Machine-learning-enhanced identification of electron transporting biomolecules in electroactive microorganisms using combined Timeof- Flight Secondary Ion Mass Spectrometry (ToF-SIMS) and Scanning Probe Microscopy (SPM) data. (R-12314)

Long range electron transport in so-called electroactive microorganisms (e.g. cable bacteria) is not only of interest for (electromicro)biology, but is also a new exciting frontier at the interface with (bio-)physics and the emerging fields of bioelectronics / biodegradable electronics. The use of state-of-the-art Time-of-Flight Secondary Ion Mass Spectrometry combined with Scanning Probe Microscopy (ToF-SIMS/SPM-equipment in IMEC is first of its kind worldwide), yielded highly promising information on the composition of cable bacteria. The general scope of this project is to introduce machine learning in order to improve the identification from the complex ToF-SIMS datasets of the constituting molecules and in particular of the electron conducting moieties. It is expected that this proposed interdisciplinary study, combining machine learning with state-of-the-art ToF-SIMS, (micro)biology and (bio)physics, will have a significant impact on the fundamental understanding of longdistance electron transport in biology and could enhance the possibilities of ToF-SIMS for the study of the chemical structure of biological systems and other complex material systems.

Keywords:Bioelectricity, bioelectronics, Cable bacteria, Electroactive bacteria, Machine learning, SPM, ToF-SIMS

Disciplines:Machine learning and decision making, Visual data analysis, Statistical physics, Electronic (transport) properties, Applied and interdisciplinary physics