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Martijn A. Zwijnenburg

Publications and source records attributed to Martijn A. Zwijnenburg.

2 recordsLinked to original sources

Model structures and electron transfer properties of conductive nickel-organic nanoribbons in cable bacteria

Cable bacteria are multicellular bacteria capable of centimeter-scale conduction through a regular fiber network embedded in their cell envelope. The conductivity of these fibers is extremely high for biological materials, and rivals that of the best synthetic conductive polymers, but the underlying electron transport mechanism remains elusive. Recent microscopic and spectroscopic evidence indicates that each fiber embeds a bundle of intertwined nanoribbons as the conductive conduit. Each nanoribbon consists of a one-dimensional nickel-organic framework, built from stacked nickel bis(1,2-dithiolene) oligomers (NiBiD units) as molecular building blocks. Here we performed DFT calculations of nanoribbon model structures, in order to characterize their electronic properties, examine potential stacking configurations and verify whether these structures can support efficient conductance. Our simulations indicate that nanoribbons are comprised of tightly stacked AA or AB-type packings of NiBiD units. In the most energetically stable structure (AB-type) some Ni centers are predicted to be 5-fold coordinated due to formation of an inter-layer Ni-S coordination bond. In several energetically low-lying structures, the electronic coupling between neighboring molecules exceeds the critical threshold for charge delocalization permitting efficient charge transport beyond small polaron hopping. Our results hence reveal that nanoribbons based on NiBiD units exhibit favorable charge transfer properties that may explain the unusually high conductivities measured in the fibers of cable bacteria.

physics.chem-ph↗

Comparative Analysis of search Approaches to Discover Donor Molecules for Organic Solar Cells

Identifying organic molecules with desirable properties from the extensive chemical space can be challenging, particularly when property evaluation methods are time-consuming and resource intensive. In this study, we illustrate this challenge by exploring the chemical space of large oligomers, constructed from monomeric building blocks, for potential use in organic photovoltaics (OPV). For this purpose, we developed a python package to search the chemical space using a building block approach: stk-search. We use stk-search to compare a variety of search algorithms, including those based upon Bayesian optimization and evolutionary approaches. Initially, we evaluated and compared the performance of different search algorithms within a precomputed search space. We then extended our investigation to the vast chemical space of molecules formed of 6 building blocks (6-mers), comprising over $10^{14}$ molecules. Notably, while some algorithms show only marginal improvements over a random search approach in a relatively small, precomputed, search space, their performance in the larger chemical space is orders of magnitude better. Specifically, Bayesian optimization identified a thousand times more promising molecules with the desired properties compared to random search, using the same computational resources.

physics.chem-ph↗