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Materials and Processes
Food texture is related to the way our senses perceive and feel the rheological and mechanical properties of edible substances. For example a potato chip is crispy; an apple is crunchy; butter is soft; bread is firm; candy is hard; yogurt is smooth; cream is thick; cake is moist, and honey is sticky. Food texture is critical for the consumer and impacts on a product’s market share. It is affected by the composition, manufacturing process, storage conditions and aging. It impacts the final quality and nutrition value of the food product. Food industry strives to improve texture while enhancing the product’s nutritional value and health benefits. For example, healthy oleogels can be used in substitution of harmful trans/saturated fats while retaining the sense of a “mouthful” product.
Texture is complex to quantify, as it is the result of interplay of the food mechanical and rheological properties as physically sensed in the mouth. It is the result of the complex movement of chewing involving our jaws, teeth, and tongue, and the combined comminution (particle size distribution change) and gradual dissolution of substances in saliva.
This project aims to develop and combine mechanical and rheological testing methodologies that will characterize texture rapidly and reliably, in real time, during the manufacturing and storage period. The experimental program will be complemented by state-of-the-art artificial intelligence (AI) and machine learning (ML) methodologies in order to correlate improved texture with optimized manufacturing and storage processes.
The ideal candidate will combine strong experimental and computational skills, an interest in food science and engineering, mechanics, rheology and numerical methods/software (e.g., MATLAB, Python).
https://vasileioskoutsos.wixsite.com/softmaterials
www.eng.ed.ac.uk/about/people/dr-dimitrios-i-gerogiorgis
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
An undergraduate degree in Chemical/Mechanical Engineering, or a closely related area (Physics, Chemistry), with a strong background in computational modelling.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree.
Further information on English language requirements for EU/Overseas applicants.
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere
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The conversion of biomass into value-added platform chemicals can greatly alleviate the reliance on fossil feedstock. Biomass alcohol such as benzyl alcohol is one of the important biomass-derived platform compounds and a significant feedstock, which are used to produce a series of value-added derivatives, such as benzaldehyde, hydrobenzoin, benzoin, and deoxybenzoin. Among these derivatives, the C-C coupling chemicals, hydrobenzoin, benzoin, and deoxybenzoin have higher value than the rest and can be used as the versatile intermediates for the production of high value-added downstream products, such as various chemical additives, dyestuff, pharmaceuticals as well as the precursor of photo initiator. However, in the conventional process, the synthesis of above-mentioned C-C coupling products focuses on sharpless asymmetric dihydroxylation of alkenes, and requires the use of toxic cyanide and metal complex catalysts, resulting in a host of by-products, therefore limits the development of the traditional biomass alcohol conversion.
Photocatalysis has recently emerged as a promising way for benzyl alcohol conversion because of the low energy consumption, mild reaction conditions and high selectivity. However, the reaction mainly promoted by UV light, which is a very small portion of sunlight resource. This project will develop visible-light driven photocatalysts with suitable band levels to facilitate sunlight absorbtion and create separated electrons and holes for redox reactions, particularly the photocatalytic coupling of benzyl alcohol to C-C coupling compounds with non-toxic and high selectivity catalytic process. In addition, it is important to improve the student the experimental skill, materials characterization skill and data analysis skill.
Student who joins our group will learn the fundamentals of photocatalytic reactions and photocatalyst synthesis, the use of GC, BET, FTIR, GCMS, XPS, SEM and chemical analysis to understand the reaction mechanisms. Furthermore, the student will be trained in the critical analysis of experimental data, material characterization and writing skills.
Primary objectives:
1. Train the student with photocatalytic experimental skills.
2. Teach the student how to synthesise photocatalysts.
3. Develop the student with analytical skills of experimental data and characterization, the use of GC, BET, FTIR, GCMS, XPS, SEM and chemical analysis to understand the reaction mechanisms.
4. Improve the student with critical thinking and writing abilities.
5. Reduce the by-product benzaldehyde and increase the yields of C-C coupling products.
6. Increase the selectivity of a specific product (hydrobenzoin, deoxybenzoin or benzoin).
7. Understand the mechanism of the photocatalytic C-C coupling reaction.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.
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Approximately 2 million individuals globally suffer from kidney failure, necessitating treatment options such as transplantation and dialysis. Transplantation is limited by donor availability, forcing many to rely on HD. Whereas transplant recipients exhibit approximately 80% survival rates five years post-procedure, those undergoing HD have less than a 50% chance of surviving the same period due to what's known as “residual uremic syndrome” resulting from the incomplete removal of certain uremic toxins during HD, significantly contributing to the higher mortality observed in these patients.
Current HD technologies rely on membranes which are limited by size, thus unable to effectively eliminate larger uremic toxins from the patient's bloodstream. This approach lacks precision and effectiveness as it is designed on small molecules like urea and fails to address other, more harmful toxins.
The first crucial step is to clearly identify the metabolites associated with adverse effects. This task can be addressed using a combination of metabolomics and AI. Metabolomics can detect a wide range of metabolites, some of which may play critical roles in the health outcomes of patients with kidney failure.
Three studies have investigated the link between serum metabolites and mortality in patients with kidney disease, but they have yielded inconsistent results regarding which metabolites are implicated, underscoring the need for further research. The integration of metabolomics with AI may also enhance our understanding of the mechanisms: this deeper insight is essential for developing more effective HD treatments that can mitigate the adverse effects. A comprehensive AI-based analysis of the existing data is essential, laying the groundwork for future large-scale metabolomics research. However, identifying these metabolites is just the initial step. The ultimate goal is to leverage this information to enhance dialysis treatments by developing materials capable of efficiently capturing the most toxic molecules. AI has the potential to expedite the exploration of the vast materials space. Achieving both the identification of harmful metabolites and the development of effective materials is an ambitious task, given the multitude of toxins and materials involved.
Fortunately, AI technologies can greatly accelerate progress towards these dual objectives.
https://www.bbc.com/news/uk-scotland-edinburgh-east-fife-67156562
Such study will allow to correlate specific materials features (e.g. chemical composition and porosity descriptors) to the ability of the filtering materials of removing toxic molecules correlated to mortality. This work will provide guidelines for material synthesis and/or selection in the design of more efficient and tailored HD treatment which can reduce patients’ mortality.
References: [1] The Kidney Project, University of California San Francisco, https://pharm.ucsf.edu/kidney [2] S. Al Awadhi et al, A Metabolomics Approach to Identify Metabolites Associated With Mortality in Patients Receiving Maintenance Hemodialysis, Kidney Int Rep 2024 9, 2718–26. [3] S. Kalim et al., A Plasma Long‐Chain Acylcarnitine Predicts Cardiovascular Mortality in Incident Dialysis Patients, J American Heart Association 2, 2013. [4] Hu, J.-R., et al Serum Metabolites and Cardiac Death in Patients on Hemodialysis, Clin J Am Society of Nephrology 14(5): 747-749, 2019. [5] https://nurturebiobank.org/, visited on 4th October 2024 [6] T. Fabiani et al., In silico screening of nanoporous materials for urea removal in hemodialysis applications, Phys. Chem. Chem. Phys., 2023, 25, 24069. [7] REDIAL, redefining hemodialysis with data-driven materials innovation, project https://www.suspromgroup.eng.ed.ac.uk/redial
https://www.suspromgroup.eng.ed.ac.uk/redial
https://www.ai4biomed.io/research/projects-2025/#cmsm
https://www.ai4biomed.io/
Application deadline: 20 January 2025
We offer fully funded 4-year studentships, covering tuition fees, stipend (£19,237 in 2024/25) and an individual budget for travel and research costs. There are also allowances for sick pay, maternity leave and other purposes. Funding has open eligibility regardless of your nationality and domicile.
The CDT offers additional funding for public engagement activities, evaluation experiments and research visits.
Each student will also benefit from state-of-the-art facilities, including unique data and computational resources. The CDT has access to EPCC facilities including the University’s HPC Centre of excellence offering unique AI capability (Cerebras CS-1/CS2, Graphcore Bow Pod), supercomputing (ARCHER2, DiRAC, Cirrus), and analytics platforms, assisted by over 100 technology experts. EIDF will provide research compute capacity for AI via a new cluster of 136 Nvidia A-100 GPU cards.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Tuition fees + stipend are available for Home/EU and International students
CDT AI in Biomedical Innovation Conditions
We offer fully funded 4-year studentships, covering tuition fees, stipend (£19,237 in 2024/25) and an individual budget for travel and research costs. There are also allowances for sick pay, maternity leave and other purposes. Funding has open eligibility regardless of your nationality and domicile.
The CDT offers additional funding for public engagement activities, evaluation experiments and research visits.
Each student will also benefit from state-of-the-art facilities, including unique data and computational resources. The CDT has access to EPCC facilities including the University’s HPC Centre of excellence offering unique AI capability (Cerebras CS-1/CS2, Graphcore Bow Pod), supercomputing (ARCHER2, DiRAC, Cirrus), and analytics platforms, assisted by over 100 technology experts. EIDF will provide research compute capacity for AI via a new cluster of 136 Nvidia A-100 GPU cards.
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