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‘’Novel nanocomposites based on green resin chemistries reinforced with 2D nanofillers and natural fibres’’

Applications are invited for a fully funded studentship to start in February 2024

About the project: Nanocomposites based on thermosetting resin matrices are one of the most promising lightweight materials to replace metal components and reduce energy and greenhouse gas emissions in the transport industries. Conventional synthetic thermosetting resins derived from petroleum sources have serious drawbacks eg long degradation times, toxicity and contributing to global warming. They are typically combined with reinforcing agents such as expensive carbon fibres, which result in nonrecyclable composites. Bio-based thermosetting resins derived from natural occurring raw materials hold great promise as a sustainable natural alternative. However, bioresins being in their infancy, show inferior performance compared to their petroleum-based counterparts. This research aims to use state-of-the-art bioresins, with high % wt bio-content, and enhance their performance by reinforcing with 2D nanofillers such as graphene, as well as natural micron-sized biofibres.

The research will start with a literature review, identifying the most promising systems, applications, and research gaps. The experimental part will identify the most effective methodologies for dispersing the reinforcing agents into selected bioresin systems. Composites preparation includes precision weighting of components and determination of optimum weight ratios, dispersion methods, and processing conditions for optimum performance. You will investigate the fibre-polymer compatibility and possible fibre surface treatment for interfacial bonding with the matrix, the synergistic effects between different micro and nano-fillers, all of which play an important role on the properties of the final composites. Differential Scanning Calorimetry (DSC) will be used to study how the curing of the matrix is affected by the addition of the fillers and establish the curing conditions for optimum crosslinking density. Rheological properties and processability will be assessed via rotational rheometry. Thermogravimetric Analysis (TGA) will be used to assess the thermal stability of the fibers and developed composites. Mechanical testing will be performed to determine mechanical properties. Adhesion performance will be also tested by performing lap shear tests to determine adhesion strength of the developed systems on selected adherents. Scanning electron microscopy will be used to provide insights into microstructure and interfacial bonding strength and understand the mechanisms of failure.

The aims and objectives of this PhD will be:

1. To determine appropriate dispersion methodologies of the micro and nano-fillers into selected bioresin matrices
2. To determine how reinforcing agents affect curing and crosslinking of the matrix systems
3. To fine-tune experimental parameters to optimise mechanical and thermal properties
4. The ultimate goal is to develop novel eco-friendly composite systems that meet application requirements

At the School of Engineering, we have a strong track record in graphene/polymer nanocomposites. Our polymer labs have the facilities and equipment required for materials formulation and characterisation. The project aims to publish the results at esteemed journals and conferences raising awareness about the effectiveness of green technologies for sustainable transport.

Eligibility:

We are looking for a highly motivated candidate who should have:

At least a 2.1 degree in Materials Science, Engineering, or Chemistry or MSc/MRes in a relevant discipline, ideally with some lab experience relative to the work described above in polymer composite materials (preparation and characterisation methods).

Funding notes:

The tuition fees are fully funded by the Faculty of Technology of the University of Sunderland. With this PhD scholarship you are expected to support the research culture at the Faculty, with demonstration support in workshops etc (six hours per week).

The candidate is expected to start in February 2024. For informal inquiries please contact Associate Professor Panagiotis Karagiannidis: panagiotis.karagiannidis@sunderland.ac.uk.

Applications should be submitted online quoting the title of the PhD. Submit your CV and a one-page statement about your background and why you are interested in this programme.

Deadline for submissions is 5 January 2024. Interviews will be held the week commencing 8 January 2024.

PhD Position Title: Development of an Adaptive Predictive Maintenance Framework for Industrial Systems

Applications are invited for a fully funded PhD position to start in February 2024.

About the position: The School of Engineering at University of Sunderland, UK is pleased to announce a fully funded (tuition fee waiver + monthly stipend) PhD position. The three-year research position offers an excellent opportunity for a motivated, self-driven, and talented individual to contribute to the area of predictive maintenance in modern industrial systems.

The proposed PhD research topic is related to the field of smart factories and embedded systems. The proposed research seeks to design a framework for predicting required maintenance in industrial systems. In the said framework, the sensor data will be collected using IoTs, and a prediction model will be proposed using machine learning techniques. The research outcomes will enhance equipment reliability, safety and improve overall operational efficiency.

The successful candidate will be expected to identify key equipment that require predictive maintenance in modern industrial systems. Moreover, the candidate will need to identify the appropriate sensors to acquire and preprocess the data, develop machine learning models, design the visualization tool, implement, and validate the framework on real systems. The successful candidate will also be expected to publish the research findings in renowned/high-impact journals of the domain.

Qualifications and requirements:

• Strong academic background in electronics engineering/computer engineering/computer science (preferably an honours degree both at the bachelor and master's level)
• Strong programming skills and prior experience of programming and development in Python, C/C++
• Proficiency in oral and written communication skills
• Demonstrable team skills, passion to pursue a career in research/academia
• Prior experience of research, paper writing/publication is highly desirable
• Prior knowledge of machine learning algorithms is highly desirable
• This PhD position is strictly on-campus. Distance or remote learning cannot be supported
• As a part of this studentship, the successful candidate will be supporting in lab demonstrations (six hours per week for about 24 weeks every academic year)

Funding notes:

• The tuition fees are fully funded by the Faculty of Technology of the University of Sunderland. With this PhD scholarship you are expected to support the research culture at the Faculty, with demonstration support in workshops etc (six hours per week).
• The candidate is expected to start in February 2024. For informal inquiries please contact Dr Umer Farooq: umer.farooq@sunderland.ac.uk.

Applications should be submitted online quoting the title of the PhD. Submit your CV, academic transcripts, and a one-page statement about your background and why you are interested in this programme.

Deadline for submissions is 5 January 2024. Interviews will be held the week commencing 8 January 2024.