Fourteen promising young Delft researchers receive Veni grant

News - 19 July 2024 - Webredactie Communication

The Dutch Research Council (NWO) has awarded fourteen young TU Delft researchers from the Science (ENW) and Applied and Engineering Sciences (TTW) domains, a Veni grant of up to 320,000 euro. This will allow the laureates to further develop their own research ideas over the next three years. A total of 174 Veni grants were awarded.


In the ENW domain, seven Veni’s were awarded to:

Photo-active hydrogen bonded pairs for efficient artificial leaves
Dr. Tessel Bouwens, Applied Sciences (AS)


Using sunlight to generate the building blocks for medicines? Yes, we can, if we manage to solve the
efficiency problem in artificial photosynthetic devices. This research proposes to address the efficiency
problem by developing a molecular shuttlebus for delivering charged species at the desired location at
the right time to circumvent that these reactive charged species will undergo destructive side reactions.
Using the concept of the molecular shuttlebus, I want to develop a new method to generate the building
blocks for medicines employing solar energy, without using the polluting fossil fuels.

Ray to Release: X-rays trigger cleavage of cytotoxins for synergistic radio-chemotherapy
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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Combinatorial applications in parameterized algorithms
Dr. Carla Groenland, Electrical Engineering, Mathematics & Computer Science (EEMCS)


Network algorithms are ubiquitous: you use them for example when finding the shortest route home. In
this project, insights from combinatorics are used to find better algorithms, bringing fundamental
mathematics a step in the direction of real-life applications. The project includes reconstructing
properties from as few questions as possible (``query reconstruction'') and recognizing redundant steps
in sorting algorithms (``slow sorting''). The latter has implications for routing algorithms.
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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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Characterization of organic matter through spectro-polarimetry
Dr. Sandra Potin, Aerospace Engineering (AE)


The light holds information on the surface it has been reflected on. This is currently used as a technique
to detect signs of organic molecules, the building blocks of life in the Solar System. But this can be
limited by the detection capacity of the space scientific instrument. This project proposes to use an
intrinsic property of the light, polarization, to isolate and better identify the carbon-based molecules.
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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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Ultrafast detection of short-lived intermediates during the electrocatalytic CO2 reduction reaction
Dr. Yan Vogel, Applied Sciences (AS)


The petrochemical industry provides us with the products required to sustain our current living
standards, but also releases ~40 Gt a year of carbon dioxide driving climate change. The use of
renewable electricity to convert carbon dioxide into valuable chemicals can solve this problem.
However, we are currently unable to obtain the desired products because of the lack of understanding
of the carbon dioxide reactions. This project aims to unlock the mechanism behind these reactions by
using advanced spectroscopic tools, leading to a new method of chemical visualization for the
production of clean chemicals.
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In the TTW domain, six Veni’s were awarded to:

RECLIMATE: Resource-efficient climate-resilient buildings by multi-hazard risk modelling and
resilience-oriented decision-making
Dr. Simona Bianchi, Architecture and the Built Environment


Climate change poses higher risks to our vulnerable homes. Urgent adaptation solutions are needed to
create resilient urban communities. Yet, current design and assessment methods do not consider multi-
hazard resilience quantifications and extreme heat consequences, which results in ineffective solutions.
This project pioneers a comprehensive assessment framework to measure the heat vulnerability of
buildings and quantify their overall resilience in the face of climate uncertainties. It introduces novel
multi-disciplinary design methods, frameworks and digital tools for resource-efficient resilient designs
and retrofits in the era of climate-induced extremes.
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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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Energy-Efficient Real-Time Edge Intelligence for Wearable Healthcare Devices
Dr. Chang Gao, Electrical Engineering, Mathematics & Computer Science (EEMCS)


In this innovative project, researchers are developing new software and hardware technology to make
healthcare wearables, like eye movement trackers, hearing aids, and heart rate monitors, smarter and
more efficient. By processing personal data and artificial intelligence algorithms for healthcare directly
on these devices powered by specialised AI hardware accelerators, the project aims to enable instant
wearable healthcare and enhance privacy. This approach also reduces energy use, promising longer
battery life and more sustainable devices. The technology could transform how we monitor health
conditions, making it quicker, more secure, and accessible to a wider audience.
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Intensifying electrochemistry through downscaling - Micro ElectroChemical Systems (MECS)
Dr. Adrian Mularczyk, Applied Sciences (AS)


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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Revealing Hidden Networks of Coastal Sediment Pathways via Laboratory & Numerical Experiments
Dr. ir. Stuart Pearson, Civil Engineering and Geosciences (CEG)


Sediment is essential for creating a safe and sustainable coast. In this project, I will track the pathways
that sand takes on an experimental laboratory beach and extend those findings with computer models.
By revealing the interconnected network of sediment pathways shaping our coast, we can better
understand how to manage the sediment that builds ecosystems and protects us against flooding.
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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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Signal Processing and Learning from Higher-Order Network Dynamics
Dr. Elvin Isufi, Electrical Engineering, Mathematics & Computer Science (EEMCS)

Networks, such as those that distribute water to our homes or our brain, generate streams of data according to their topology but conventional processing techniques do not fully capture their complex dependencies. The research will investigate novel techniques to better leverage the network structure for processing these data so as to detect more accurately brain anomalies, forecast future water demands, and make them deployable to a large-scale setting.