Microplastics are widespread contaminants in water, posing risks to marine ecosystems and human health. Current filtration methods struggle to remove them effectively due to their small size, various types and inert properties. This project proposes a novel eco-friendly solution using water-immiscible liquids to capture microplastics via capillary agglomeration, enabling an easy and sustainable subsequent water-microplastics separation. A high-throughput molecular dynamics approach will identify the most efficient capturing liquids by testing various microplastics against hundreds of candidates. The project will also develop phase diagrams to map microplastics agglomeration behavior, providing new guidelines for sustainable microplastics removal from wastewater effluents.

A novel, smart way to process concrete is additive manufacturing. It allows the production of custom-made concrete objects with highly specific shapes and properties, with applications in the creative industry, architecture, landscaping, and more. This project aims to develop a smart additive manufacturing process for concrete products, in which the final product properties are optimized using a digital twin. The digital twin approach integrates product design into the manufacturing process, allowing for on-the-fly optimisation and aiming to reach first-right manufacturability. Furthermore, it empowers to develop an efficient advanced manufacturing process and novel products with improved quality features and higher durability/sustainability.

Particulate materials are the most manipulated substance on the planet, after water. They are of paramount importance to the chemical-pharmaceutical, agri-food, energy, high-tech manufacturing, mining, and construction industries. However, their unique behaviour cannot be captured comprehensively in macroscopic (continuum) models, while microscopic (discrete) models are too inefficient to handle the enormous number of particles. This has so far prevented the development of efficient computational models for particulates, which already exist for fluids and solids. Hence, while cars and airplanes are nowadays designed on the computer, particulate-handling industries still rely on time-consuming and expensive experimentation. To realise virtual prototyping of particulate processes, I will use a novel multiscale approach, microscopically resolving regions where general macroscopic models fail. From these multiscale simulations, I will extract efficient, application-specific macro-models and use them to understand and design innovative particulate-handling machinery. Two timely applications will be developed in collaboration with industry: additive manufacturing, the future of many high-tech industries; and continuous granulation, an aspiration of the pharma industry. Rapid prototyping will be used to create miniature setups, enabling validation and calibration from a single experiment. I am uniquely positioned to carry out this research, having extensive experience in micro- and macro-modelling, coupling, open-source development, model validation, and mesh refinement. The central pillar of this project is coarse-graining, an innovative new coupling technique, developed by me, that is used by universities and industry around the world. Via coarse-graining, my team will integrate two highly-regarded open-source softwares, MercuryDPM, which I founded, and oomph-lib. We will be the first to apply goal-oriented refinement to particulate systems, guaranteeing efficiency and robustness of the integrated software. All advanced algorithms created in this project will be released open-source, meaning immediate dissemination to academia and industry. This will enable a significant speed-up in the design and optimisation of numerous particulate-handling applications.

Powder agglomeration is common in many particulate processes, including tabletting and selective laser sintering. To optimise process conditions, industry currently relies on expensive trial plants, as no efficient and accurate simulation methods are available. Discrete particle simulations have been used to simulate these processes, but they are slow due to the large number of degrees of freedom (often >1 billion particles per litre), and can only simulate small volumes. Mesoscale particle methods promise to overcome this limitation by upscaling the individual particles, allowing for the simulation of realistic and complex processes. Additionally, the mesoscopic model will act as the key transitional bridge towards developing a predictive macroscopic continuum model. A particular focus in this research will be the quantitatively accurate prediction of large-scale processes, the flow dynamics, and the simulation of highly dynamic, high-pressure, hightemperature processes such as tabletting and selective laser sintering. The contact models will be calibrated and validated by a range of experiments, from individual particle reorganisation in small-scale experiments to bulk experiments.

This project aims at developing an efficient sustainable solution for domestic space heating and hot water supply with an integrated hybrid fuel cell – heat pump system. Utilizing waste heat from the fuel cell, we will deliver an optimized design for the hybrid system, and develop the key element required: a heat exchanging fuel cell electrode with optimized coolant flow path, extracting high heat without reduction in fuel cell safety or efficiency, relying on the design freedom of additive manufacturing. Based on hydrogen, the solution combines effective heat and hot water supply with negligible CO2 emissions.