Thirteen promising young Delft researchers receive Veni grant

Ray to Release: biocompatible release of small molecules based on low dose ionizing radiation
Dr. Mark de Geus, Applied Sciences (AS)

Cancer therapy causes side effects to the patient which limits further treatment options. Antibody-drug
conjugates (ADCs) and radiotherapy (X-rays) both cause localized damage to tumor cells to reduce side
effects. This research project will develop new chemistry to combine these approaches. The antibody
conjugate accumulates in tumor cells. Afterwards, X-rays act as molecular ‘scissors’ to separate the
antibody and the drug, allowing the drug to kill the tumor. The linker that connects the antibody to the
drug is chemically engineered to make it more sensitive to X-rays, which means that a lower dose of
radiation can be used
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Adaptive Algorithms for Non-Stationary Reinforcement Learning
Dr. Julia Olkhovskaia, Electrical Engineering, Mathematics & Computer Science (EEMCS)

Reinforcement Learning (RL) is revolutionizing automation tasks such as autonomous driving and
managing smart power grids. RL stands out for its unique ability to actively learn from interactions and
adapt to new data. However, in rapidly changing environments, it is struggling to perform in large-scale
problem settings. My research aims to overcome this by developing advanced RL algorithms that are not
only adaptive but can also process a vast amount of environmental data in real time. This approach is
bridging the gap between theoretical RL models and their practical, real-world applications
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Advancing Subsurface Characterization via Ensemble Nonlinear Data assimilation (ASCEND)
Dr. Max Ramgraber, Civil Engineering and Geosciences (CEG)

The subsurface is important for water supply and the energy transition, yet difficult to access and
observe directly. Limited information entails uncertainty, which can be resolved with data assimilation
(DA). Currently, most DA methods are either simplistic or computationally prohibitive. This project
develops a scalable DA algorithm that can be tailored to the system’s demands, which permits statistical
analyses that are as simple as possible and as complex as necessary. These analyses are instrumental for
efficient and rigorous engineering in the subsurface.
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The NEXT WAVE: enabling indefinite wave equation simulations for key-enabling technologies
Mr. Dr. ir. Vandana Dwarka, Electrical Engineering, Mathematics & Computer Science (EEMCS)

Modern scientific innovations use simulations to push beyond limits. Plasma confinement and quantum
mechanics rely on simulations of embedded wave equations. Unfortunately, one simulation can take
months to complete. Simulation hurdles stem from a complex mathematical feature of these wave
equations, which prevents developing theory to support robust simulation algorithms. Hence,
algorithms lag years behind advances in synergistic modelling of coupled dynamics to mimic real-life
systems. This research eliminates that backlog by combining novel theory and co-design with industry
experts to enable and accelerate simulations of key-enabling technologies, such as fusion reactors and
quantum computers
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Intensifying electrochemistry through downscaling - Micro ElectroChemical Systems (MECS)
Dr. Adrian Mularczyk, Electrical Engineering, Mathematics & Computer Science (EEMCS)

Smaller size at larger scale. Producing complex reactor geometries, smaller than a human hair, with
precision, reliability and speed has been the key to many technological advances. Achieving this in the
field of electrochemical systems is not trivial and requires a symbiotic interplay between several
disciplines from engineering and chemistry. If successful, this can unlock a revolution in our way of
designing and producing electrochemical reactors and boost their capabilities to be integrated in our
energy grids and chemical conversion infrastructure.
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Fluidic Sensing: Giving Soft Robots the Sense of Touch
Dr. Shibo Zou, Mechanical Engineering (ME)

Soft robotics holds the promise to handle delicate objects with human-like dexterity and care in real-
world environments, from greenhouses to operating theatres. Sensory feedback is crucial to achieve
high autonomy. Fluidic sensing extracts feedback mechanically through the actuation pressure variation
induced by shape change and can potentially bring the sense of touch to soft fluidic robots. However,
this actuation pressure variation is currently too low for practical applications. I aim to unravel the
underlying mechanical design principles that can amplify the actuation pressure variation in fluidic
sensing and translate these principles into multifunctional soft robotic devices for real-world
applications.
“With this Veni, I will uncover the mechanical design principles to enhance the self-sensing performance
of soft fluidic actuators. The design knowledge has the potential to advance the readiness of soft robots
for industrial automation."
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