Lectures and Events - Department of Materials and Interfaces

Upcoming Lectures

  • October16

    14:00 PM

    Gerhard M.J. Schmidt Lecture Hall

    Rigidity at the Nanoscale: Engineering (super)selectivity at the bio-interface with DNA

    Prof. Maartje Bastings

    Understanding and manipulating precise interactions between materials and biology – the biointerface – is key to ensure...

    Understanding and manipulating precise interactions between materials and biology – the biointerface – is key to ensure optimal performance of diagnostics and therapeutics. Functional materials for biological applications, e.g. vaccines or implants, work best when their interaction with cells is precise. If not, side effects and toxicity might occur. Interactions are labeled superselective, when they happen only in a very specific (cellular) context and as such, present a strategy to enhance the therapeutic effect of bioactive materials. Selective multivalent interactions are traditionally engineered with a focus on the balance of valency and affinity, and often a good amount of structural flexibility is present. In my laboratory, we hypothesized that rigidity at the nanoscale could be a strong determinant of super-selectivity. We combine insights from biophysics and tools from DNA nanotechnology to engineer materials with a controlled flexibility/rigidity balance which allows to present molecules and organize interactions in precise spatial patterns. I will show how structural mechanical properties on the nanoscale determine the self-assembly mechanisms of supramolecular crystals, how they are critical for super-selective Multivalent Pattern Recognition (MPR) and how spatially controlled multivalent interactions are key in the fine-tuning of immune activation pathways. Exploiting programmable flexibility within the well-defined DNA molecule, our research presents a new engineering strategy to investigate the impact of nanorigidity in functional soft matter, surface order and communication with life.

  • October31

    11:00 AM

    Gerhard M.J. Schmidt Lecture Hall

    Catalytic (De)Hydrofunctionalization of Alcohols and Related Value-Added Transformations

    Dr. Debasis Banerjee

    Direct application of renewable alcohols as electrophilic coupling partner represents a sustainable alternative, as...

    Direct application of renewable alcohols as electrophilic coupling partner represents a sustainable alternative, as they can be readily available in industrial scale production from lignocellulose biomass.1 However, key challenge relies on control and selective tuning of the metal-hydride for such transformations using hydrogen-borrowing (HB) catalysis. Therefore, new approaches on the development of bi-functional-metal catalysis gaining significant attention. Recently, there is a potential drive to replace the precious noble-metal catalysts using earth abundant and inexpensive non-noble metals for sustainable organic transformations. Since past couple of years we have explored various applications of HB-catalysis using 3d-metals. A detailed mechanistic and kinetics studies were also established for such transformations.2-5

  • February12

    11:00 AM

    Gerhard M.J. Schmidt Lecture Hall

    AI (R)Evolution in Chemistry and Physics

    Prof. Alexandre Tkatchenko

    Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search and...

    Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search and generation, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding (quantum) molecules and materials? Aiming towards a unified machine learning (ML) model of molecular interactions in chemical space, I will discuss the potential and challenges for using ML techniques in chemistry and physics. ML methods can not only accurately estimate molecular properties of large datasets, but they can also lead to new insights into chemical similarity, aromaticity, reactivity, and molecular dynamics. For example, the combination of reliable molecular data with ML methods has enabled a fully quantitative simulation of protein dynamics in water (https://arxiv.org/abs/2205.08306). While the potential of machine learning for revealing insights into molecules and materials is high, I will conclude my talk by discussing the many remaining challenges.

  • May16

    11:00 AM

    Gerhard M.J. Schmidt Lecture Hall

    Annual Gerhard Schmidt Lecture

    Prof. Angel Rubio

  • December16

    11:00 AM

    Gerhard M.J. Schmidt Lecture Hall

    title tbd

    Prof. Anke Weidenkaff

  • February17

    11:00 AM

    Gerhard M.J. Schmidt Lecture Hall

    title tbd

    Prof. Christian A. Nijhuis

  • March17

    11:00 AM

    Gerhard M.J. Schmidt Lecture Hall

    title tbd

    Prof. Wim Noorduin