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Uncovering descriptor-property relationships of Metal-organic frameworks for olefin oligomerization

Project Description

The direct conversion of olefins into heavier oligomers over Brønsted acid catalysts is one of the most researched strategies to upgrade shale gas feedstocks into more valuable products. The increased availability of shale gas resources and their consequent decreased cost over the past decade have attracted significant interest in their conversion into liquid chemicals and fuels.
Metal-organic frameworks (MOFs) represent a class of hybrid material built from metal ions with well-defined coordination geometry and organic bridging ligands. MOFs for heterogeneous catalysis are extremely attractive because of their well-defined pore surface chemistry that allows much-desired structure-activity relation to be established. Particularly, MOFs with excellent thermal/chemical stabilities have been recently explored for various catalytic applications. This project focuses on the iterative optimization of the catalytic properties of this class of MOFs.

First, a mechanistic microkinetic model will be developed to describe the oligomerization of light olefins on acidic catalysts. Second, a library of MOFs with potential application in olefin oligomerization will be generated.
The mechanistic model will be characterized by identifying some “kinetic” and “catalyst” descriptors, the latter accounting for the impact of the specific catalytic properties of the MOF on the kinetics of the process. In the last step the catalytic performance (e.g. conversion, selectivity) will be optimized by tuning the catalyst descriptors of the microkinetic model. The next iteration will start via consideration of the next MOF exhibiting descriptors closer to the optimal performance. The closure of this catalyst design cycle requires translating
the optimized set of catalyst descriptors to physical properties that can be determined from material characterization and tuned during the MOF synthesis.

While optimization of MOFs for olefin oligomerization is the immediate objective of this proposal, our laboratory will utilize this methodology in several other areas of our research on heterogeneous catalytic processes. The proposed iterative method will not only provide a library of MOFs for olefin oligomerization but will facilitate discovery and characterization of a new class of catalytic materials.

Computational kinetic modelling is currently in high demand in both industry and academia. The successful candidate will develop skills that lie at the heart of process industry, fine chemical and pharmaceutical industry. The area of catalytic applications of MOFs has been growing during the past years to become one of the most prospective technology in the fields of chemical synthesis and reaction engineering.

This is a multidisciplinary project involving catalysis, chemical reaction engineering and first-principle kinetic analyses. The successful candidate will benefit from a top-level research environment, as well as acquire skills at the interface between computational catalysis and microkinetic modelling. This project represents a unique opportunity for a dynamic and ambitious researcher to contribute to the predictive design and engineering of metal-organic materials and exploiting their potential in multi-step catalytic processes of high industrial relevance. The applicant will join a vibrant and well-established research group that is interested in the discovery and design of novel catalytic materials that address fundamental challenges in the chemical, environmental and energy landscape.

The ideal candidate will have a 1st class degree or equivalent in chemical engineering, process engineering, chemistry, industrial chemistry, material sciences or related disciplines, experience in cross-disciplinary work, excellent laboratory and computational skills and a hands-on approach to problem-solving. The successful candidate will benefit from a top-level research environment, as well as acquire skills at the interface between catalysis and reaction engineering, that are in high demand in both industry and academia. We are looking for highly motivated, committed, and creative individuals, able to work in a team and with excellent communication skills.
The candidate should have International English Language Testing Service (IELTS) average of 6.5 or above with at least 6.0 in each component if English is not his first language.

Funding Notes

The necessary infrastructure (office space, computational resources, etc.) will be available for the duration of the proposed fellowship. The costs of the research will be covered by funds available to Dr. Sergio Vernuccio and Dr. Peyman Z Moghadam at the University of Sheffield. For further details please contact Dr. Sergio Vernuccio via

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