PhD Opportunities

Project title: Tackling the pandemic of antibiotic-resistant infections: An artificial intelligence approach to new druggable therapeutic targets and drug discovery (3 places offered)

The use of antibiotics to control bacterial infections is perhaps the most important achievement of modern medicine. However, we have failed to keep pace with microbes becoming increasingly resistant to available treatments. Antibiotic-resistant infections are already another global pandemic claiming almost 5 million deaths per year globally.

The increasing isolation of strains resistant to "last resort" antimicrobials has significantly narrowed, or in some settings completely removed, the therapeutic options. This is particularly alarming in low and middleincome countries. Unfortunately, new classes of drugs are not being invented and resistance continues to spread inexorably. A better understanding of the means used by microbes to resist antibiotics may result in the discovery of hitherto unknown targets suitable to develop new drugs against.

In this research, we will use artificial intelligence, bioinformatics and microbiology to identify new potential druggable targets that when blocked may render the microbe susceptible to antibiotics. Next, and utilizing other learners, we will identify drugs that can block these targets. Our analysis will also target another important aspect linked to antibiotic-resistant infections that is transmission, again using a combination of expertise we will use our and publicly available data to study drivers and transmission of resistant pathogens in different anthropogenic environments including (communities, hospitals, livestocks, etc.,)

Contact points

Dr. Tania Dottorini tania.dottorini@nottingham.ac.uk

Project title: To explore the genetic mechanisms of multiple pain phenotypes based on the UK Biobank cohort

There are many site-specific pain phenotypes in the human body such as back pain, hip pain, knee pain, etc. These pain phenotypes could be considered as common complex traits like diabetes. However, we have limited knowledge about the genetic mechanisms of these pain phenotypes despite some studies have suggested that genetic components play a role in the disease mechanisms. The UK Biobank has collected the genetic information and pain-related information of its participants which make this genetic research possible. We aim to identify the genetic variants that contribute to multiple pain phenotypes through a genome-wide association study (GWAS) approach using the UK Biobank datasets. We will also investigate the genetic correlations among these pain phenotypes. Year 1: the student will receive training in background reading and get familiar with GWAS software. Year 2: the study will perform multiple GWAS to explore potential genetic variants for pain phenotypes. Years 3: the student will submit manuscripts to journals and publish them. Meanwhile prepare his/her dissertation. 

Contact point

Dr. Weihua Meng weihua.meng@nottingham.edu.cn

Project title: Probabilistic machine learning analysis of electrochemical data for characterization of mixed-species biofilms 

Biofilms are microstructured microbial communities that thrive at surfaces and interfaces. Differently from the planktonic lifestyle, in which single cells swim independently in liquid media, unaware of the presence of surrounding surfaces, biofilm “mode of life” entails a broad range of interactions among cells and between cells and the environment. The combined effect of these dynamic interactions is the aggregation of cells at the interface and the production of extracellular polymeric substance (EPS) that

keep the cells close to each other and attached to surfaces. In real-world, biofilms are open systems, so they are naturally exposed to influx and contamination from the surrounding environment. The natural consequence is that most biofilms are mixed-species communities, comprising bacteria and fungi. Mixed-species biofilms are a serious concern in healthcare, as they result in difficult-to-treat infections and they harbour antimicrobial resistant microorganisms. 

While natural biofilms comprise of multiple bacterial and fungal species, most studies still concern single species biofilms, which present an unrealistic response to antimicrobial agents, thus requiring costly animal model experiments for further validation. Recently, there have been several attempts to study multispecies biofilms using stable multispecies models. While the results obtained from these experiments are closer to reality, there is still a knowledge gap in the understanding of mixed-species

biofilm in open systems. Even if high-end methods like sequencing are available to characterize mixed-species biofilms, these are expensive and require highly skilled operators. Currently, there is not yet a single technology to monitor mixed-species biofilms, and complex, customized protocols are required to understand these complex systems in laboratory and industrial research. 

The availability of low-cost methods to characterize mixed-species biofilms will allow the rapid identification of pathogens in mixed-species biofilm infections and in water systems, with great benefits in terms of health and lower maintenance expenses, respectively. Further, the rapid identification of pathogens in the periprosthetic liquid or on the surface of the implant will enable effective prevention and treatment of serious infections, like those occurring in prosthetic and implanted patients. The global economic burden of biofilms is estimated in excess of $5000 bn a year. Overall, there is an urgent need for rapid and low-cost methods for mixed-species biofilm characterization in biomedical, environmental and bioprocess industry. 

Direct electrochemistry of biofilms is an established research area for biofilms characterization. Biofilm electrochemistry can contribute to the resolution of mixed-species biofilms, due to its low cost, real-time and non-destructive characteristic. While biofilm electrochemistry cannot provide a final identification of each microbial species, it is in theory possible to analyse the specific signature of

each microbial species using probabilistic machine-learning (PML) methods. In this project, the two PhD students will develop a novel method for real-time, online characterization of mixed-species biofilms using bioelectrochemical methods in combination with PML driven data analysis. The proposed method is expected to produce a digital output that can be directly analyzed and further elaborated at low cost. This is a step toward a more complete characterization and knowledge of mixed-species biofilms, which will have strong applications in health and industrial sectors. 

PhD student 1 will focus on the electrochemical, microscopy and spectroscopy characterization of polymicrobial biofilms. S/he should have a background in Chemical/Biochemical Engineering or Physical Chemistry, with a strong interest in Microbiology. This project will involve the collaboration of two external scientists, Massimiliano Galluzzi (SIAT-Shenzhen, China) and Elia Marin (KIT, Kyoto, Japan), which will contribute to the Atomic Force Microscopy (AFM) and Raman Spectroscopy characterization of the mixed-species biofilms, respectively. The PhD student 1 might be shortly seconded to these labs for training and part of the experimental work. 

PhD student 2 will focus on the writing of the Probabilistic Machine Learning (PML) code and electrochemical data (from PhD student 1) analysis for the modelisation of the mixed-specie biofilms. S/he would have a background in Data Science/Computer Science/Physics/Applied Mathematics/Engineering with a strong interest in Mathematical and Computational Biology. The PhD student 2 will work closely with the PhD student 1 to optimize the data acquisition pipeline. The PhD student 2 will be co-supervised by Alberto D’Onofrio, a PML and Mathematical/Computational Biology expert at University of Trieste, Italy. The project might include a short-term secondment at University of Trieste for training in mathematical methods and data analysis. 

Contact points

Enrico Marsili (UNNC) – enrico.marsili@nottingham.edu.cn 

Elia Marin (Kyoto Institute of Technology, Japan) - elia-marin@kit.ac.jp

Massimiliano Galluzzi (Shenzhen Institute of Advanced Technology, China) - galluzzi@siat.ac.cn

Alberto D’Onofrio (University of Trieste, Italy) - alberto.d'onofrio@units.it

The project aims to develop a suite of intelligent data-driven approaches in the transformation of the traditional manufacturing paradigm to smart manufacturing. It could empower today's car manufacturers to adopt data-driven strategies to enhance the customer experience, ensure road safety and ultimately stand out in the fierce competition automotive market.

The specific objectives include:

1. Construct new data-driven models that can predict/explain the relationships between different user, task and environment variables as they relate to automated vehicle design characteristics. There has been an emphasis on how from a technological perspective the status of the driver (e.g. emotions, fatigue, motion sickness system use) can be monitored and predicted– especially in real-time.
2. Develop intelligent algorithms and data warehouse technologies that researchers and practitioners can use to profile users, prepare automated vehicles (AVs) for a smooth transition between roles and calibrate user trust for AVs.
3. Develop a new data-driven vehicle concept that demonstrates the ultimate user experience by showcasing the novel adaptive Human Machine Interfaces (HMIs) developed within the research theme.

There is a focus is to explore how a driver profile, specifically within an automated driving context and generated based upon driver status, can be used to inform intelligent adaptations of the HMIs. 

Contact point

Prof. Xu Sun (xu.sun@nottingham.edu.cn)

Combinatorial optimisation problems (COP) have extensive real-life applications. However, most of them are NP-Hard and finding the optimal solutions is normally computationally prohibitive for large-size instances. The problems become even harder when uncertainties are taken into account to improve the practicality of the solutions.

The existing approaches to tackle these types of problems can broadly be classified into analytical model driven methods (typified by mathematical programming methods) and data-driven methods (e.g. genetic programming and reinforcement learning. The former methods focus on the analytical properties of the mathematical model but may suffer from the robustness issues over uncertainties from the input data.

The data driven methods often formulate the combinatorial problems as online optimisation problems and try to tackle the problem sequentially based on some policies or rules upon the realisation of random variables and the states of the partial solution at each decision point. One of the main drawbacks of these data driven methods is their inability to efficiently exploit the core structures and properties of the problem.

More specifically, existing data driven methods primarily focus on the objectives to be optimised but often neglect various complex inter-dependencies among the decision variables (in the form of constraints) and their collective influence on the objective.

In this research, the students shall investigate integrating linear/integer programming methods with the latest deep learning methods, including but not limited to reinforcement learning and graph neural network based learning.

Contact point

Prof. Ruibin Bai Ruibin.bai@nottingham.edu.cn

Converting carbon dioxide (CO₂) into chemical fuels through photocatalytic process via harvesting solar energy has attracted increasing attention as a promising technology to reduce carbon emission and mitigate the greenhouse gas effect. The state-of-art photocatalytic materials mainly rely on ultraviolet light irritation, and the photocatalytic applications are hindered by a number of bottlenecks such as low solar energy utilization rate, poor selectivity of reduced product, limited photogenerated electronhole separation efficiency, and unsatisfactory CO₂ conversion efficiency. Therefore, it is important to study the mechanisms and methods to enhance the visible-light induced photocatalytic reduction efficiency of carbon dioxide.

In this study, microfluidics will be applied to fabricate porous microfiber membranes, the surface of which will be integrated with one-dimensional metal oxide nanorod arrays to form branched nanorod hierarchical heterostructures, which can increase the mobility of charge carriers to the surface of the fiber membrane and improve the photocatalytic activity. The mechanism of the effective separation of photogenerated charges by the 1D branched nanorod hierarchical heterostructure will be elucidated, and the migration principles of the photogenerated charges on the surface of the materials with nanopores and nanorods will be studied. The heat and mass transfer model combing CO₂ adsorption/diffusion with photocatalytic reaction will be established. The transport mechanism of CO₂ molecules and water vapour molecules in porous media as well as their mutual-working mechanism will be studied. The contribution of heat transfer to the mobility of the photogenerated charges will be investigated. The role of the coupling mechanism between heat and mass transfer with regards to the carbon capture process and visible light induced photocatalytic reduction process will be elucidated. The heat and mass transfer mechanism of gas-liquid trans-membranes will be studied as well. The research will pave a way for development of the microfiber membrane technology which can lead to efficient capture and photocatalytic reduction of CO₂ under visible light into methane.

Ultimately, the research project will provide new insights to understanding the light-heat coupling mechanism, and absorption-diffusion coupling mechanism in photocatalytic process, and it will inspire novel energy-saving and environmental-protection applications. 

Contact point

Yong Ren yong.ren@nottingham.edu.cn

In separation of mixture, it is crucial to select the appropriate combination of driving forces. To achieve a safe, economical, and operable separation process, large number of feasible options will need to be studied in the design stage. Unfortunately, equilibrium data are not readily available for the system especially while developing a new processing route. In addition, it is costly to obtain such equilibrium data empirically.

In this work, it is aimed to develop a robust prediction method to supply the necessary data for the above process at suitable accuracy as needed for various stages of process development. Furthermore, experiments will be designed to validate and increase the accuracy of the calculation at the given design/operation condition. In this project, successful candidates will be provided with the intensive training about process development especially in the area of separation process.

In addition to experimental works, a systematic use of process modelling and machine learning algorithms will be beneficial to the optimization of the designed process. There are also opportunities for the developed method to be validated on industrial-linked projects.

Contact point

Lionel O’Young, Lionel.OYoung@nottingham.edu.cn

Kam Loon Fow, Kam-Loon.Fow@nottingham.edu.cn

Kien Woh Kow, Kien-Woh.Kow@nottingham.edu.cn

Project title: Research and development of innovative vibration control of electro-mechanical drive trains (EMDs) using novel active bearings

This project is focused on addressing important challenges in achieving effective vibration/noise suppression of an electromechanical drive train, consisting of a combined electric motor and a mechanical drive train, for a wide range of advanced manufacturing and transportation applications. The noise and vibration problems in electromechanical drive trains are commonly addressed by over-engineering that leads to relatively bulkier and heavier designs. Therefore, to address the vibration and noise problems, the project aims to develop an innovative active vibration control system for electromechanical drive trains, incorporating electromechanical actuation to suppress excessive vibration of the system.

The project will cover vibration control system design which will be verified through simulation and experimental investigations. The research investigation includes the rotordynamic modelling and analysis of electromechanical drive trains; design of vibration control systems with observers; and the stability analysis, performance evaluation and optimization of active vibration control systems for electromechanical drive trains. This project is expected to provide an important theoretical and practical foundation for the development of effective active vibration control of electromechanical drive trains for industrial applications.

Contact point

Assoc. Prof. Dunant Halim dunant.halim@nottingham.edu.cn

Neural network based learning has become the dominant paradigm in modern machine learning. Although the success stories of machine learning – especially deep learning – have garnered significant attention, there are fundamental challenges that must be addressed before neural networks may be deployed as reliable components in safety/mission critical systems. Some of the notable challenges include interpretability, robustness, and cost of training.

To address these challenges, a systematic approach based on sound mathematical principles must be adopted. At the same time, such research must be guided by the insight obtained from practical applications of machine learning.

The aim of this proposal is to study computational properties of neural networks at a fundamental level. We analyze salient aspects such as scalability, accuracy, robustness and interpretability of machine learning systems deployed in engineering applications. The proposal is part of a broader cross-disciplinary project that involves electrical engineering, chemical engineering, and material sciences. As such, this is an ideal opportunity for candidates who are interested in fundamental research which can also broaden their understanding of science and engineering by collaborating with researchers from a variety of disciplines.

Contact point

Dr. Amin Farjudian: Amin.Farjudian@nottingham.edu.cn

Prof. Jim Greer: Jim.Greer@nottingham.edu.cn

This PhD project will focus on materials computational modelling for chemical sensing. Highly motivated students with a degree in chemical engineering, chemistry, physics, or material science are encourage to apply. This project is part of a larger programme between the Departments of Chemical Engineering, Computer Science and Electrical and Electronic Engineering leading to intelligent sensors.

Chemical sensing, as are sensors in general, are now in use for a wide range of applications in chemistry, energy, industrial control, and health applications. For example, there are typically between a half dozen to two dozen sensors performing different tasks in a modern smartphone, allowing information from the external world to be converted to digital information for processing and decision making. Often information from a large number of sensor nodes in a network is transmitted to a central computing infrastructure such as the 'cloud' or a locally dispersed computing infrastructure (the 'fog').

In the era of IoT and big data, the requirements for gas sensors, in addition to sensitivity and selectivity, have been increasingly placed on sensor simplicity, deployment in ‘dirty’ environments, ease for integration, and flexibility. A key to meet these requirements is the development of high-performance gas sensing materials. Two-dimensional (2D) materials provide a number of attractive properties that are beneficial to gas sensing, including the versatile and tuneable electronic/optoelectronic properties, the rich surface chemistry and they can be readily convert chemical detection into electrical signals. Since the materials are ‘2D’, the surface-to-volume ratio is exceptionally high, allowing for chemical detection to readily modulate electronic currents, hence providing an efficient transducer. Understanding the gas-solid interaction and the subsequent signal transduction pathways is essential for improving the performance of existing sensing materials and searching for new high performing materials.

In this project, the reactivity for flammable (butane, hydrogen, propane, methane) and toxic (carbon monoxide, sulfur dioxide) gases interacting with various lowdimensional structures will be investigated using quantum chemical calculations to determine reactivity and bonding mechanisms to the sensor materials. Since quantum confinement effects strongly influence the electronic structure of nanostructures, tailoring the geometries to induce a significant perturbation upon specific gas absorption to the band structures will be explored.

Contact point

Dr. Hainam Do Hainam.Do@nottingham.edu.cn

Prof. Jim Greer Jim.Greer@nottingham.edu.cn

For patients with severe diseases, e.g. elderly people or new-born babies, the real-time monitoring of multiple physiological indices during MRI scanning is essential. Due to the strong electromagnetic field in MRI Scanner room, the traditional electronic sensors for monitoring health status will be damaged. This project proposes an MRI compatible wearable sensor based on Fiber Bragg Grating (FBG) technology with the advantages of immunity to EM fields, high sensitivity, light-weight, high flexibility, high stability, low cost and small size. This wearable sensor is able to monitor the following parameters in real time: 1. Human physiological indices: body temperature, heart rate, blood oxygen level, breathing, volume of perspiration; 2. Environmental parameters, i.e. room temperature, humidity and audio level.

Besides real-time monitoring, machine learning will be adopted in data analysis, in order to analyse the patient’s emotion and to identify any dangerous circumstances, e.g. sudden apnoea, abnormal heart rate. For example, the audio signal will be separated into environmental audio and human audio, and based on the signal of human audio, breathing, heart rate, and body temperature, dangerous circumstances can be diagnosed and a warning message will be sent to the doctors immediately. The proposed system, with only minor modification to the system setup, can also be used as daily or long-term physiological monitoring within normal environment where there is a need.

The objectives of this proposed research in Phase I are to:

1. Design and setup a sensing system for the real-time detection of human physiological indices (body temperature, heart rate, blood oxygen level, breathing, volume of perspiration) and environmental parameters (room temperature, humidity and audio level), which is mainly designed for simultaneous monitoring of patients during MRI scanning.
2. Develop the diagnosing and warning system using signal processing techniques and machine learning algorithm. 3. Investigate the relationship between physiological indices (especially PWV) with hypertension and angiocardiopathy.

Contact point

Jing Wang Jing.Wang@nottingham.edu.cn

Project title: Multi-material DLP printer for fabricating programmable devices

Additive Manufacturing provides a unique opportunity to create complex geometries that can be customized for individuals. The recent voxelated manufacturing technology (also known as multi-material additive manufacturing or 4D printing) enables a new level of design and customization freedom, bringing new opportunities to a variety of applications. This project aims to develop a Digital Light Processing (DLP) based additive manufacturing system to achieve voxelated manufacturing aiming for customized medical devices. The successful Ph.D. candidate will develop a novel system with unique printing and cleaning strategy to achieve hybrid printing of polymeric/ceramic materials.

This project will be carried out jointly with the University of Nottingham Centre for Additive Manufacturing (CfAM) group and the student is expected to work at CfAM UK during Year 3 (subject to the student’s progress). In year 1 and 2, the student will be based at China Beacons Institute, University of Nottingham Ningbo China.

We are seeking talented candidates with:

• First or upper second class degree in mechanical/mechatronics or related scientific discipline
• Demonstrated ability to develop precision mechatronics system and algorithm
• Background with relevant packages (MATLAB,Python,LabVIEW)
• A professional and self-motivated work attitude is essential 

Contact point

Dr. Yinfeng He yinfeng.he@nottingham.edu.cn

Project title: Development of multi-material DLP compatible photoreactive formulations for specialized medical devices

Additive Manufacturing of polymers and metals has significant progress over the past 40 years. However, AM of ceramic is still at an early stage of development and not yet widely used in commercial use. In recent years, the strategy of using photoreactive ceramic slurries has attracted great attention owing to its capability of achieving high-resolution ceramic structures. This project aims to develop photoreactive ceramic formulations that is compatible with Digital Light Process(DLP) and Projection Micro-stereolithography (pµSL) Printing process. The successful Ph.D. candidate will look into the popular ceramic suspensions (Silica, SiC), dispersion, and polymerization strategy, and optimize the print and sintering process to achieve customized parts with minimum contamination.

This project will be carried out jointly with the University of Nottingham Centre for Additive Manufacturing (CfAM) group and the student is expected to work at CfAM UK during Year 3 (subject to the student’s progress). In year 1 and 2, the student will be based at China Beacons Institute, University of Nottingham Ningbo China.

We are seeking talented candidates with:

• First or upper second-class degree in Chemistry/Material Science/Polymer/Ceramic Technology or related scientific discipline
• Experiences with Additive Manufacturing especially digital light processing are preferable.
• A professional and self-motivated work attitude is essential 

Contact point

Dr. Yinfeng He yinfeng.he@nottingham.edu.cn

Project title: Design and manufacture of vascular stents for the application of voxel based manufacturing

The real potential and value of Additive Manufacturing (AM) will come from the design and Implementation areas. We will explore the development of next-generation bio-scaffolds for Heart, digestive tract or orthopaedic diseases using our developed Multi-material DLP printers and photoreactive formulations. The Multi-material DLP printers will be applied to control the distribution of multiple polymer materials, enable macro- and micro lattice structure, and fulfil the customized shape of the bio-scaffold. Reliability optimization among material distribution, structural dimensions and processing parameters is to be carried out to maintain the scaffold’s durability to resist mechanical and structural damage. The successful implementation of this project will be promising for the development of various high-end bio-scaffold products for the Heart, digestive tract or orthopaedic diseases.

This project will be carried out jointly with the University of Nottingham Centre for Additive Manufacturing (CfAM) group and the student is expected to work at CfAM UK during Year 3 (Subject to the student’s progress). In years 1 and 2, the student will be based at China Beacons Institute, University of Nottingham Ningbo China.

We are seeking talented candidates with:

• First or upper second-class degree in mechanical/material or related scientific discipline
• Demonstrated ability to design, manufacture and evaluation of medical devices
• Background with relevant packages (CAD/CAE software)
• A professional and self-motivated work attitude is necessary 

Contact point

Dr. Yinfeng He yinfeng.he@nottingham.edu.cn

Project 1: Hydrogen (H₂) energy is a clean energy for developing the sustainable society towards the Net Zero, yet the current H₂ production routes are not sustainable, viz. almost 50% H₂ production is based on steam reforming reactions employing natural gas and/or light hydrocarbons, and the use of fossil resources is unsustainable and associated with significant emissions of greenhouse gases. Ethanol is an established renewable platform since its production via biomass (such as corn grains and sugarcane) fermentation is the currently major route and economical. Hence, in steam reforming replacement of fossil resources with ethanol, i.e., steam reforming of ethanol (SRE) is one of the promising methods for producing sustainable H2.

CO₂ hydrogenation to methane and methanol

Methane (CH₄) and methanol (CH₃OH) are key fuels and platform chemicals for many important applications. They are conventionally obtained from fossil resources such as natural gas and coal. Hence, the use of the captured CO2 as the carbon source can be a sustainable option to produce green CH₄ and CH₃OH for sustainable development of the society. Built on our previous research findings, this project will focus on the further development of economic catalysts based on transition metals such as Ni for methanation and Cu for hydrogenation to green methanol, which will be supported by relevant in situ and kinetic studies to gain mechanistic insights of the systems for pilot test and scaling up.

Steam reforming of ethanol for bio-hydrogen production

Steam reforming reactions are normally performed in the temperature range from 200 to 650 °C, and hence catalyst deactivation at high temperatures is a challenging aspect to be addressed. Regarding the Ni-based reforming catalysts, the preparation of highly dispersed yet stable supported Ni phases could be achieved by (i) metal phase engineering such as second metal doping to disperse Ni, (ii) metal-support engineering such as confinement and (iii) process intensification (such as the use of structured supports) This project will seek rational design of novel reforming catalysts with significantly improved activity and stability and novel intensified processes for SRE. In addition, based on the experimental results, process simulation and techno economic assessment (TEA) which will be addressed by this project. 

Project 2: CO₂ as one of the main emissions which impose significant impact on human society such as climate change. To answer the call of China carbon neutrality in 2060, this project aims to convert the captured CO₂ from various sources (such as power plant and steel industry) to green platform chemicals such as syngas (via reforming reactions such as). This will be achieved by thermal and/or plasma catalysis, and will take the rational approach for catalyst design which involves the mechanistic study of the activity of different metallic catalysts such as Ni for dry reforming. Also, process intensification of reforming processes (via such as using fluidised bed and/or plasma activation) will be researched to alleviate the catalyst deactivation issues using fluidised bed.

Dry reforming of methane with CO2 for syngas production

Syngas (i.e., CO+H₂) is the essential feedstock to produce many value-added oxygenated chemicals and long-chain hydrocarbons via Fischer-Tropsch reactions. Again, the use of the captured CO₂ as the feedstock, together with CH4, can help the shift the current reliance on fossil resources to more sustainable sources such as carbon waste and biogas, hence leading to the production of green chemicals via green syngas. However, the system involves significant carbon sources, which leads to substantial carbon deposition (coking), causing catalyst deactivation and pressure build-up. Also, the system requires high temperatures to activate the stable CO₂/CH₄, which lead to metal sintering and catalyst deactivation. Hence, rational design of novel reforming catalysts with significantly improved activity and stability and novel intensified processes are needed, which will be addressed by this project. 

Contact point

Dr. Xiaoxia Ou Xiaoxia.Ou@nottingham.edu.cn

CO₂ hydrogenation with hydrogen from renewable resources can not only mitigate the CO₂ emission, but also produce commodity chemicals that can be used either as fuels or as precursors in many industrial chemical processes. Catalytic hydrogenation of CO₂ has been studied intensively, however, due to the low carbon to hydrogen ratio and the low adsorption heat of CO₂ on the catalyst surface, the products of direct hydrogenation are limited mostly to low molecular weight hydrocarbons or oxygenates instead of heavier liquid hydrocarbons, which are more suitable for transportation fuel.

The team has gained a lot of experience in heterogeneous catalysts design for energy and environmental applications. In this project, high performance catalyst will be designed for CO₂ hydrogenation to maximize liquid hydrocarbons production.

Contact point

Dr. Xiaoxia Ou Xiaoxia.Ou@nottingham.edu.cn

The development of anticancer drugs involves drug-like molecule design and synthesis, lead identification and optimization, as well as later development in clinical trials, and then finally marketing. Computer aided drug development can greatly reduce the research and development (R & D) cycle and R &D costs. Within this, artificial intelligence is currently actively adopted for drug screening, design and synthesis. As a powerful data analysis and data mining tool, machine learning, as an important branch of artificial intelligence, is expected to play an important role in virtual screening.

This project aims to set up an artificial intelligence based anti-cancer drug discovery platform with the ability to virtually screen potential anticancer candidates. The screening of inhibitors for high-risk tumor related targets such as BRCA, EGFR, LSD1, PARP, DNMT1 will be carried out as case studies for this platform. Meanwhile the obtained potential inhibitors for these targets will be synthesized for biological tests and further development.

We are seeking TWO PhD students in this project. Candidate 1 is expected to carry out synthesis who should have strong experience in medicinal chemistry and organic synthesis. Candidate 2 is expected to have strong experience in applying machine learning in drug screening, who ideally should have experience in computer programing.

You are welcomed to contact us (via email bencan.tang@nottingham.edu.cn) to discuss these opportunities further.

Contact point

Dr. Bencan Tang bencan.tang@nottingham.edu.cn

Project title: multiphase flow studies with cutting-edge technologies (high-precision 3D printer, artificial intelligence, supercomputers and so on)

Multiphase flow systems are widely used in chemical, pharmaceutical, environmental industries. However, the multiphase flow is usually highly dynamic and very complex, hindering the development of multiphase flow theory and the optimization of multiphase flow systems.

In our lab, PhD students will work closely with industrial partners on multiphase flow systems, such as chemical reactors, dry powder inhalers, medicine coating, protein recovery from liquid streams. Cutting-edge technologies, including artificial intelligence (AI), 3-D printing, high-speed camera, intrusive probes, will employed to experimentally characterize the multiphase flow. Referring to the experimental data, numerical models will be developed with workstations, servers and supercomputers. Finally, multiphase flow systems can be designed and intensified for specific industrial processes. 

Contact point

Xiaoyang Wei xiaoyang.wei@nottingham.edu.cn

Project title: Accumulating & Controlling Mechanism of Dominant Bacteria during 

Particle Enhanced biological Wastewater Treatment Particle Enhanced Bio-Reactor (PEBR) has been developed by the PI’s team for efficiently biological wastewater treatment, which leverages the suspended particles as the carrier to provide numerous surface area for the growing of bacteria, thus intensifying the biological treatment process and enhancing the performance of the system. 

The project is proposed to extent the application of this technology for the industrial wastewater containing high concentrations of COD and/or NH4-N. Ph.D. candidates will focus on detailing mechanisms of growing and accumulating of working bacteria on the surface of carriers and R&D of multi-functional particles, respectively 

Contact point

Dr. Yuanyuan Shao yuanyuan.shao@nottingham.edu.cn

Project 1: Integrated SCCO2 extraction and encapsulation process for oil powders

Oils are widely used in domains such as nutrition, cosmetics, and pharmaceuticals. Converting these oils into a powder means they can be incorporated into a wider range of formulations: capsules, pills, dried beverages, drink mixes, supplements, vitamins, and emulsions. Currently, spray-drying with acacia gum is already used in industry to produce a variety of oil powders. Microencapsulation using supercritical antisolvent process is being developed. This study aims to develop an integrated, cost-effective process enabling highly-efficient supercritical CO2 oil extraction as well as in-situ micro- and nanoencapsulation of bioactive oils. 

Project 2: Detoxication of Jatropha curcas seed oils for edible use

Jatropha curcas is a popular high-yielding oil plant in China. Its seed kernels contain nearly 60% oil, with the majority of the fatty acids being beneficial oleic acid and linoleic acid. However, because of the presence of toxic phorbol esters, it is no longer edible. As a result, successful detoxication of Jatropha curcas seed oils will significantly increase the edible-oil supply, which has significant socioeconomic implications. This study aims for the removal of phorbol esters in multiple stages, including pre-extraction, extraction, and oil refining processes, as well as the development of dedicated methods for ultimate removal for mouse tests. 

Contact point

Dr. Dongbing Li dongbing.li@nottingham.edu.cn

Project title: Comprehensive investigation on the deposition, dissolution, and absorption as well as in-vivo pharmacokinetics and pharmacodynamics of the inhalable macromolecules 

Biologics delivery by pulmonary route has been proven feasible and effective. As an alternative to the non-parenteral route, pulmonarytargeted delivery of biologics shows promising potential in improving bioavailability and sparing dose. However, the deposition, dissolution, and absorption processes of macromolecules has not been systemically investigated. 

Therefore, in this project, series of macromolecules with varied properties with/without molecular cargos for drug loading will be engineered into inhalable size for further in-vitro and in-vivo aerodynamic behaviour studies. 

Contact point

Dr. Yuanyuan Shao yuanyuan.shao@nottingham.edu.cn

Project title: Photopolymerization of fluorinated olefin in a Continuous vortex reactor 

Sustainability is one of major concern and bottleneck for the growth of chemical industry with increasing supervision of environment protection. The application of photochemistry to chemical processes is becoming attractive for both academic and industrial chemistry to reaching sustainability in this context as photons can be considered as “green” catalyst. However, some limitations of using photochemistry, such as short distance of light penetration, makes the scale-up of photochemical batch processes into industry more difficult. 

To overcome these shortcomings of photochemical batch processes, the flow chemistry, which allows continuous reactive manufacturing organics, become an alternative to traditional batch methods as it can offer alternative scalable routes by numbering up smaller footprint reactors, small volumes of the reaction mixture in reactor means that the build-up of reactive or hazardous intermediates can be controlled more easily than in batch. Moreover, flow chemistry can overcome the limitations of light penetration due to smaller volume, reduce reactor fouling and avoid over-irradiation. 

This project aims to develop a continuous photochemical technique for a polymerization process of synthesizing high-value functional polymer oil based on an advanced vortex chemical reactor developed by our research team. The polymerization process involves gas and liquid two-phase fluids. This continuous photopolymerization facility will consist of a few subsystems, including core reactor, light-source, condensation device and reagent feeding system, requiring high innovation to the structure design of the entire system. To industrialize this technique, comprehensive study via experiment and numerical simulation will be covered on characterizing the mass transfer rate of oxygen from gas to liquid, flow pattern of two-phase flow, coupling effect of heat and mass transfer on the conversation rate, and synergetic effect of irradiation, mass transfer and bubble features. Apart from the academic training through project, there is also great opportunity for this technique developed in this project to be validated on industrial-linked projects. 

Contact point

Guang Li: Guang.Li@nottingham.edu.cn