Available projects

A data-led approach for mapping the lignin depolymerisation process

Supervisors: Prof Mimi Hii, Dr  Antonio del Rio Chanona, Prof Klaus Hellgardt

Lignin has been recognised as an important source of the next-generation sustainable fuels and high-value chemicals, which can contribute to reducing greenhouse gas emissions and our dependence on fossil resources. To facilitate the industrialisation of lignin for sustainable bio-production, new strategic approaches need to be developed to understand and predict the depolymerisation of the lignin mixtures (derived from different types of biomass). A promising option is the use of intelligent platforms that automate the design of experiments to explore process kinetics and self-optimise the process performance. With the fast development of computer-aided technologies, it is now possible to integrate experimental data with computational frameworks powered by optimisation and machine learning algorithms in a closed-loop fashion. This project will construct a mathematical mapping between lignin composition and depolymerisation reaction performance indices. The construction of this mapping will be carried out through a recursive design of experiments – verified using the high-throughput and kinetic capabilities of the ROAR facility. Success of this project will allow the establishment of an automated modelling and experimental framework to optimise valuable compound production by accounting for the varying composition of lignin at the production phase, which is one of the main barriers for its much wider use in sustainable production of biochemicals. 

A Heterocycle Vending Machine: Towards the autonomous and self-optimising synthesis of a heterocyclic screening collection of fragments and lead-like

Supervisors: James Bull,  Dr Philip Miller,  Prof Alan Spivey, Dr Christopher Rowlands

This project will develop a heterocycle ‘vending machine’ that will enable the diversity-oriented-synthesis of heterocycles in an autonomous and self-optimised fashion. By combining recent advances in light-activated fundamental organic transformations, flow chemistry technology and self-optimisation algorithms, a telescoped multi-step process will be developed for the synthesis of novel heterocyclic compounds. A variety of simple precursors will be combined through the same sequence via formation and reaction of diazo compounds, and sequential cyclisation. It is envisioned that these simple commercially available building blocks will be fed into the machine, reacted in a self-optimised process to produce high value heterocyclic compounds with potential applications in the pharmaceutical sector. This approach, will deliver varied substituted heterocycles on demand, avoiding many repetitive steps by being directly applicable across different substrates. Compounds will be targeted to be appropriate as fragments or lead-like compounds for screening against biological targets. The integration of in-line optical detection in the flow process will facilitate the generation of real-time continuous data that will be fed into a self-optimising algorithm enabling reaction parameters (light, reaction time/flow rate, heat etc.) to be changed ‘on the fly’ in order to map out the optimum reaction conditions for a specific reaction. 

A New Paradigm for Selective Bromination in Flow (funded by BASF)

Supervisors: Prof Klaus Hellgardt, Prof Mimi Hii (Christian Holtze, BASF).

Click here to read BASF Project Objectives

Bromination is a common enabling requirement in the synthesis of complex organic molecules to be used as actives in agrochemicals or pharmaceuticals. However, it entails two key challenges:

  • Handling and storage of elemental bromine is difficult, as it is toxic, corrosive and hazardous.
  • Controlling selectivity of the bromination reaction is frequently difficult and yet crucial for producing the desired product.

The approach of this project to overcome these challenges is to use flow chemistry to generate bromination reagents ’on-demand’ and utilise them directly in a reaction with the substrate to yield the desired product with good selectivity, e.g. controlling chemoselective and regioselective bromination of an aromatic ring. The benefits of flow chemistry are threefold: (i) unstable intermediates can be generated in-situ and deployed immediately in the reaction; thus minimizing hazardous inventory, (ii) larger amounts can be made as the process is easy to scale up, and (iii) the kinetic product can be favoured by controlling residence time and better heat management.

In the project the following aspects will be developed:

  • In-situ generation of the brominating species from non-hazardous precursors. This can be achieved by chemical means, electrochemical, or photochemical activation.
  • Design and setup of a sustainable flow chemistry methodology taking into consideration aspects relating to the reaction kinetics, mass and heat transfer, the selected reactor type and other unit operations, their sequence, safety, corrosion, and process control.
  • Operation of the setup to produce research amounts of material, and its characterization regarding selectivity and yield.
  • Scale up of the reactor/operation over longer periods to demonstrate delivery of several kilograms of the desired product.

Digital aspects of the project will involve modelling and design of the reactor, and optimization of compositional and processing parameters, through machine learning or design-of-experiment approaches, for example.

Project context and opportunities: 

The project is a Chemistry project with substantial Chemical Engineering and Data components. It is very well suited to candidates seeking to work across disciplines at the Chemistry - Chemical Engineering – Data interface. This project forms part of a wider suite of activities that BASF is supporting within the CDT. Click here to read BASF Project Objectives. The successful candidate will be able to interact with, and leverage the benefits of, this wider activity. There will be opportunities for placement(s) (total of up to one year duration) within the BASF organisation during the studentship period.

Accelerated Development of Cat@MOF catalysts for hydrogenation reactions

Supervisors: Dr Rob Davies, Dr Philip Miller, Dr Camille Petit

Catalytic hydrogenations have widespread industrial applications, and it is estimated that 10-20% of all reactions used today in the production of chemicals are catalytic hydrogenations. This project targets a new family of Cat@MOF catalysts for these important transformations, based upon the incorporation of homogeneous hydrogenation catalysts (Cat) into the pores of heterogeneous metal organic framework (MOF) materials. These constructs have been shown to possess significant benefits over existing catalysts in term of improved activity, better/alternative selectivity, and ease-of-use / separation. A large novel library of Cat@MOF materials will be assembled by combining existing homogeneous hydrogenation catalysts with well-known isoreticular MOF families (in which the metal and organic connectors can be readily modified whilst maintaining the same MOF topology and structure). These new catalysts will be initially screened and optimised using automated high throughput methodology. The best performing Cat@MOF materials will be upscaled and built into new flow reactors, with initial studies focusing upon their application for the hydrogenation of bio-derived and waste feedstocks such as levulinic acid and carbon dioxide to yield value-added chemicals.

Accelerating robotic molecular material discovery through data-led computation

Supervisors: Dr Kim Jelfs, Dr Becky Greenaway, Prof Claire Adjiman

Supramolecular synthesis is a powerful strategy for assembling complex molecules, but doing this by targeted design is challenging. It typically takes 1-2 years to synthesise and characterise a new system, and one can then find that the properties of the system are not those desired. Using automated synthesis can expand the number of systems that we can attempt to synthesise, but even then you are exploring ~100 systems at a time, compared to a near infinite search space from molecular organic building blocks. Worse still, the “hit” rate on a robotic platform for materials discovery is typically low (typically <10%, can range up to ~30% on rare occasions), with most reactions not resulting in the targeted product. This is inefficient, and wasteful, both in terms of time and consumable costs. In this project, we aim to fuse together computational discovery to allow us to explore much vaster regions of the chemical search space for molecular materials. We previously showed that a retrospective computational analysis of a robotic screen could predict particularly unsuccessful reaction combinations to avoid (Nature Commun.20189, 2849). This was achieved by assembling the potential products (for which we have automated in-house software available) and then calculating, at the density functional theory (DFT) level, their formation energies. The least energetically favourable reactions were the ones that should not have been tested on the robotic platform. Here, if we use this analysis in advance, we can identify the most promising reactions. To accelerate this process, and avoid the need for numerous computationally expensive DFT calculations, we will use design of experiment concepts to identify a set of structures for which to collect both experimental and computational data so as to maximise the information content of the data set and to train data-driven models to predict the success or failure of the reactions. The data set and models will continue to develop as the project progresses. This combined computational and experimental approach, with a feedback loop for the generated data will allow us to increase the success rate of robotic synthesis, something of much wider applicability. We aim to more than triple the success rate to more than two-thirds of reactions, producing more new material systems in a single robot screen than previously reported in the field.

Automated Late-Stage C–H Diversification of Complex Drugs and Natural Products (co-funded with The Janssen Pharma Companies of Johnson & Johnson)

Supervisors: Dr James Bull, Dr Becky Greenaway, (Eric Tan, Janssen)

Drugs are often highly complex molecules that require lots of resources spent in their design and development. The late-stage C–H functionalization (LSF) of drugs allows the modulation of the properties of highly functionalized molecules, without committing to de novo design and synthesis, allowing cost- and time-efficient production of highly potent analogues for different purposes. Currently, one major limitation in the field is that chemical reactions amenable to the LSF of C–H bonds are scarce, and in most cases allow the introduction of only one specific functional group. We propose in this project to use high-throughput reaction screening techniques to invent a new reaction that allows the introduction of a synthetic handle onto aromatic compounds. The reaction conditions must be developed in such way that most drugs can be functionalized and then diversified using modern technologies such as photoredox or electrochemical catalysis. Ultimately, we wish to implement an automated workflow in which a robot will use the reactions that will be developed to diversify and make analogues of lead compounds or commercial drugs. The project is co-funded by Janssen Pharmaceutica (Johnson&Johnson) and there is the possibility to undertake an industrial placement in Beerse (Belgium, main research site of Janssen) as part of the PhD programme. 

Automated Reaction Optimisation and Scale-up of Novel Oligo and Polythiophene Materials in Flow

Supervisors: Dr Philip Miller, Prof Martin Heeney

Oligo- and polythiophenes are interesting materials that have unique electrical and luminescent properties. These physical characteristics have led to their exploitation in the field of plastic electronics. The ability to tune their optical and electrical properties, and to easily process these materials into thin films has led to a number of commercial applications. They have also found novel applications as bioimaging agents, harnessing their luminescence and targeting behaviour for studying diseases such as Alzheimer’s. The 2000 Nobel Prize in Chemistry was awarded ‘for the discovery and development of conductive polymers’ based on such polythiophene materials. The synthesis of oligo and polythiophenes is typically achieved via transition metal-catalysed coupling of a metalated thiophene precursor (e.g. trialkylstannane or boronic acid/ester) with a thienyl halide. This route obviously generates waste streams of toxic alkyl tin or more benign boronic acid by-products, a problem that is further exacerbated on scaling-up these molecules for commercial applications. Such coupling reactions become more atom economical, greener and straightforward if the C-C bond formation is achieved directly by functionalisation of the heteroaromatic C-H bond. 

This project aims to exploit recent discoveries from our labs that enable the direct catalytic arylation of thiophene derivatives for the synthesis of novel oligomeric and polymeric heteroaromatic materials. A droplet flow-based approach will be used to screen reactions – mapping of reactivity space and generating a ‘feedback loop’ system via an in-line spectrometry and algorithm to control reaction parameters (temp., time, flow rate etc.). Flow chemistry will also be used to scale-up these reactions and to produce materials of the highest quality (purity, molecular weight, dispersity etc.). Novel materials will be tested for their for electronic and luminescence properties, and will find applications in the plastic electronics and imaging fields.

Capturing chemical intuition for machine-guided discovery of metallopharmaceuticals targeting cancer stem cells

Supervisors: Dr. Ali Salehi-Reyhani, Prof R Charles Coombes, (Dr. Kogularamanan (Rama) Suntharalingam , Prof Jacqui Shaw )

The ultimate aim of this project is to develop novel drug candidates that specifically target aggressive sub-populationof cancer cells. Using machine learning and robotic synthesis we will digitally capture chemical intuition and harness it for the synthesis and testing of a novel redox-active copper (II) metallopharmaceutical library. Need: Cancer relapse and metastasis is responsible for over 90% of cancer deaths [Seyfried et al., Crit Rev Oncog. 2013] and remains a major obstacle to beating cancer. Considerable effort has been put forward to understand the mechanisms of metastasis; however, despite significant advances in treatments, the onset of metastasis is practically terminal. Anticipating its emergence, developing adequate drugs and predicting drug resistance remains a serious and unmet challenge to the field. Complexity of the Problem: The standard stochastic model of cancer assumes that all cancer cells that constitute a tumour have equalpotential for malignancy. The hurdle to successful treatment then lies in being able to eradicate the entire cancer cell population and critically this must include cancer stem cells (CSCs). CSCs are a rare, but critically important, sub-population of cancer cells that, according to the CSC hypothesis, are the primary drivers of the development of the disease and evidence is mounting toward implicating their role in metastasis.[Yang et al, Chin J Cancer Res. 2015;] Traditional therapies have multiple limitations that result in treatment failure such as the limited selectivity of agents leading to local toxicity affecting healthy tissue. These limitations are compounded when targeting CSCs due to their slow rate of division, high expression of drug-efflux pumps and a high capacity for DNA repair. The therapeutic potential of metal complexes in cancer therapy has attracted a lot of interest. Platinum drugs, such as cisplatin, carboplatin and oxaliplatin, are the modern mainstay of the metal-based compounds inthe treatment of cancer. These do not target CSCs and so resistance is often developed. Research programs aimed at discovering anti-CSC agents have largely focused on biologics and purely organic molecules. However, a recent string of high-profile clinical failures have prompted a need for new drug development strategies.[Garber, Nat. Rev. Drug Discovery 2018] Solution: Recently, the Suntharalingam group showed that a family of redox-active copper (II) complexes with phenanthroline-based ligands and nonsteroidal anti-inflammatory drugs (NSAIDs), are capable of potently and selectively killing breast CSCs.[Boodramet al. Angewandte 2016;Eskandariet al. Chemical Science 2019]These are a promising class of compounds that are currently undergoing extensive animal studies and have attracted considerable industrial interest. Further synthetic investigations are required to identify optimal drug candidates. However, given the high number of permutations in the choice of substituent ligandsand synthesis conditions,the curse of dimensionality prevents the full exploration of their chemical space using traditional benchtop approaches. In this project, we seek to use robotic synthesis platforms and machine-guided exploration to develop the next generation of these compounds.

Dual Catalytic C–H Alkylation: An automated approach to reaction discovery (co-funded by AstraZeneca)

Supervisors: Dr James Bull,  Prof Alan Spivey, Dr Becky Greenaway

This research will develop the palladium-catalysed alkylation of C–H bonds using alcohols as the alkylating reagent. C–H functionalisation promises significant advantages in the construction of complex molecules avoiding pre-functionalisation. To date, C–H alkylation is less developed than other C–H functionalisation processes, and commonly relies on toxic alkyl halides. Here, a dual catalytic system will be developed to employ alcohols as alkylating agents. This work will expand the currently limited scope of  C–H alkylation reactions, and avoid the requirement for activated alkyl halides. Reaction development (hit finding) and optimisation (DOE) will be performed with the aid of automation (Freeslate platforms in ROAR), to rapidly scope out the reaction space. Successful reactions will then be studied in detail for further insights. This project is co-funded by and in collaboration with AstraZeneca, and may include the possibility to undertake a placement.

Generation of nitrous oxide (N2O) via thermal decomposition of ammonium nitrate in a flow reactor (funded by BASF)

Supervisors: Prof Klaus Hellgardt, Prof Mimi Hii (Christian Holtze, BASF).

Click here to read BASF Project Objectives

Goal: Discover and develop the generation of N2O in a liquid phase, based upon the thermal decomposition of ammonium nitrate in an inherently safe flow reactor system.

Description: Nitrous oxide (N2O, ‘laughing gas’) is a widely used selective oxidant of hydrocarbons in a liquid phase (e.g. Cyclohexene to Cyclohexanone). The generation of nitrous oxide by thermal decomposition of ammonium nitrate is a well-known process. Due to safety concerns, the reaction is currently carried out under ambient pressure, thus generating gaseous N2O. The nitrous oxide must be compressed and cooled before storage can take place, prior to use. In this project, the decomposition of ammonium nitrate will be carried out under high pressure (e.g. 100 bar), which can be performed safely using a flow-reactor to control the hazards of thermal runaway, thus enabling ‘on-demand’ generation of N2O for immediate use.

 Tasks and deliverables:

  • Design of a small, and highly flexible, supply option for nitrous oxide in a liquid phase. As part of this:-
    • Design an intrinsic safe decomposition step
    • Determine if chloride can be used as catalyst for the decomposition reaction
    • Integrate the preheater, reaction zone and final cooling into one flow reactor suite.
  • Demonstrate that the pressurized liquid nitrous oxide stream can be directly fed into exemplar oxidation steps (no N2O storage required).
  • Demonstrate the lower investment cost requirement due to avoidance of N2O compressor

Project context and opportunities: The project is a Chemical Engineering project with substantial chemistry components. It is very well suited to candidates seeking to work across disciplines at the Chemical Engineering – Chemistry interface. This project forms part of a wider suite of activities that BASF is supporting within the CDT (Click here to read BASF Project Objectives).  The successful candidate will be able to interact with, and leverage the benefits of, this wider activity. There will be opportunities for placement(s) (total of up to one year duration) within the BASF organisation during the studentship period.

High-Throughput Screening for Molecular Organic Fullerene Encapsulants

Supervisors: Dr Becky Greenaway, Prof Matt Fuchter, Dr Kim Jelfs

Fullerenes have widespread uses in biomedical and materials applications, for example, they are commonly used in organic photovoltaic devices (OPVs). The encapsulation of fullerene in supramolecular complexes, such as molecular organic cages formed using dynamic covalent chemistries (DCvC), could allow for the fine-tuning of properties and also enable the selective formation of fullerene adducts. Additionally, fullerenes have previously been shown to act as a template and drive supramolecular assembly towards a desired topology. However, the design of such systems is challenging – not only does the cage need to form using DCvC, the cavity size must be large enough to host a fullerene molecule, and the binding must be favourable in order to form a stable supramolecular complex. If you also take into account the numerous precursor combinations and the different species they could form by self-assembly, the search space is too vast to explore empirically. We will employ a combined experimental and computational approach to narrow the search space, rapidly screen combinations and complex formation using high-throughput automation, and for any complexes formed, investigate their properties and applications. We will also study successful fullerene-organic cage complexes in more depth using computational modelling, and therefore use the experimental findings to inform the design of new complexes with improved properties in a closed feedback loop.

Machine guided optimization of organic semi-conductor films for light activated antimicrobial and antiviral surfaces

Supervisors: Prof  Saif Haque, Dr. Ali Salehi-Reyhani, Dr Andrew Edwards, Prof Nicholas Harrison

Antimicrobial resistance (AMR) and the spread of infection is an area of grave concern globally and needs urgent attention. We are in the midst of COVID19 but how do we halt its progression and prepare for or even prevent the next pandemic? Infection mediated by contaminated surfaces is critical to the spread of disease. In fact, studies have so far shown that microbes and viruses, including SARS-CoV-2, can survive on various surfaces for up to a week. This project focusses on the development of light-activated antimicrobial surfaces based on novel solution-processable nanocomposite materials. We ask the following question: Do our light-activated antimicrobial surfaces provide a feasible mechanism of infection control? In this project, we will use robotic synthetic chemistry and machine learning approaches to develop the next generation of LAMS (Light-activated Antimicrobial Surfaces) to destroy microbes on surfaces and limit the spread of infection. More specifically, we aim to use the powerful combination of robotic aided synthetic chemistry, machine-guided learning and materials design to accelerate the development of these novel antimicrobial coatings on a timescale not possible using classical approaches to chemistry. The execution of this project will lead to a novel technology that will form part our arsenal to limit or even halt the progression of future pandemics and tackle the increasing existential threat of antimicrobial resistance.

Model-based experiment design to minimize material use in developing models of solvation

Supervisors: Prof Claire Adjiman, Prof Amparo Galindo, Prof Alan Armstrong

In liquid phase reactions and other liquid phase processes such as crystallization or liquid-liquid separation, solvation properties play a critical role as the choice of solvent can have an impact on reaction rate, affecting free energy barriers as well as the maximum concentration (solubility) that can be achieved, on regioselectivity or enantiomeric excess, and on partition coefficients, amongst many other factors. During the development of pharmaceutical or agrochemical processes, the identification of an appropriate solvent or solvent mixture can be a painstaking process and can require significant amounts of material to perform experiments in different media and at different conditions. The aim of this project is to effect a substantial reduction in the amount of material needed and in the time required to develop predictive models of solvation applicable to a particular substrate or reaction. We will achieve this by developing a novel model-based methodology to design high-throughput experiments (e.g., in a few solvents at different temperatures) with maximum information content so that the model can be developed successfully. We will apply this methodology to the prediction of solubility and selectivity. We will verify the predictive capabilities of the proposed models by conducting experimental campaigns in a larger set of solvents and we will refine the methodology on this basis.

Optimisation of Frustrated Lewis Pair (FLP) Catalysts for the Hydrogenation of Pharmaceutical Intermediates

Supervisors: Dr Andy Ashley, Dr Chris Tighe, Prof Matt Fuchter.

Catalytic hydrogenations represent one of the most important families of all chemical transformations, and are employed at all scales of chemical production. These catalysts, however, are predominantly based on rare, toxic, and expensive transition metals (e.g. Rh, Ru, Pd, Pt), which present particularly challenging problems during the synthesis of drug molecules that necessitates stringent and costly purification techniques. In recent years a new and exciting chemical methodology using catalysts based on inexpensive and abundant main group elements has been discovered. Known as ‘frustrated Lewis pairs’ (FLPs), these consist of a p-block centred Lewis acid [typically B-based e.g. B(C6F5)3] and a Lewis base which (for steric and/or electronic reasons) cannot interact strongly with one another; this leads to unquenched reactivity that can be exploited for the reaction with H2, which can subsequently be delivered to substrates via catalytic polar (H+ + H) hydrogenation. However, FLPs are currently less tolerant to moisture and have a slower intrinsic rate than traditional hydrogenation catalysts, hindering their industrial application. This project aims to understand and improve the catalytic performance and industrial scalability of FLP-based hydrogenations to provide a practical alternative to transition metal-based catalysts. To achieve these goals, the project will involve close collaboration between the Departments of Chemistry and Chemical Engineering. You will learn how to synthesise FLP catalysts and use a high pressure, stirred reactor to measure intrinsic reaction kinetics. In-situ spectroscopic techniques (e.g. FTIR, Raman) and ex-situ analysis (e.g. NMR) will be used together with advanced chemometric methods. These results will underpin a rational approach to understanding reaction mechanisms, design and optimisation of the catalyst.

Quantitative Ligand Parametrisation for Catalyst Design and Prediction for Ullmann Arylation by a Combined Experimental and Computational Approach

Supervisors: Dr Silvia Diez-Gonzalez, Dr James Bull and Dr Kim E Jelfs

In the synthesis of complex molecules, the ability to react already functional group rich compounds presents an ongoing challenge. This is particularly true in catalytic reactions, and is a major objective in the development of improved base metal mediated reactions. We are developing copper-catalysed transformations using bidentate ligands, containing an N-heterocyclic carbene (NHC) and a hemilabile second coordination site, which are highly effective in O-arylation reactions. To enable the design of improved ligands and ensure their broad applicability and efficiency, we aim to model these new ligands and the ligand-Cu-X species using DFT calculations to classify and parametrise the ligands.

Overall, this work will provide a quantitative insight to ligand properties, as well as develop a suite of copper catalysts to form aryl–O, aryl–N and aryl–CF3 bonds with high functional group tolerance. Innovative ligand design, incorporating strongly coordinating NHCs, enhances catalytic ability and leads to improved tolerance of coordinating and polar functionality, which are commonly a limitation. Preliminary results in this collaborative work have proven that high yields can be achieved at very low metal loadings (≤0.5 mol %). Moreover, there are clear variations in the ligand performance with structure, but no straightforward trends.

This project will undertake the first computational parametrisation of this ligand framework to directly feed into the development of upgraded catalysts. This will be validated against experimental data (kinetic analysis and isolated complexes) and used to generate predictive tools for ligand design. The interpretation will exploit existing kinetic data for C–O coupling, as well as generate new data for analysis through the ROAR screening, kinetics and analytical capabilities. The developed understanding will support further catalyst design, and enhance yield, efficiency, and functional group tolerance. Specifically, this approach will be applied to discover new catalytic systems for N-arylation reactions and the trifluoromethylation of aryl halides, as important medicinally relevant motifs.


Rational Design and Scale-up of Photoreactors (funded by BASF)

Supervisors: Prof Klaus Hellgardt, Prof Mimi Hii (Christian Holtze, BASF).

Click here to read BASF Project Objectives

Photochemical activation of chemical reactions promises complementary synthetic pathways to thermally activated processes, which can be much more energy-efficient, and sustainable. Moreover, they can be more atom economic and reduce the required number of synthetic steps (step economy). With respect to chemistry, in many cases they offer superior selectivity over thermally activated processes.

Nevertheless, and despite the fact that there has been research on photochemistry for one more than 150 years, few photochemical processes are used on an industrial scale. The most important reason is the cost associated with inefficient light-sources and relatively high prices of electrical energy compared to thermally activated processing alternatives. As a consequence, photochemical reactor technology has been neglected. Currently most industrial processes are operated in sub-optimal reactors, where space-time-yields are compromised by the fact that the illuminated volume is small compared to the reactor volume.

We believe that the key paradigms that have led to this situation are changing

  • with the advent of efficient LED-light sources
  • the need for changing from fossil fuels to green electricity
  • and the advances in flow chemistry, which promises a seamless R&D workflow starting at a small lab-scale with relatively predictable scaling criteria.

Therefore, we face the need for catching up with the development and engineering of bespoke equipment for carrying out photochemistry on an industrial scale. This research project will tackle the key challenges by elucidating the fundamental concepts for target-oriented photoreactor design. To this end we will use

  • Cutting-edge modelling of photoreactors with respect to spatially resolved photon intensity, fluid dynamics, and chemical reactions
  • Experimental validation using advanced methods of characterization including stopped flow and periodic operation
  • Design of experiments and automated testing

Building on the modelling capabilities and the developed understanding, the project will aim at the following aspects in its second phase:

  • Rational design of a scalable reactor concept
  • Construction of a lab-scale reactor
  • Testing, validation, and parameter optimization in the reactor
  • Benchmarking against commercially available reactor solutions

Project context and opportunities: 

The project is a Chemical Engineering project with substantial Modelling and Data components. It is very well suited to candidates seeking to work across disciplines at the Chemistry - Chemical Engineering – Data interface. This project forms part of a wider suite of activities that BASF is supporting within the CDT.  Click here to read BASF Project Objectives. The successful candidate will be able to interact with, and leverage the benefits of, this wider activity. There will be opportunities for placement(s) (total of up to one year duration) within the BASF organisation during the studentship period.

Synthesis of Platform Chemicals from Biomass-Derived Syngas using Frustrated Lewis Pair (FLP) Catalysts

Supervisors: Dr Chris Tighe, Dr Andy Ashley

The manufacture of platform chemicals from sustainable feedstocks is highly desirable. Ligno-cellulosic biomass (e.g. wood and agricultural waste) is a potentially renewable source of some of the carbon and hydrogen required to meet the global demand for platform chemicals, without requiring the use of land, which would otherwise be used to grow food. This biomass can be readily converted by a process known as gasification to a so-called syngas, containing a mixture of H2, CO and, owing to the high oxygen content of biomass, a significant fraction of CO2. Synthesis gas, derived mainly from fossil fuels, is already converted on an industrial scale to methanol, and to long chain hydrocarbons and oxygenates by the Fischer-Tropsch reaction (FTR). Any CO2 in the syngas does not participate significantly in the FTR, thus the carbon in CO2 is not utilised. Whilst the FTR has a good yield, the selectivity is poor, yielding a product with a wide distribution of hydrocarbon chain lengths. The aim of this project is to develop and optimise a scalable combination of homogeneous ‘frustrated’ Lewis pair (FLP) catalyst and solvent, to selectively convert biomass-derived syngas, including the CO2 fraction where possible, into platform chemicals. Such novel, selective catalysts would be much better utilised when applied to the targeted synthesis of high value platform chemicals subsequently used to make long-lived products, which sequester carbon, rather than much lower value transport fuels typically produced by the FTR, which are quickly burned, releasing more CO2 into the atmosphere and contributing to global warming. Consequently, the initial focus of this project will be on developing FLP catalysts selectively producing ethene, which is readily converted into myriad products, such as polymers. To achieve these goals, the project will involve close collaboration between the Departments of Chemistry and Chemical Engineering. You will learn how to synthesise FLP catalysts and use a combination of a continuous, automated gas mixing system and stirred high pressure reactor to measure the intrinsic reaction kinetics and selectivity, including quantitative online analysis of the gaseous products by FTIR and mass spectrometry. These measurements will be used to elucidate reaction mechanisms and optimise catalysts for yield and selectivity with respect to the target molecules.

The Computerized Chemist: Building an automated microfluidic reactor for the optimization of challenging organic transformations

Supervisors: Dr Christopher Rowlands, Prof Christopher Braddock

This project combines a new automated microfluidic reaction platform with relay cross metathesis (‘ReXM’), a cutting-edge but sensitive and unoptimized synthetic route to biochemically-useful terpenoids, which are a class of compounds accounting for ~60% of all known natural products. The automated reaction platform consists of a computer-controlled valve and heater array, and by actuating these, the chip can be reconfigured to mimic any other microreactor, allowing the reaction conditions (temperature, reaction time, stoichiometry, etc.) to be altered as desired. In this project, the chip will be used to automatically optimize ReXM, producing a powerful method for producing arbitrary terpenoids. Ultimately this combination of automated optimization and synthetic utility will yield a ‘dial-a-terpenoid’ system, where the desired reaction is optimized on-the-fly to yield the desired product.

Trifluoromethylation using Copper-based Reagents and Catalysts (co-funded by Syngenta)

Supervisors:  Dr Rob Davies and Prof Chris Braddock, (Dr David Sale, Syngenta)

The late-stage incorporation of fluorine into drug or agrochemical targets is highly desirable due to the unique pharmacological properties conferred by fluorine such as enhanced lipophilicity, bioavailability, and metabolic stability. However examples of well-performing catalytic systems for trifluoromethylation, a key step in the synthesis of many of these target molecules, are rare and a mild copper-catalysed system using fluoroform remains elusive. This project focusses upon the development of privileged copper-based catalytic systems for trifluoromethylation. A Quality-by-Design approach will be used based on obtaining improved understanding of the mechanism including the identification and characterisation of catalytic intermediates, kinetic profiling, and studies on the function of the ancillary ligand and the role of off-cycle events including catalyst deactivation. The project is funded by Syngenta and there is the possibility to undertake an industrial placement as part of the PhD programme.