MSDE Symposium 2023: Frontiers in Molecular Engineering

MSDE Symposium 2023: Frontiers in Molecular Engineering 18 May 2023, London and online

18 May 2023, London and online MSDE Symposium 2023: Frontiers in Molecular Engineering #MSDE

Book of abstracts

Registered charity number: 207890

Introduction

Dear Colleagues, A warm welcome to the MSDE Symposium 2023: Frontiers in Molecular Engineering. Organised by Molecular Systems Design and Engineering , this event aims to discuss how molecular engineering approaches are driving significant breakthroughs across a broad range of research disciplines and applications, with a particular focus on the future of molecular engineering. This is the first in-person/hybrid iteration of this symposium since the pandemic and a great opportunity to engage with the journal’s community both in-person and virtually with our international community. We hope the hybrid nature of this event makes this symposium as inclusive as possible. The focus of this symposium is to highlight how molecular engineering is contributing to some of the biggest global challenges of our time. We have invited international experts in their area of research who will be covering topics from sustainable polymers and challenges towards net zero to machine learning approaches for molecular design and molecular design for addressing biomedical challenges. This event provides an opportunity for our valued community members to discuss and explore our most important challenges. A panel discussion on the future of molecular engineering will be a fantastic opportunity for the audience to engage with our panelists about ways in which molecular engineering can contribute to addressing current challenges. We hope you will take part in these sessions and share your experiences with us! Over the last several years, MSDE has rapidly become one of the most important publication venues for research in molecular engineering. The symposium will provide a way for our wide community to get in touch with the Editorial Board members, journal editors and fellow researchers across a broad range of topics in molecular design and engineering. It’s will also be a great way to learn more about the journal and hear about exciting new projects to look out for. We strongly encourage delegates to raise questions to our speakers and poster presenters during the discussion sessions and throughout our dedicated poster sessions. We’d like to thank each of the speakers, poster presenters and participants for all their contributions. Again, welcome to what promises to be an exciting symposium. We hope that this event will act as a springboard for future activities and that it will help in fostering new research collaborations.

Dr Maria Southall Executive Editor, MSDE Royal Society of Chemistry

Professor Juan de Pablo University of Chicago

Editor-in-chief MSDE

Meeting information

MSDE Symposium 2023: Frontiers in Molecular Engineering is sponsored by the journal Molecular Systems Design & Engineering of the Royal Society of Chemistry. This e-book contains abstracts of the 24 posters presented at MSDE Symposium 2023: Frontiers in Molecular Engineering . All abstracts are produced directly from typescripts supplied by authors. Copyright reserved. All sessions, including the posters, are available to access via the virtual lobby. Further information on how to join the meeting and best practice for an online event is detailed in the joining instructions. Oral Presentations and Discussions All delegates at the meeting, not just speakers, have the opportunity to make comments or ask questions during the sessions. If you would like to ask a question during the discussion type ‘question’ or ‘comment’ into the chat box at the relevant point during the session. Networking sessions There will be regular breaks throughout the meeting for socialising, networking and continuing discussions started during the scientific sessions. During the networking sessions you will be able to join existing networking rooms or initiate one-to-one chats. Existing networking rooms will be visible from the virtual lobby. To create a one-to-one chat, simply click on the name of the person you would like to speak to and select if you would like to have a private or public conversation. For a public conversation, other delegates can join your chat room. You can participate in the networking sessions with other delegates in the InEvent app. On the web version, you can only be in one session at a time (this includes networking rooms). Posters The online poster session will take place on Wednesday 17 May and can be accessed from the online platform. Please refer to the programme for timings. It is an opportunity for delegates attending online to present their poster and discuss it with other delegates. Poster sessions will take place on Wednesday 17 May for online posters and during the lunch session at 11.45 for those attending in person. Please refer to the programme for timings and follow signage at the venue. Poster Prize A Poster prize will be awarded to the best poster as judged by the committee.

Scientific Committee

Claire S Adjiman Imperial College London, United Kingdom Linda Broadbelt Northwestern University, United States Luke Connal Australian National University, Australia Andrew Ferguson University of Chicago, United States LaShanda Korley University of Delaware, United States

Yongye Liang Southern University of Science and Technology, China Juan de Pablo University of Chicago, United States Anja Palmans Eindhoven University of Technology, Netherlands Robert Riggleman University of Pennsylvania, United States Patrick Stayton University of Washington, United States

Speakers

Linda Broadbelt Northwestern University, United States

Linda Broadbelt is Sarah Rebecca Roland Professor in the Department of Chemical and Biological Engineering and the Associate Dean for Research of the McCormick School of Engineering and Applied Science at Northwestern University. She was Chair of the Department of Chemical and Biological Engineering from 2009-2017. She was also appointed the Donald and June Brewer Junior Professor from 1994-1996. She has completed the short course Business for Scientists and Engineers through the Kellogg Graduate School of Management. Her research and teaching interests are in the areas of multiscale modeling, complex kinetics modeling, environmental catalysis, novel biochemical pathways, and polymerization/depolymerization kinetics. She served as the Past Chair, Chair, First Vice Chair and Second Vice Chair of the Catalysis and Reaction Engineering Division of AIChE, and also previously served on the Executive Board of the National Program Committee of AIChE. She is currently an Associate Editor for Industrial &Engineering Chemistry Research. Her honors include selection as the winner of the R.H. Wilhelm Award in Chemical Reaction Engineering from AIChE, the E.V. Murphree Award in Industrial Chemistry and Engineering from the American Chemical Society, the Dorothy Ann and Clarence Ver Steeg Award, a CAREER Award from the National Science Foundation, and an AIChE Women’s Initiative Committee Mentorship Excellence Award, selection as a Fellow of the American Association for the Advancement of Science, a Fellow of AIChE, and a Fulbright Distinguished Scholar, appointment to the Defense Science Study Group of the Institute for Defense Analyses, and selection as the Su Distinguished Lecturer at University of Rochester, Ernest W. Thiele Lecturer at the University of Notre Dame and the Allan P. Colburn Lecturer at the University of Delaware. broadbelt.northwestern.edu/members.html#LBroadbelt

Luke Connal Australian National University, Australia

Luke Connal is a Senior Lecturer at the Research School of Chemistry at the Australian National University (ANU) where he is an ANU Futures Fellow. His research program is in the design of advanced polymeric materials for applied systems. He has been recognised by numerous awards such as the ACS Chemical and Engineering News Talented 12. chemistry.anu.edu.au/people/academics/prof-luke-connal

Speakers

Marc-Olivier Coppens University College London, United Kingdom

Marc-Olivier COPPENS is Ramsay Memorial Professor in Chemical Engineering at UCL, since 2012, after professorships at Rensselaer and TU Delft. Having served as Head of Department of Chemical Engineering for 8 years, he is, since 2021, Vice-Dean for Engineering (Interdisciplinarity, Innovation) at UCL. He directs the UCL Centre for Nature-Inspired Engineering (CNIE), which was granted “Frontier Engineering” (2013) and “Progression” (2019) Awards by the UK’s Engineering and Physical Sciences Research Council (EPSRC). He is most recognised for pioneering nature-inspired chemical engineering (NICE) over the past 25 years and developing a systematic nature- inspired solution methodology to accelerate innovation and address Grand Challenges, associated to sustainable development. He has published >170 peer-reviewed journal articles and has delivered >50 plenaries, keynotes and named lectures. He is Fellow of IChemE, AIChE, RSC, Corresponding Member of the Saxon Academy of Sciences (Germany), Qiushi Professor at Zhejiang University (China), Scientific Council Member for IFP Energies nouvelles (France), and serves on advisory and editorial boards, including Editor-in-Chief of Chemical Engineering & Processing: Process Intensification. www.ucl.ac.uk/nature-inspired-engineering/people/leadership-team/prof-marc-olivier-coppens

Heather Kulik Massachusetts Institute of Technology, United States

Professor Heather J. Kulik is a tenured Associate Professor in the Department of Chemical Engineering at MIT. She received her B.E. in Chemical Engineering from the Cooper Union in 2004 and her Ph.D. from the Department of Materials Science and Engineering at MIT in 2009. She completed postdoctoral training at Lawrence Livermore and Stanford, prior to joining MIT as a faculty member in November 2013. Her research in computational catalysis and materials science has been recognised by a Burroughs Wellcome Fund Career Award at the Scientific Interface, Office of Naval Research Young Investigator Award, DARPA Young Faculty Award and Director’s fellowship, NSF CAREER Award, the AAAS Marion Milligan Mason Award, the Journal of Physical Chemistry Lectureship and a Sloan Fellowship in chemistry, among others. cheme.mit.edu/profile/heather-j-kulik/

Speakers

Niall Mac Dowell Imperial College London, United Kingdom

Niall is a Professor in Energy Systems Engineering at Imperial College London. He is a Chartered Engineer, a Fellow of both the IChemE and the Royal Society of Chemistry. His research is focused on understanding the transition to a low carbon economy, and has published more than 200 peer-reviewed scientific papers, technical reports, and books in this context. Niall has more than a decade’s experience as a consultant to the public and private sectors. He has worked with a range of private sector energy companies, and recently completed a two-year secondment to the UK Government Department BEIS (now DESNZ) where he acted as an expert policy advisor on CCUS and GGR. Niall is a member of the Scientific Advisory Board of TotalEnergies, the Norwegian CCS Research Centre (NCCS), and Joule. He was a member of the US National Petroleum Council (NPC) CCUS Roadmap Team, as well as the technical working group of the Zero Emissions Platform (ZEP), the Carbon Capture and Storage Association (CCSA), and is a science advisor to the venture capital fund, Carbon Direct. A multi award winning scientist, Niall was awarded the Qatar Petroleum medal for his research in 2010 and the IChemE’s Nicklin and Junior Moulton medals for his work on low carbon energy in 2015 and 2021, respectively. www.imperial.ac.uk/people/niall

Robert Riggleman University of Pennsylvania, United States

Robert Riggleman is an Associate Professor of Chemical and Biomolecular Engineering at the University of Pennsylvania. He earned his PhD in Chemical and Biological Engineering from the University of Wisconsin-Madison in 2007, which was followed by postdoctoral appointments at the University of Wisconsin-Madison (2007-2008) and the department of Chemical Engineering at the University of California-Santa Barbara (2009-2010). His research group develops and implements advanced molecular modeling techniques to study soft matter systems like polymers and glassy materials. The group explore dynamics and thermodynamics of these systems across a variety of time and length scales to help explain and predict experimental findings. rrgroup.seas.upenn.edu/members/robert-riggleman/

Speakers

Patrick Stayton University of Washington, United States

Patrick Stayton is Washington Research Foundation Professor and Director of the Molecular Engineering and Sciences Institute at the University of Washington. He is the founding Director of the Institute for Molecular Engineering and Sciences, and the Center for Intracellular Delivery of Biologics. His research group works at the interface of fundamental molecular science and applied molecular bioengineering. Studies are aimed at elucidating the basic principles underlying biomolecular recognition, and connected projects applying these principles to medical applications in the drug delivery, medical diagnostics, and regenerative medicine fields. He has also been awarded the 2009 Faculty Research Innovation Award, UW College of Engineering, and the Distinguished Teacher and Mentor Award from the Department of Bioengineering. bioe.uw.edu/portfolio-items/stayton/

Poster presentations

IP01

Synthesis and Characterisation of Asymmetric Perylene-based Supramolecular Polymers Helal Alharbi University of Bristol, United Kingdom Development of thermodynamically-consistent machine-learning Equations of State: Application to the Mie fluid Gustavo Chaparro Imperial College London, United Kingdom Development of a Cross-Reactive Fluorescent Sensor Array for the Detection of Liver Fibrosis Ross Gillespie University of Glasgow, United Kingdom Advanced Featurization and Characterization of MOF Pores for Adsorption Applications Arun Gopalan University of Manchester, United Kingdom Exploring the influence of chemical modifications on the formation of perylene-based SMPs Maximilian Hagemann University of Bristol, United Kingdom Evaluation of Polymer-Calcite Interfacial Strength Through a Uniaxial Tensile Simulation Study Keat Yung Hue Imperial College London, United Kingdom Effect of reversible binding on self-assembly and phase separation in coacervate blends Zuzanna Jedlinska University of Pennsylvania, USA

IP02

IP03

IP04

IP05

IP06

IP07

IP08

Prediction of the Interfacial Tension of Sugar-Based Surfactants Through Molecular Modelling

Muhammad Ariif Hafiizhullah Kamrul Bahrin Imperial College London, United Kingdom

IP09

Fast and accurate classifier-based surrogate models ensuring miscibility in optimisation-based design of solvent mixtures Tanuj Karia Imperial College London, United Kingdom Design of optimal solvents for CO 2 chemical absorption: the effect of simultaneous evaluation of molecules and process performance Ye Seol Lauren Lee Imperial College London, United Kingdom

IP10

IP11

Modelling ZIF interfaces in polymer composites Jasmine Lightfoot University of Bath, United Kingdom

IP12

Experimental screening of transition metal-doped TiO2 for hydrogen production through photoreforming of methanol Ruiman Ma University of Sheffield, China Solubility prediction of paracetamol in polyvinyl pyrrolidone using the SAFT-γ Mie Group-Contribution approach Shubhani Paliwal Imperial College London, United Kingdom Nature-Inspired 3D Scaffolds to improve T-Cell Culturing Environments for Adoptive Cell Transfer Cancer Immunotherapy Lucy Todd UCL, United Kingdom

IP13

IP14

Online posters

V01

Characterizing peri-Condensed Polybenzenoid Hydrocarbons using Deep-Learning Shany Erez Technion, Israel Obtaining Parameters for Equation of State Modeling of Bitumen Thermodynamic Properties Michael Greenfield University of Rhode Island, USA Local free energy landscapes of photoisomerization in polymer matrices Gustavo Perez Lemus The University of Chicago, USA Influence of N-substitution on the properties of Bicyclooctadiene/ Tetracyclooctane photoswitch for Molecular Solar Thermal Energy Storage Akanksha Sangolkar National Institute of Technology, Warangal, India Exploring the Depolymerization of an Intrinsically Recyclable Hyperbranched Polyester Using Density Functional Theory Calculations Alexander Shaw Northwestern University, USA

V02

V03

V04

V05

V06

Manufacturing “living” 3D structures Christoph Spiegel Heidelberg University, Germany

V07

Low-cost machine learning prediction of excited state properties of iridium-centered phosphors Gianmarco Terrones Massachusetts Institute of Technology, USA Comparison of different force fields in the determination of the excess chemical potential of thiophene in the [C4MIM] [BF4, Cl, Br, CH3COO] ILs Marco Vinicio Velarde Salcedo Universidad Autonoma del Estado de Mexico, Mexico

V08

V09

MM/PBSA binding free energy calculations of heparin binding domain of fibronectin with self-assembled monolayers

Viswanath Vittaladevaram University of Galway, Ireland

V10

PySAGES: Flexible, Efficient, GPU-Accelerated Sampling Methods Pablo Zubieta The University of Chicago, USA

Synthesis and characterisation of asymmetric perylene-based supramolecular polymers Helal Alharbi University of Bristol, United Kingdom Supramolecular polymers (SMPs) are a unique class of materials, which are held together by non-covalent and highly directional interactions. SMPs and conventional polymers possess common properties, such as low viscosity. However, owing to the reversibility of non-covalent interactions, SMPs exhibit significantly more dynamic behaviour, enabling them to display various properties such as self-healing, processability, and stimuli- responsiveness. 1 Perylene diimides (PDIs) have attracted attention as building blocks for SMPs through solution self-assembly. PDIs feature a range of attractive properties, including optoelectronic properties, that enables the investigation of their self-assembly, thermal and photochemical stability, easy chemical modification, and widespread potential applications. 2 Modification on the imide position is one of the strategies for designing novel functional PDIs. 3 Asymmetric PDIs with different substituents at each imide position are an essential subgroup within this class of compounds owing to their substantial synthetic versatility, 4 and have not been explored in great details for their self-assembly behaviour. In this work, we aim to explore the formation of SMPs based on asymmetric PDIs by targeting the imide positions on perylene tetracarboxylic dianhydrides. To facilitate the synthetic process, we applied a recently developed one- step technique developed by Hawker and co-workers, which uses a stoichiometric mixture of various amines in a one-step reaction. 5 Different asymmetric PDIs have been successfully synthesised using a variety of hydrophilic/ hydrophobic amines. We expect to design novel SMPs based on asymmetric PDIs and explore their self- assembly mode, behaviour and resulting supramolecular structures. References 1. T. F. A. De Greef, M. M. J. Smulders, M. Wolffs, A. P. H. J. Schenning, R. P. Sijbesma and E. W. Meijer, Chem. Rev., 2009, 109, 5687–5754. 2. C. Jarrett-Wilkins, X. He, H. E. Symons, R. L. Harniman, C. F. J. Faul and I. Manners, Chem. - A Eur. J., 2018, 24, 15556– 15565. 3. R. S. Wilson-Kovacs, X. Fang, M. J. L. Hagemann, H. E. Symons and C. F. J. Faul, Chem. – A Eur. J., 2022, 28, e202303443. 4. H. Wang, H. Liu, J. Wang, Z. He, Z. Zhang, E. He, R. Zhang and H. Zhang, Tetrahedron, 2016, 72, 7076–7080. 5. M. J. Robb, B. Newton, B. P. Fors and C. J. Hawker, J. Org. Chem., 2014, 79, 6360–6365.

IP01

Development of thermodynamically-consistent machine-learning equations of state: application to the Mie fluid Gustavo Chaparro, Erich A. Müller Imperial College London, United Kingdom The accurate description of thermophysical properties of fluids is crucial for design and engineering purposes. From a practical perspective, these properties are often correlated (and sometimes predicted) employing analytical empirical closed-form mathematical expressions denoted as equations of state (EoS). Historically, it has been considered that the inclusion of physically-sensible depictions (e.g. molecular interactions) into the EoS will improve both the robustness and reliability. These more theoretical approaches come at the expense of a long and sometimes cumbersome development. An example worth mentioning is the Statistical Associating Fluid Theory of Variable Range employing a Mie potential [ U=C ε ( (σ/r) λr - (σ/r) λa ) ], a.k.a., SAFT-VR-Mie [1] which is currently the most reliable EoS for this fluid and is capable of mapping back to molecular simulations results. In this contribution, a change in paradigm for developing EoS [2] is explored, where an artificial neural network (ANNs) is trained to “learn” the Helmholtz free energy of the Mie fluid, providing for a thermodynamically consistent data-driven EoS. Thermophysical properties of the Mie fluid are obtained using high-throughput Molecular Dynamics (MD) simulations. The LAMMPS software [3] is used to perform all simulations. The simulations are run with a cut-off of 5σ with no tail corrections and expressed in reduced Lennard-Jones units (i.e., k b =1, ε=1 and σ=1). The MD simulations consider Mie fluids with a repulsive exponent (λ r ) within the range 7-34 and an attractive exponent (λ a ) fixed to 6. The simulated phase space considers dimensionless densities (ρ * ) from 10 -4 to 1.2 and dimensionless temperatures (T * ) from 0.6 to 10.0. The computed thermophysical properties are used to train the residual Helmholtz free energy model of the Mie fluid (FE-ANN EoS). The proposed formulation incorporates physical insights, e.g. analytically fulfilling Maxwell’s relations and the ideal gas law. This data-driven model is trained using first- and second-order derivative information, such as the compressibility (Z= P*/ ρ * T * ), internal energy (U * ), isothermal compressibility (κ T ), isochoric heat capacity (C v * ), thermal expansion coefficient (α P ), heat capacity ratio (γ= C p * /C v * ) and Joule- Thomson coefficient (μ JT * ). The FE-ANN EoS is implemented using TensorFlow [4]. The trained FE-ANN EoS accurately describes the target thermophysical properties of the Mie fluid. Moreover, the physical-inspired formulation of the FE-ANN EoS allows computing other properties for which the model has not been trained for. For example, even though the model is trained using only PVT data, the model discovered -on its own- an unstable van der Waals loop, making it capable of computing vapour-liquid equilibria. The FE-ANN EoS results are on par with those of SAFT-VR-Mie, (arguably the best model for the Mie fluid) but were obtained in a span of a few months as opposed to the decades of development needed for the SAFT-VR-Mie EoS. References

1. Lafitte, et al. (2013). J. Chem. Phys., 139(15), 154504. 2. Rosenberger, et al. (2022). Phys. Rev. E, 105(4), 045301. 3. Thompson, et al. (2022). Computer Phys. Comm., 271, 108171. 4. Tensorflow (2005).www.tensorflow.org/ (accessed 6th October 2022)

IP02 IP02

Development of a cross-reactive fluorescent sensor array for the detection of liver fibrosis Ross Gillespie, Francesco Zamberlan, Liam T Wilson, William J Peveler University of Glasgow, United Kingdom Liver disease ranks as one of the leading causes of death in the modern day, but unlike the other leading causes, mortality rates of liver disease are increasing. [1] The common end stage for almost all liver disease is fibrosis and then cirrhosis, and a key reason for difficulty in tacking liver disease is that the current diagnosis techniques fail to effectively detect the early stages of liver fibrosis. Recent progress has highlighted that cross-reactive sensors, that operate on the principle of the mammalian nose or tongue, can successfully monitor fibrotic disease in patient blood samples, rather than relying on any one specific biomarker. [2,3] This offers a hope of early detection, but in order to improve the early detection possibilities of such a sensing array, a ‘semi-targeted’ or selective approach, that discriminates whole families of particular biomarkers but still retains a degree of cross-reactivity, may be a powerful tool. [4]

We have engineered a modular molecular architecture that can be synthesised with a variety of targeting ‘warheads’ and fluorescent reporters to create our selective sensing arrays. In particular we have focused initially on detecting and discriminating matrix metalloproteases (MMPs) as markers of fibrogenesis and glutathione S-transferases (GSTs) as markers of the initial signalling pathway. The changes in the total amount, as well as the ratios of different members of these protein families is indicative of the different stages of fibrosis. The warheads are sensitive to a wide variety of members of each protein family, giving cross-reactivity, but still targeting each family selectively. The warhead is appended to a responsive fluorophore that indicates a binding event with the proteins of interest. We present the successful synthesis of our sensing array components and promising initial data demonstrating the detection of target proteins in serum and the discrimination of members of those families. References 1. British Liver Trust. The alarming impact of liver disease in the UK. 2019

2. Rotello, V.M. et al. Adv. Mater. 30, 1-6. 2018 3. Rotello, V.M. ACS Sensors 1, 1282–1285, 2016 4. Marguiles, D. et al. Nat. Nanotechnol. 12, 1161–1168, 2017

IP03 IP03

Advanced featurization and characterization of MOF pores for adsorption applications Arun Gopalan 1 , Kaihang Shi 2 , Randall Q. Snurr 2 , Lev Sarkisov 1 1 University of Manchester, United Kingdom, 2 Northwestern University, U.S.A State-of-the-art approaches to the computational discovery of MOFs can be thought of in terms of three key components. 1) Representation : Network of atoms and bonds, a connected string of building blocks[1], etc. 2) Prediction : Molecular simulations, ab initio calculations, machine learning, etc. 3) S tructure sampler/generator : Databases like CoRE-MOFs [2] , ToBaCCo [3] , etc. or, optimizers based on Genetic Algorithm [4] , Bayes [5] , etc. How we represent materials is vital to all three steps. While the popular string and graph representations of MOFs primarily gear towards the chemical structure, adsorption properties correlate better with the characteristics of the 3D confining environment, which is unique to each MOF [6]. Hence, we present ‘pore graphs’ which are flexible enough to retain any cavity information relevant to the specific application, while being easy to compute, store and retrieve when used in characterization or machine learning problems. The void space in a material is converted into a network of interconnected pockets, using 3D image segmentation [7] . For example, let’s look at the pore graph workflow applied to Cu-BTC (HKUST-1) shown below.

Breaking down the void space into constituent pockets allows us to get the most out of existing pore descriptors. As examples, solutions to two challenging characterization problems are illustrated here:

• Detect pore windows (internal and periodic to the unit cell) • Detect the types of pockets (of different sizes/shapes)

Apart from characterization, pore graphs can be annotated with any node (pocket) or edge (connection) attribute (LCD, window size, energy histogram, etc.) and fed into graph ML algorithms to predict complex adsorption properties that are often inaccessible to simpler architectures, without having to work with cumbersome 3D images [8] . To summarize, pore graphs help us understand the cavities in a material better by representing it as a network of interconnected pockets. This reveals the underlying similarities in pockets, helps us solve many challenging characterization problems, and opens the possibility of adsorption property prediction via graph ML algorithms [9] . References 1. Bucior et al. , Cryst Growth Des, 2019, 19 , 6682–6697. Chung et al. , J Chem Eng Data, 2019, 64 , 5985–5998. Anderson et al. ,Crystengcomm, 2019, 21 , 1653–1665. 2. Lee et al. , ACS Appl Mater Inter, 2023, 13 , 23647–23654. 3. Taw et al., Adv Theory Simulations, 2022, 5 , 2100515. Bucio et al. , Mol Syst Des Eng, 2018, 4 , 162–174. Soille et al. , Signal Process, 1990, 20 , 171–182. Hung et al. , J Phys Chem C, 2022, 126 , 2813–2822. 4. Zhou et al. , AI Open, 2020, 1 , 57–81.

IP04

Exploring the influence of chemical modifications on the formation of perylene-based SMPs Maximilian Hagemann and Charl Faul University of Bristol, United Kingdom Supramolecular polymers (SMPs) are an exciting class of materials with a wide range of new properties owing to their dynamic behaviour. A current challenge in the field is to fully control self-assembly behaviour and therefore the properties of the resulting SMPs. The self-assembly behaviour of SMPs can be influenced by a variety of external forces, such as selective chemical modification of the monomers. Hereby, modification of the perylene core (bay, ortho and/or carbonyl positions) are especially interesting, causing not only changes of the stacking behaviour and self-assembly mode of the resulting polymers, but also modifying the optoelectronic properties. 1 Gaining control over those modification can be used as a tool to tune the properties of monomers and SMPs towards their desired application, e.g., for photovoltaic applications. Here we show initial steps towards the synthesis of different perylene SMP monomers with various chemical modifications. Multiple routes of modification are optimised and presented. Following these approaches we are optimistic to obtain novel SMPs, based on chemically modified perylene diimides (PDIs) for further analysis and studies, with control over structure and function. References 1. H. E. Symons, M. J. L. Hagemann, R. L. Harniman and C. F. J. Faul, J. Mater. Chem. C , 2022, 10 , 2828–2837.

IP05

Evaluation of polymer-calcite interfacial strength through a uniaxial tensile simulation study Keat Yung Hue 1 , Myo T. M. Maung 2 , Omar K. Matar 1 , Paul F. Luckham 1 , Erich A. Müller 1* 1 Imperial College London, United Kingdom 2 PETRONAS Research Sdn. Malaysia In energy applications, when the carbonate rock is weak and poorly consolidated, the hydrocarbon extraction process produces undesired particles which will impact on hydrocarbon productivity and increase environmental waste. Solids production control is essential to mitigate the problem. The solids production risk can be reduced by theinjection of formation-strengthening chemicals into the formation. In this research, molecular dynamics (MD) simulation was employed to screen different polymer candidates. This can be evaluated from the stress-strain response and the adhesion properties from a surrogate polymer-calcite uniaxial tensile simulation study. Classical atomistic MD simulations and pcff+ forcefield were employed to model the interaction of the polymer with the calcite (1 0 4). Polyacrylamide-based polymers were selected as potential candidates, including pure polyacrylamide (PAM), hydrolysed polyacrylamide (HPAM 33%) and sulfonated polyacrylamide (SPAM 33%) while polyethylene (PE) was modelled as control case. For each separate case, the polymer matrix was filled between the upper and lower solids surface layer, each consisted of 3 calcite layers. The polymer was allowed to move freely as deformable region, where the upper layer was pulled upward in z -direction and the polymer deformed slowly. The stress-strain response was measured from the normal force acting on the calcite lower layer normalized by the surface area, and the polymer strength properties were analysed. In a separate simulation, the polymer matrix was frozen, and the stress-strain behaviour was measured with both the polymer and calcite upper layer detached from the lower calcite surface completely. The stress-strain response behaviour is depicted in Figure 1 , while the polymer deformation behaviour can be observed in the simulation snapshot in Figure 2 . For the deformable polymer cases, in general, when the calcite upper layer is pulled upward, the polymer reaches peak tensile stress and decreases drastically due to the damage in the polymer matrix. Preliminary studies shows that the peak stress value recorded for the polymer- calcite system is the same as pure polymer system, which suggests the value is attributed to pure polymer tensile strength. Hence, the frozen polymer cases are modelled to force the detachment of the polymer from the interface, where the tensile stress recorded is considered as interfacial strength from literature[1]. The maximum tensile stress in Figure 1 shows that polymer-calcite interfacial strength is significantly stronger than pure bulk polymer strength, validating the polymers can adsorb strongly to the calcite. As shown in Figure 2 , HPAM33% shows better polymer strength due to the presence of carboxylate group in ionized form, which agrees with literature[2]. Uniaxial tensile simulation studies allow the interpretation of the polymer deformation mechanisms and provide insights into the polymer-calcite interfacial strength analysis. It serves as a useful screening simulation tool to evaluate the suitable potential candidates for improved adsorption and formation strengthening performance.

IP06

Effect of reversible binding on self-assembly and phase separation in coacervate blends Zuzanna Jedlinska and Robert Riggleman University of Pennsylvania In this work, we investigate how reversible bonding modulates self-assembly and phase separation in polymer blends. We perform simulations of coarse-grained models of polymers using the Theoretically Informed Langevin Dynamics (TILD) method. The TILD method is a hybrid particle/field approach where explicit coordinates of the molecules are retained and used to calculate bonded interactions while non-bonded forces (e.g. electrostatics) are calculated by mapping the particles to a density field which leads to a significant speed-up compared to MD implementations. All simulations are performed using our in-house, GPU-accelerated software, MATILDA. FT [1], which is available open-source on GitHub (rar-ensemble/MATILDA.FT). We model polymers as discrete Gaussian chains, with only a fraction of monomers "active" monomers being capable of forming bonds. We study two types of systems - one having only one type of active monomers, and another with two active monomer species. Monomers which belong to different binding types cannot cross-bind, which enables the system to undergo orthogonal phase separation. We investigate the effect of the fraction of active monomers and the energy associated with bond making/breaking on the properties of the coacervates. We quantify the density of the resulting coacervate phases, analyze their connectivity (fraction of bonded monomers and the the number of unique binding partners), and the MSD of the polymer chains allows us to detect the onset of gelation. In addition, in the systems capable of orthogonal phase separation, we quantify the extent to which two phases separate. We study, how these quantities are affected when either excess salt is introduced into the system, or when monomer- monomer repulsion is varied. We anticipate that our results will be useful in the molecular design of orthogonal phase-separating materials, which could be particularly important in controlling the formation of biomolecular condensates. References 1. Jedlinska, Zuzanna M., Christian Tabedzki, Colin Gillespie, Nathaniel Hess, Anita Yang, and Robert A. Riggleman. “MATILDA.FT, a Mesoscale Simulation Package for Inhomogeneous Soft Matter.” arXiv, https://doi.org/10.48550/ arXiv.2302.02474.

IP07

Prediction of the interfacial tension of sugar-based surfactants through molecular modelling Muhammad Ariif Hafiizhullah Kamrul Bahrin 1 , Harry Cárdenas 1 , S. Shahruddin 2 , Jofry B. Othman 2 , Andrés Mejía 3 , Omar K. Matar 1 , Erich A. Müller 1* 1 Imperial College London, United Kingdom, 2 Specialty Chemical Technology, PETRONAS Research Sdn. Malaysia, 3 Universidad de Concepción (UdeC), Chile Sugar-based surfactants (SBS) are amphiphiles with potential applications in various industrial settings. The large industrial-scale production of these “green” surfactants has become possible due to the availability of feedstocks synthesised from biomass through biorefineries [1] . As opposed to petrochemical surfactants, SBSs are carbon- neutral, non-toxic, potentially cost-effective, and can be suitably tailored by modifying their morphology [2] . A typical SBS is composed of a polar head derived from glucose, a linker and a hydrocarbon (alkane) tail (see Figure 1). There is, however, a large and diverse chemical ‘space’ to be explored if one is to design surfactants for specific applications, as small differences in the morphology of these components (e.g., length of tails) will affect the surfactant properties [3] . Experimental screening of candidate surfactants is costly and slow due to the prior need to synthesise and purify prototypes chemically. In silico calculations employing molecular dynamics (MD) simulations can rapidly assess interfacial properties over a wide range of operating conditions. A molecular modelling approach is presented by predicting the surface tension of non-ionic alkylpolyglucoside (APG 12 ) on the water/air interface. Canonical all-atom MD simulations are performed. The simulations consist of roughly 12000 molecules of water and a variable amount of surfactant molecules ranging from 25 to 170 APG 12 molecules. The air is represented by a vacuum in which specific water molecules can escape from the liquid phase at random. Each state point is run for a minimum of 12 ns at 298.15 K with a time-step of 1 fs using the pcff+ force field. Adsorption data obtained from the MD simulations, relating the interfacial tension, γ, (or surface pressure, Π) with surfactant surface coverage, Γ, are used to build a 2D simulation-based equation of state. The equation of state is used to derive a model for the surfactant excess chemical potentials at the interface. The adsorption isotherm of APG 12 (the γ – bulk concentration, c, curve) is determined from the derivation of a thermodynamic relationship that incorporates a free energy transfer. An enhanced sampling method is used to compute the free energy change associated with transferring a single surfactant molecule from the bulk solution phase to the interface, which is one of the inputs to the model. Figure 1 shows the predicted and the experimental interfacial tensions as a function of the bulk concentration, c. While experimental data (including ours)have uncertainties, it is seen that the simulation predictions are quantitatively correct. One notes that the pcff+ force field is parameterised to match bulk properties of organic moieties and, as such, was not specifically designed to reproduce interfacial properties accurately. A framework to predict adsorption isotherms of non-ionic surfactant solutions is presented, which is based solely on the chemical structures of the surfactant molecules and devoid of any empirical fitted parameters.

IP08

Fast and accurate classifier-based surrogate models ensuring miscibility in optimisation-based design of solvent mixtures Tanuj Karia, Benoît Chachuat, Claire S. Adjiman Imperial College London, United Kingdom Solvent mixtures have the potential to achieve better processes (1) and products (2) relativeto using pure solvents. Given the large number of possible mixtures, it is desirable to use a systematic optimisation-based framework for solvent mixture discovery, such as Computer-aided mixture/blend design (CAM b D) (3). A key consideration during the design of solvent mixtures is to ensure that the designed mixture exhibits a single, stable liquid phase at the conditions of interest. Checking for stability is a challenging problem, which is often tackled using simplifying assumptions (4), to make the optimisation model is tractable (5,6). This can be complemented by a rigorous stability check post-optimisation, (7,8). This, however, entails re-solving the optimisation model specifically when the designed mixture is found to be immiscible (9) and often results in lower mixture performance. In this work, we propose to embed an artificial neural network-based classifier-surrogate model in the CAM b D framework to capture the phase stability constraint. The performance of the classifier surrogate is tested on two CAM b D case studies (10,11) for solvent design. Using these case studies, we demonstrate the effectiveness of the surrogate classifier in enabling the in silico design of miscible mixtures without the need for re-optimisation. Finally, the use of the proposed surrogate classifier provides the probability of miscibility as a practical, interpretable metric. References 1. Chai S, Song Z, Zhou T, Zhang L, Qi Z. Computer-aided molecular design of solvents for chemical separation processes. Current Opinion in Chemical Engineering 2022;35:100732. 2. Enekvist M, Liang X, Zhang X, Dam-Johansen K, Kontogeorgis GM. Computer-aided design and solvent selection for organic paint and coating formulations. Progress in Organic Coatings 2022;162:106568. 3. Karunanithi AT, Achenie LE, Gani R. A new decomposition-based computer-aided molecular/mixture design methodology for the design of optimal solvents and solvent mixtures. Ind Eng Chem Res 2005;44(13):4785-4797. 4. Smith JM, Van Ness HC, Abbott MM. Introduction to Chemical Engineering Thermodynamics. McGraw-Hill; 2001. 5. Buxton A, Livingston AG, Pistikopoulos EN. Optimal design of solvent blends for environmental impact minimization. AIChE J 1999;45(4):817-843. 6. Dahmen M, Marquardt W. Model-based formulation of biofuel blends by simultaneous product and pathway design. Energy Fuels 2017;31(4):4096-4121. 7. Michelsen ML. The isothermal flash problem. Part I. Stability. Fluid Phase Equilib 1982;9(1):1-19. 8. Baker LE, Pierce AC, Luks KD. Gibbs energy analysis of phase equilibria. Society of Petroleum Engineers Journal 1982;22(05):731-742. 9. Conte E, Gani R, Ng KM. Design of formulated products: a systematic methodology. AIChE J 2011;57(9):2431-2449. 10. Jonuzaj S, Akula PT, Kleniati P, Adjiman CS. The formulation of optimal mixtures with generalized disjunctive programming: A solvent design case study. AIChE J 2016;62(5):1616-1633. 11. Watson OL, Jonuzaj S, McGinty J, Sefcik J, Galindo A, Jackson G, et al. Computer aided design of solvent blends for hybrid cooling and antisolvent crystallization of active pharmaceutical ingredients. Organic Process Research & Development 2023;25(5):1123-1142.

IP09

Design of optimal solvents for CO 2 chemical absorption: the effect of simultaneous evaluation of molecules and process performance Ye Seol Lauren Lee, Amparo Galindo, George Jackson and Claire S. Adjiman Imperial College London, United Kingdom The search for new solvents for carbon dioxide (CO 2 ) chemical absorption is imperative to achieve high energy efficiency and economic and environmental performance [1]. However, identifying the most promising solvent candidates remains challenging as it is necessary to explore a vast chemical space that entails mixed-integer decisions and capture a highly nonlinear interplay between the molecular properties and process performance [2]. In this study, a robust computer-aided molecular and process design (CAMPD) framework that enables the simultaneous design of optimal aqueous amine solvents and CO 2 chemical absorption-desorption processes is presented. New feasibility tests [3,4] integrated with an outer-approximation algorithm [5] are proposed to provide a reliable way to remove infeasible molecules and process conditions before solving the full process optimization problem. The efficiency of the proposed CAMPD algorithm is demonstrated by the successful completion of 150 optimization runs. The comparative study in which the relative performance of the optimal solvents obtained by applying two conventional CAMPD approaches shows that the proposed CAMPD approach leads to more promising solvents. References 1. Bui, M., Adjiman, C. S., Bardow, A., Anthony, E. J., Boston, A., Brown, S., Fennell, P. S., Fuss, S., Galindo, A., Hackett, L. A., et al., 2018. Carbon capture and storage (CCS): the way forward. Energy & Environmental Science, 11(5), 1062–1176. 2. Adjiman, C.S., Galindo, A. and Jackson, G., 2014. Molecules matter: the expanding envelope of process design. In Computer Aided Chemical Engineering (Vol. 34, pp. 55-64). Elsevier. 3. Gopinath, S., Jackson, G., Galindo, A., Adjiman, C.S., 2016. Outer approximation algorithm with physical domain reduction for computer-aided molecular and separation process design. AIChE Journal 62, 3484–3504 4. Lee, Y.S., Galindo, A., Jackson, G. and Adjiman, C.S., 2023. Enabling the direct solution of challenging computer-aided molecular and process design problems: Chemical absorption of carbon dioxide. Computers & Chemical Engineering, p.108204 5. Fletcher, R. and Leyffer, S., 1994. Solving mixed integer nonlinear programs by outer approximation. Mathematical programming, 66(1-3), pp.327-349.

IP10

Modelling ZIF interfaces in polymer composites Jasmine Lightfoot, Sharifah Alkandari 1 , Bernardo Castro Dominguez University of Bath, United Kingdom

Although polymer films are widely used in gas barrier and separation technologies, pure polymeric materials are limited due to a trade-off existing between gas selectivity and permeability. One option to enhance separation performance is through the addition of filler particles, which can absorb penetrating molecules or increase the effective diffusivity of certain gases. In our research group, we have demonstrated the success of cellulose acetate/ ZIF-67 composite films, which exhibit improved gas permeability and selectivity for CO 2 /N 2 , CO 2 /CH 4 , and O 2 /N 2 . In particular, we have shown experimentally that the performance of composite films can be further boosted through alternative manufacturing processes. Molecular simulations were performed to explain the observed improvements in barrier performance between ZIF-containing cellulose acetate films prepared through electrospraying (asymmetric membrane), compared to traditional mixing (mixed matrix membrane). Bulk and slab systems of ZIF-67 were generated and validated, before being used as the basis for a cellulose acetate/ ZIF composite model. In this study, the morphology of polymer chains at the interface was compared with those in a neat cellulose acetate bulk system. It was demonstrated that chains were preferentially elongated parallel to the surface, and exhibited lower mobility and higher density in proximity to the ZIF. Conversely to polymer slabs, which show little to no penetration of gas molecules over the course of a molecular simulation, carbon dioxide molecules readily entered and were retained within the ZIF inorganic matrix. We propose that in mixed matrix membranes, where particles are sparsely dispersed, the surrounding rigid, highly dense shell of cellulose acetate blocks oncoming gases, which are instead redirected to surrounding amorphous polymer. In asymmetric membranes, the alignment of particles normal to the gas flux force penetrant gases through the performance enhancing ZIF. Imperfections in the electrospun layer, which appear as gaps between adjacent ZIF particles, are instead plugged by interacting, dense cellulose acetate factions. As particles are closely packed, the option of bypassing the zeolitic framework via amorphous polymer is not possible in asymmetric membranes, resulting in enhanced separation performance.

IP11

Experimental screening of transition metal-doped TiO2 for hydrogen production through photoreforming of methanol Ruiman Ma 1, Serigo Vernuccio 2 1 University of Sheffield, China, 2 University of Sheffield, Italy Environmental pollution, consumption of fossil fuels, and shortage of clean energy are some of the most serious issues facing the world today. Thus, hydrogen is receiving special attention from the research community as the next generation energy carrier (Schneider et al. , 2014). The use of solar energy, as a clean and renewable source is particularly promising to achieve photocatalytic hydrogen production using materials such as titanium dioxide as a photocatalyst which has the advantages of low cost, wide availability, and stability (Ni et al. , 2007). However, the band gap width of the pristine titanium dioxide (3.2 eV) is not suitable for absorbing visible light radiations, and only UV light can be utilized (Fresno et al. , 2014). Since UV light accounts for only a small fraction of the solar radiation, the inability to utilize the contribution of visible light strongly limits the efficiency of solar photocatalytic hydrogen production. To overcome this problem, novel materials need to be designed to improve the efficiency of hydrogen production. In this work we synthesized, characterized and tested in the hydrogen production from photoreforming several materials obtained from photodeposition of transition metals (e.g., Cu, Ag, Pt, Au, Pd) on titanium dioxide. We used methanol as a hole scavenger, to limit the recombination rate of electrons and holes, thus improving the hydrogen production rate. Photocatalytic tests were performed in an annular glass batch reactor (V = 280 ml; H = 40 cm), equipped with a high-pressure mercury vapor lamp located in a quartz jacket. The photocatalytic activity of several different metal dopants (Pt, Pd, Cu, Ag, Au) was tested under both UV and visible light radiation and the effect of different dopant amounts (in the range 0-10% mol) and different sacrificial agent concentrations (in the range 0-3 M) on the hydrogen production rate were examined. The results showed that the Pt-doped titanium dioxide (with a Pt loading of 0.5% mol) exhibits the best performance with a hydrogen production rate of about 180 mmol h -1 . The catalyst activity shows the following order Pt >Pd >Au >Cu >Ag under UV light radiation and Pd >Au >Pt >Cu >Ag under visible light radiation suggesting that the hydrogen production is governed by a different photocatalytic mechanism depending on the frequency of the radiations. The photocatalytic activity is enhanced as result of an increased methanol concentration up to a value of 2.5M. Further increase in the sacrificial agent concentrations do not result in any additional change in the hydrogen production rate. References 1. Fresno, F. et al. (2014), Journal of Materials Chemistry A , 2(9), pp. 2863–2884. doi: 10.1039/c3ta13793g. 2. Ni, M. et al. (2007), Renewable and Sustainable Energy Reviews , 11(3), pp. 401–425. doi: 10.1016/j.rser.2005.01.009. 3. Schneider, J. et al. (2014), Chem. Rev. , 114(9), p. 9919−9986.

IP12

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