferred structures. It is desirable to optimize synthesis procedures to facilitate materials development in a faster, more reproducible way. The requirement of specialised conditions and high sensitivity to changes in synthesis conditions often results in lower than expected activity of catalyst candidates. Hence, the use of a statistically guided approach could greatly benefit the optimization of titanosilicate synthesis. However, conventional optimization techniques, such as factorial, Plackett–Burman, central composite, Box–Behnken and orthogonal designs, have many disadvantages.

The goal of this study was to develop a systematic approach to improve the catalytic activity of titanosilicates, via optimization of the synthesis conditions, utilizing the Doehlert matrix model. A mesoporous titanosilicate (MTSM) was synthesized, via surfactant templating, as an effective heterogeneous catalyst for the epoxidation of cyclohexene with TBHP as oxidant. The effect of the pore structure and the amount of framework Ti4+ on catalytic performance was also investigated.

The paper was authored by Ayomi Perera, Panos Trogadas, Michael Nigra, Haiyue Yu and Marc-Olivier Coppens.

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New research paper published in the Journal of Materials Science