CDT React - Available Projects - Intake 2022
1 fibre to rule them all: massively parallel fibre-optic sensors for real-time reaction monitoring
Supervisors: Dr Ali Salehi-Reyhani, Prof Klaus Hellgardt
Abstract - Next generation synthesis requires next generation technology: we need better sensors to monitor, understand and optimize reactions at greater depth, and to do so rapidly and scalably. The aim of this project is to support the age of digital chemistry by developing an all-optical analytical platform that is able to capture chemical information the state of a reactor in real-time and use machine learning to guide synthesis. We propose to develop a massively parallel fibre-optic sensor incorporating multiple optical detection principles to achieve multi-parameter detection (UV-Vis-NIR spectroscopy, Raman, temperature, pH, turbidity).
Need: There already exist highly capable systems for reaction discovery (e.g. NMR and mass-spectrometry) but these aren’t suited for real-time reaction monitoring. Limited by current sensor design, workflows make do by incorporating many individual sensors. But the combination of bulky benchtop probes each to measure singular parameters in situ is cumbersome and inherently difficult to scale, being incapable of probing reduced volume systems such as microfluidics or flow-based reactors.
Complexity of the data: Acquiring and deconvoluting multiplexed optical signals is relatively straightforward; however, the proposed detection system will critically dependent on the automatic processing of high-dimensional chemometric datasets. Closing the ‘data-loop’ to guide control and optimization will be challenging given the high number of permutations in the choice of reactants and synthesis conditions and the curse of dimensionality prevents the full exploration of their chemical attributions. Thus, we will explore how machine guided reinforcement learning (neural nets) and robotic synthesis platforms can overcome this bottleneck for real-time process monitoring.
Sustainable Manufacturing of Platform Chemicals from Biomass
Supervisors: Dr Andy Ashley, Dr Chris Tighe
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 large amount 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. Furthermore, whilst the FTR has a good yield, the selectivity is poor, yielding a product with a wide distribution of hydrocarbon chain lengths.
The overall goal of this project is to develop and optimise a scalable combination of homogeneous ‘frustrated Lewis pair’ (FLP) catalyst and sustainable solvent, to selectively convert biomass-derived syngas, including the CO2 fraction, into platform chemicals. Multiple parallel batch reactors and a flow reactor, coupled with on- and off-line analytical methods, will be used to generate catalyst performance data; this will be analysed using e.g. kinetic modelling and/or artificial intelligence to elucidate information on the catalytic cycle, and off-cycle processes such as catalyst deactivation. This data-driven approach may be used to optimise the catalyst structure, and even to optimise the reaction conditions in flow, in real time, through the automated Labview interface.
The initial focus of the project will be on achieving high selectivity to ethene, which is readily converted into myriad products, such as polymers. 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.
An interpretable AI framework for chemical retrosynthesis
Supervisors: Dr Antonio del Rio Chanona, Prof Sophia Yaliraki, Prof Klaus Hellgardt
Synthesis planning is a research priority that lies at the interface between chemistry, chemical engineering, and computer science. This process is predominantly carried out via retrosynthetic analysis where the desired chemical compound is iteratively broken down into smaller precursors until available reactants (e.g. industrial platform chemicals) are found. Empowered by the unprecedented development of artificial intelligence and new computational capabilities, machine learning based retrosynthesis has become the new norm for discovery of novel molecules, materials, and drugs. This is mainly accomplished via natural language processing by converting organic chemicals into SMILE strings or other molecular fingerprints. Despite its huge success, this approach is data-driven, thus it does not provide additional physical insight into the underlying reaction system. As the reactivity and chemical properties of organic compounds are greatly determined by their functional groups, a more promising alternative is to develop an automated framework which breaks down the whole molecules into functional groups to enable physical knowledge informed retrosynthesis. This can be achieved by combining cutting-edge techniques such as graph neural networks, graph/network theory, and symbolic regression. Furthermore, wider metrics such as compound toxicity and price can be estimated along with reactivity in the same manner so that multi-criteria decision-making can be addressed to rank new synthetic pathways at an early research stage. Success of this project will therefore deliver an interpretable AI based innovative retrosynthesis framework which accounts for environmental and economic sustainability.
Porous Liquids for Catalysis
Supervisors: Dr Becky Greenaway, Dr Mark Crimmin, Prof Matthew Fuchter
Porous liquids (PLs) are an exciting new class of counterintuitive porous materials that combine the properties of a microporous solid with the mobility of a liquid. Like their solid counterparts, these liquids with holes have the capability to demonstrate gas uptake and release, and guest selectivity. However, unlike solid porous materials, they could also potentially have unique applications, such as the ability to be pumped around a continuous system facilitating guest loading/unloading steps, or being used as a gas-loaded reaction solvent. Despite this potential, the application of PLs in areas other than gas uptake and selectivity has not been investigated. Recently, we reported the formation of high cavity concentration PLs using porous organic cages (POCs) – discrete molecules containing permanent molecular cavities accessible through windows – and demonstrated their ability to exhibit increased uptake of a range of gases (e.g., CO2, CH4, Xe, SF6) over neat liquids (Nature, 2015, 527, 216; Chem. Sci., 2017, 8, 2640, ChemRxiv, 2021, DOI:10.26434/chemrxiv.14719503.v2). In addition, there have been reports of the use of solutions of cages for catalytic CO2 conversion into cyclic carbonates (Sustainable Energy Fuels, 2019, 3, 2567), and as nanoreactors (Chem. Soc. Rev., 2008, 37, 247). These systems differ from PLs, which are defined as having permanently empty pores that are accessible to guests, due to either guests being in competition with the solvent which occupies the cavities or guest binding being driven by a hydrophobic effect. In this project, we will build on these initial reports and explore different ways of applying PLs in the area of catalysis. This includes the use of gas-loaded PLs as a reusable and controllable external gas source in stoichiometric gas reactions, and as a solvent to carry out lower pressure gas reactions by ‘simulating’ high gas concentrations that are normally only achieved at higher pressures, and as nanoreactors to catalyse small molecule reactions by confinement. Once we have a thorough understanding of these different methodologies, we will then seek to translate this into a high-throughput workflow with the aim of being able to use PLs to rapidly screen a range of catalytic reactions involving the incorporation of a gas as a reagent.
Accelerating Net Zero Manufacturing with Intelligent Optical Reactor Technology
Supervisors: Dr Ceri Hammond, Dr Antonio Del Rio Chanona
Quickly transitioning to net zero manufacturing requires researchers to use modern methods for process discovery and development. This is especially true for the development of heterogeneously catalysed processes, which are still typically optimised by painstaking trial-and-error methods. As such, the overall objective of this project is to combine pioneering spectroscopy and reactor technology with machine learning and real-time optimisation to achieve self-optimising reactor technology applicable to the synthesis of important chemical products from biomass feedstock using heterogeneous catalysts.
We recently pioneered development of a novel heterogeneous catalyst reactor equipped with fiber optic technology. This reactor allowed us to follow the conversion of glucose to various bio-based chemicals of industrial interest using heterogeneous zeolite catalysts, at true continuous operational conditions and in real time, by performing operando optical spectroscopy. In particular, unique optical signals related to the active sites of the catalyst and all of the reaction pathways of the chemical process were identified, including the desired reaction pathway and those of undesired side reactions. Moreover, we could relate the intensity of each optical signal to the quantity of products formed, as verified by offline methods (HPLC and 1H-13C HSQC NMR), resulting in real time information of the performance of each catalyst in terms of activity, selectivity, and stability. As the reaction pathway signals are charge transfer bands associated with activation of the substrate by the catalyst (as opposed to chromophores of reactants and products), they also provide direct insight into the transition states of the various reaction pathways. These breakthroughs are unprecedented in catalysis research, and represent the starting point for this project.
This project will use this breakthrough to target the application of machine learning to heterogeneous catalysis and biomass conversion. In particular, we will combine fast data generation through operando optical technology with online learning algorithms to construct data-driven models in real-time through time series modelling. This will facilitate the development of ML algorithms that uniquely account for activity, selectivity and stability, at various operational conditions. We will validate this hypothesis, and then use the Intelligent Optical Reactor (IOR) technology to accelerate bio-based chemical manufacture by developing self-optimising reactor technology. In later stages, we will attempt to use the transition state insights provided by the reactor to generate advanced structureactivity relationships of relevance to future catalyst design.
A Computerized Chemist: Building an automated microfluidic reactor for the optimization of challenging organic transformations
Supervisors: Dr Chris Rowlands, Prof Chris Braddock
The student on this project will be working at the cutting edge of reprogrammable microfluidic reactor technology. They will be pioneering new types of microfluidic devices, particularly ones which can be used to mimic other devices or alter themselves on-the-fly. This flexibility will be used to optimize reaction conditions (temperature, pressure, mixing time etc.) in a reaction known as a relay cross metathesis (‘ReXM’). This 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. 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.
Moldel-based experiment design to minimize material use in developing models of solvation
Supervisors: Dr Claire Adjiman, Dr Amparo Galindo, Dr 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.
Data driven approach to elucidate the role of electrolyte in electrochemical dinitrogen reduction
Supervisors: Dr Ifan E Stephens, Prof Magda Titirici, Dr Huizhi Wang
Current ammonia synthesis, via the Haber Bosch process, produces >1% of global CO2 emissions, due to its reliance on methane-derived H2. There is a burgeoning interest in electrochemical N2 reduction to NH3 at room temperature and ambient pressures. Should the process be powered by renewable energy, it would enable sustainable NH3 production, revolutionising the fertiliser industry. Should the process be efficient enough, NH3 could become a CO2-free energy-dense sustainable fuel. Dr Ifan Stephens and colleagues recently provided the first quantitative proof that N2 electroreduction is possible under ambient conditions, using non-aqueous electrolytes consisting of THF, ethanol and LiClO4 (Andersen, S.Z. … Stephens, I.E.L et al, Nature 2019; Westhead O, Jervis, R., Stephens, I.E.L. Science 2021). Even so, the electricity-to-ammonia efficiency is only ~3%: there is ample room for improvement.
The aim of this project is to elucidate the role of the electrolyte in N2 reduction. We will screen a large number of different electrolytes (organic and ionic liquids) in parallel and use machine learning methods to analyse the ensuing data. The project will draw on knowledge from the adjacent fields of battery science and enzymatic nitrogen fixation. The ensuing insight will enable the design of more efficient electrolytes for this incredibly important reaction.
A Heterocycle Vending Machine: Self-optimising synthesis of heterocyclic fragments and lead-like compounds
Supervisors: Dr James Bull, Dr Phil Miller, Prof Alan Spivey, Dr Chris 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.
Automated NMR Data Analysis: A New Tool for Materials and Catalyst Discovery
Supervisors: Dr Kim Jelfs, Dr Becky Greenaway, Dr Mark Crimmin
There is an increasing drive to use new technologies, robotics and automation in chemical research. Automated platforms can allow thousands of experiments to be carried out within a very short time period. Characterisation data of the products of these reactions can also be rapidly collected using kit equipped with high-throughput autosamplers, including key analytical techniques such as mass spectrometry and NMR spectroscopy. However, the bottleneck in the discovery process then becomes the analysis of this huge amount of data – interpretation and assignment of this data is still largely carried out by a human, and the analysis of NMR spectra in particular is very time consuming and, arguably, tedious! This can lead to platforms lying idle for large periods of time while a human is analysing the outcome and determining which products were made. This project will focus on the development and application of software that automates the analysis of the NMR data collected during high-throughput screening workflows. Combining automated platforms with the developed software, including artificial intelligence algorithms, holds the promise of “closed loop” discovery where an initial set of reactions are analysed by software, and then based on the outcome, the algorithm decides on the next set of experiments to try and then run on the platform, for example to optimise the properties of a material or the selectivity of a catalyst. The approach developed will have wide applicability and will be tested against a variety of problems. The project will provide training in a range of experimental techniques, automation, scripting and artificial intelligence.
Semi-Hydrogenation Catalysis with Zn
Supervisors: Dr Mark Crimmin, Dr Phil Miller
Selective hydrogenation catalysis is a key technology in chemical manufacture. Hydrogenation is used to generate commercial intermediates and to fix relative and absolute stereochemistry in important molecules. The best and most widely applied homogenous catalysts all rely on precious metals (Ru, Rh, Ir). These are costly, have potential toxicity issues, and are of limited supply. There is a clear agenda based on sustainability, cost, and element security to move away from catalysts based on these metals. Catalysts based on main group elements (e.g. B, P, Si, Al, Mg–Ba) along with post-transition metals (Zn) are continually improving for hydrogenation applications. Despite the great strides that have been taken in this area, challenges remain. The incompatibility of chiral ligands so efficient for transition metal systems with main group elements has limited the advances towards developing asymmetric catalysts, in general systems that allow clear control over selectivity, be it chemoselectivity or stereoselectivity are uncommon. In this project, we will develop new zinc catalysts for the selective hydrogenation of alkynes, alkenes, nitriles and other polar substrates. Further, we will develop chiral catalysts for asymmetric hydrogenation of internal alkenes. We will use automation to screen new catalyst combinations and ultimately implement the best catalysts in a flow hydrogenation system.
Light mediated carbonylation reactions of energic organic molecules in flow
Supervisors: Dr Phil Miller, Dr James Bull, Dr Mark Crimmin
Flow chemistry approaches for light mediated reactions are now widely recognised as a viable way of improving method development, reaction screening and scale-up. The enhanced light penetration within the microchannels of flow reactors and their improved safety aspects (i.e. small inherent volumes) are their principal advantages over equivalent batch reactors. Carbonylation reactions are versatile reactions for the preparation of a wide range of organic carbonyl compounds. Conventionally, they are conducted using a transition metal catalyst at elevated temperatures and pressures in order to improve gas solubility and to facilitate CO insertion. This project will develop a novel flow-based system for conducting novel photocatalytic carbonylation reactions via a light triggered CO-releasing molecule and apply the system to reacting energetic diazo and azide compounds that can pose safety concerns in batch.
Molecular engineering of organic redox molecules for redox flow batteries
Supervisors: Dr Qilei Song, Dr Kim Jelfs, Prof Anthony Kucernak
Redox flow batteries (RFBs) are promising for grid scale energy storage with decoupled energy and power, high energy efficiency and long lifetime. Organic redox molecules have emerged as a new generation of sustainable materials for energy storage. However, there are still some scientific challenges including solubility, redox reaction stability and air stability, which remained to be improved. The current synthesis approach is still mainly based on trial and error. This research will combine computational and experimental approaches to accelerate the design and synthesis of organic redox molecules for battery energy storage. High-throughput computational screening and machine learning will be applied to accelerate the materials discovery, by studying the molecules in the chemical database and predict optimized structures and molecules with suitable properties. The modelling and prediction will be used to guide the synthesis and modification of molecules. The experimental data generated from the synthesis and reactions will be used to validate the computational predictions. The selected molecules will be screened in battery testing facility and data generated will be used to further guide the design and optimization of molecules, which ultimately will contribute to development of next-generation energy storage materials.
Project context and opportunities:
This project will be suitable for a chemistry student to develop interdisciplinary skills in computational design and modelling, chemical synthesis, electrochemistry and reaction characterization, battery technology, anddata analysis. The project is very well suited to candidates seeking to work across disciplines at the Chemistry - Chemical Engineering – Data interface. The successful candidate will have opportunity to interact with a wider collaboration network and industrial partners.
Accelerated Development of Cat@MOF catalysts for Industrially Relevant Hydrogenations
Supervisors: Dr Rob Davies, Dr Phil Miller, Dr Camille Petit
The development of highly porous metal organic frameworks (MOFs) for applications in areas as diverse as gas storage and separation, heterogeneous catalysis, and drug delivery has grown exponentially in recent years, and MOFs now constitute a highly active field of study. This project targets a novel family of Cat@MOF materials in which a homogeneous catalytic centre (Cat) is incorporated into the pores of a bespoke metal organic framework (MOF) material. These constructs can possess significant benefits over existing catalysts in term of improved activity, better/alternative selectivity, and ease-of-use/separation. The focus of the work will be on the design and development of novel hydrogenation catalysts for the hydrogenation of carbon dioxide and bio-derived and waste feedstocks to yield value-added chemicals. A range of synthetic inorganic, organic, and materials techniques will be applied to prepare the Cat and MOF components before combining them together to give the target materials. The new Cat@MOF constructs will be fully characterised and evaluated, with initial screening taking place using automated high throughput methodology. Ultimately the best performing Cat@MOF materials will be upscaled and built into new flow reactors.
Quantitative Ligand Parametrisation for Catalyst Design and Prediction for Ullmann Arylation by a Combined Experimental and Computational Approach
Supervisors: Dr Silvia Díez-González, Dr James Bull, Dr Kim 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.
Generation of Nitrous oxide (N2O, ‘laughing gas’) in a Flow Reactor (funded by BASF)
Supervisors: Prof Klaus Hellgardt, Prof Mimi Hii (Christian Holtze, BASF).
Nitrous oxide (N2O, ‘laughing gas’) is a widely used selective oxidant of hydrocarbons in a liquid phase (e.g. Cyclohexene to Cyclohexanone). Although 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 and the nitrous oxide must be compressed and cooled before storage can take place, prior to use. The goal of this project is to 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. The decomposition of ammonium nitrate will be at first 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. Further steps will include the design and optimisation of the decomposition reaction as well as demonstration of the cost-effectiveness of the approach and its direct use in exemplar oxidation steps.
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.
Machine-Learning-Driven Synthesis of the Next Generation Carbon Dots with Tunable Fluorescence/Band-Gap
Supervisors: Prof Magda Titirici, Dr Chris Tighe, Dr Kim Jelfs, Dr Suela Kellici (Southbank University)
Inorganic quantum dots exhibit interesting fluorescent and semiconductor properties and are currently explored in biological applications as bioimaging agents or in solar panels/photocatalytic processes, as well as in electronics displays. However, they are based on toxic metals (e.g. Cd, Pb), complicated synthetic processes and exhibit low stability when stored under normal atmospheric conditions. Biomass-derived carbon dots (CDs) have emerged as promising and sustainable candidates to inorganic quantum dots. Yet there remains grand challenges in the field, despite great progress made in the development of novel and sustainable carbon dots, in the engineering of their band gap to fit different requirements for visible light adsorption, as well as producing them with tunable fluorescent properties and high quantum yields. To address these challenges and produce carbon dots with tunable fluorescence and band gap, we propose to correlate reaction parameters in the preparation process of carbon dots to explore structure-properties relationship and potential applications. There is a plethora of experimental data (already existing in Titirici’s lab and wider literature) on the synthesis and properties of CDs, so we will apply machine learning (ML) for screening of high-performance materials, first to select diverse conditions under which to synthesise the CDs, and then to weight the importance of synthesis variables and to optimise the desired properties. In this project we will demonstrate how ML-based techniques can offer insights into the successful prediction, optimisation, and acceleration of CDs' synthesis processes and properties leading to emerging applications. A regression ML model on hydrothermally-synthesised CDs from various biomass precursors will be established to reveal the relationship between various synthesis parameters and experimental outcomes as well as enhancing the process-related properties such as the fluorescent quantum yield (QY) and tunable bandgap. The synthesis will first be done in batch, and then ultimately transferred to a flow reactor incorporating advanced process analytical technology (PAT), which will be used to characterise the carbon dots in-situ and tune their properties using computer control, supervised by the ML algorithm.
Expanding the photoswitch toolbox for light-addressable applications in catalysis, supramolecular chemistry and more.
Supervisors: Prof Matthew Fuchter, Prof Nick Long, Dr Becky Greenway
Photoswitches are molecules that are capable of being reversibly interconverted between (at least) two states by light irradiation. The incorporation of photoswitches into multifunctional systems has huge relevance for next-generation light-addressable applications that span catalysis, biology and materials science. The Fuchter group have pioneered the development of heteroaromatic azo switches (J. Am. Chem. Soc. 2014, 136, 11878; J. Am. Chem. Soc. 2017, 139, 1261) and so far employed them in photopharmacology (J. Am. Chem. Soc. 2020, 142, 17457) and energy storage (J. Am. Chem. Soc. 2020, 142, 8688) applications. Despite the promise of existing switches however, there remains a number of challenges to be solved in the area of switch design – from completeness of switching to red-light switching, which are often determined by the application in question and therefore critical to address. It is non-intuitive to identify a new switch structure that meets all the performance requirements for a given application, with many established photoswitches falling short of what is needed. Coupled to this, there is a potentially huge design space to navigate when it comes to homing in on improved azo switch designs. This project will harness the power of both computational and experimental high throughout discovery to develop tailored high performance heteroaromatic photoswitches. We will then seek to showcase the switches developed in two light-addressable applications: lactide polymerisation catalysis and incorporation into switchable porous materials. This will demonstrate the power of high throughput approaches in this exciting area of research.
Using water and electrons for green and sustainable reduction of C-O bonds.
Supervisors: Prof Mimi Hii, Prof Klaus Hellgardt
As we switch from petrochemical (mainly hydrocarbons) to bio-renewable (mainly oxygenated) feedstocks, safer and efficient methods to convert C-O bonds to C-H bonds will be an important part of the strategy towards a more sustainable future. While a number of electrochemical reactors have become commercially available in recent years, they are generally built for synthesis/reaction discovery, and are not designed for obtaining reaction data and understanding. In a previous project, we have constructed an electrochemical flow reactor that can be used to reduced amide bonds - one of the most stable C=O bonds found in nature.
In this project, we will aim to extend this to the reduction of other types of C=O compounds (aldehydes, ketones, acids and esters) using a data-guided approach.
A combined experimental and computational approach for the rational synthesis of rotaxanes as ‘smart’ drug delivery systems
Supervisors: Prof Ramon Vilar, Dr Kim Jelfs, Dr James E. M. Lewis
The efficacy of many drugs can be limited by undesirable properties such as poor aqueous solubility, low bioavailability, and “off-target” interactions in the body. To combat these deficiencies, various drug carriers have been investigated to enhance the pharmacological profile of therapeutic agents. In particular, there is growing interest in systems that have high degree of spatiotemporal control for the release of the drug to a specific target. However, such systems are scarce due to synthetic challenges and difficulties in applying them in vivo. We have recently developed a novel approach to control the interactions of a drug with its biomolecular targets as well as for its controllable delivery to cancer cells using external stimuli (Angew. Chem. Int. Ed. 2021, 60, 10928). This has been achieved by ‘caging’ the drug in a rotaxane – a type of interlocked molecule. This project will build on our initial results to develop a systematic approach for the modular caging of different drugs into rotaxanes. In the new assemblies, we will incorporate drug molecules that can target different biomolecules as well as ‘triggers’ to control their delivery to specific cells (e.g. cancer cells). To achieve these aims, the project will involve synthesis of sophisticated rotaxanes, testing the ‘uncaging’ mechanisms to target the desired biomolecules/cells and application of data-driven and computer modelling approaches for the rational design of the new systems.
Next generation antimicrobial surfaces: machine guided discovery of novel molecular films.
Supervisors: Prof Saif Haque, Dr Ali Salehi-Reyhani
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.
3D Printed Scaffolds for Catalytic Hydrogenation Reactions in Flow
Supervisors: Prof. George Britovsek (Chemistry), Dr. Billy Wu (Dyson School of Design Engineering), Dr. Peter Italiano (H2GO)
Heterogeneous catalysis is most commonly conducted in packed bed reactors, where liquid or liquid/gas streams are passed through a packed bed of catalyst in pellet or powder form. These packed bed reactors have challenges such as high pressure drops and temperature gradients. These issues are often difficult to control and can result in lower throughputs. Furthermore, the irregular temperature gradients or hotspots due to the inhomogeneity of the catalyst bed are difficult to remove when scaling to larger production, which can limit the overall conversion and the ultimate yield. In recent years, catalytic static mixers (CSMs, see picture) have been developed using structured catalysts based on 3D printing techniques to address the problems mentioned above associated with packed bed technology.1)
Additive manufacturing allows the design and manufacturing of catalyst supports with almost endless variation in structure. The scaffolds can be optimised in terms of surface area, whsv, mixing efficiency and stability. Common materials used for 3D printing are steel, aluminium and titanium and catalysts can either be coated directly onto the metal, or alternatively, an oxide support, for example alumina, can be used and the catalyst is deposited on the metal oxide layer by wet impregnation.
In this project, new CSMs will be designed using topological optimization approaches which consider multi-physics effects such as heat/mass transfer coupled with chemical kinetics (using COMSOL Multiphysics). A range of different architectures will be investigated, depending on the parameters that need to be optimised. 3D printing will be performed using different metals on a Concept Laser mLab cusing direct metal laser sintering machine, available in the Department of Mechanical Engineering at Imperial College. Here, laser sintering parameters can be controlled to introduce bulk porosity into the scaffold structures, with X-ray computed tomography used to measure the resulting microstructural properties of the reactors.
Biomass is generally oxygen-rich and removal of oxygen can be achieved either with hydrogen or CO.2,3 Here we will investigate hydrogenation of biomass-derived substrates,which will be carried out in flow. We will use a Phoenix Flow Reactor, available at ROAR, which allows easy variation of the reaction parameters such as temperature, pressure, flow rate etc. Hydrogenation reactions will be optimised in terms of activity and product selectivity.
1) CSMs: Johnson Matthey Technol. Rev., 2018, 62, (3), 350. Doi:10.1595/205651318x696846
2) Coskun, T., Conifer, C. M., Stevenson, L. C., & Britovsek, G. J. P.* (2013). Carbodeoxygenation of Biomass: The Carbonylation of Glycerol and Higher Polyols to Monocarboxylic Acids. Chem. Eur. J. 19(21), 6840-6844. doi:10.1002/chem.201203069
3) Vriamont, C. E. J. J., Chen, T., Romain, C., Corbett, P., Manageracharath, P., Peet, J., Britovsek, G. J. P. (2019). From lignin to chemicals: Hydrogenation of lignin models and mechanistic insights into hydrodeoxygenation via low-temperature C-O bond cleavage. ACS Catalysis, 9(3), 2345-2354. doi:10.1021/acscatal.8b04714
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.