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The Surface and Bulk Properties of Particulate Materials and their Caking Behaviour

Project Description

In partnership with a major European chemical company

Led by Professor Daryl Williams SPEL is one of the research groups within the Department of Chemical Engineering Imperial College London. SPEL first found life in Spring 2007 and currently has 10 PhD students and 4 postdoctoral researchers.
SPEL specialises in characterising solid state particulate materials, such as powders, fibres films and aggregates. With our world class characterisation and solids preparation facilities, we study pharmaceuticals, biopharmaceuticals, foods, adsorbents and complex organic solids with a specific interest in understanding the fundamental relationship between particle properties and their performance/manufacture.

Powder processing is common in many industries such as detergent, pharmaceutical, bulk chemical and food. Many materials are susceptible to changes with time leading to bulk powder caking and agglomeration, resulting in an unmanageable powder and process downtime, which impacts upon profitability and final product performance. Though industrial test methods exist which can quantifiy the mechanics and onset of powder caking phenomena, the core relationship between material surface and bulk properties with caking phenomena are not clearly understood for many classes of powders.
This project will use advanced materials characteriation techniques available at ICL to establish those surface and bulk material attributes which are determinative in powder caking behaviour. These attributes include: Surface energy, Water sorption isotherms (DVS) and surface/bulk amorphicity, Particle surface area/morphology and as well as related effects of time and temperature. The student will be trained in DVS and IGC techniques pioneered at ICL, as well as other characterisation techniques such as DSC, XPS and BET. The project will examine on a series of model particulate materials (from selected classes of solid state materials eg dry detergents, pharmaceutical formulations, agrichemicals) which represent common types of powder caking which are problematic industrially. The materials characterisation work will be performed at ICL, whilst the cake mechanics/agglomeration work will be performed at our industrial partner. The key research question will be which material surface property is most crucial in determining the caking performance for a specific material class. The development of new experimental methods, analyses and understanding by this project will directly inform industrial practise at our partner who is one of Europes largest chemical companies.

The PhD student will be part of the 2019 Cohort for the Centre for Doctoral Training in the Advanced Characterisation of Materials based at Imperial College.

This project will suit a graduate in chemical or mechanical engineering or in the physical sciences including materials science, chemistry and physics. To be eligible, candidates must either have, or expect to obtain, a Master’s degree with 2.1/Merit or higher or a Bachelor’s degree with research/ relevant industry experience equivalent of an MSc. This studentship is only available to UK and EU students who fulfill the three-year UK residency criteria.

The deadline is Friday May 3rd, however applicants are recommended to apply early. Please send your CV and cover letter to:

Funding Notes

They will receive a comprehensive professional and technical training programme, (i) a tax free bursary of not less that £17k, (ii) tuition fees at UK rate, and (iii) an annual financial contribution towards research and training. The industry partner will also provide financial support as agreed upon between the industry partner and the supervisor. It is expected that the applicant will spend two 3 months periods working with the industrial partner in their European laboratories.

How good is research at Imperial College London in Aeronautical, Mechanical, Chemical and Manufacturing Engineering?

FTE Category A staff submitted: 172.80

Research output data provided by the Research Excellence Framework (REF)

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