Driven by organic neuromorphics to achieve autonomous on-chip learning
After completing her Bachelor in Applied Physics on the topic of magnetization reversal in cobalt wires she continued her physics Master interested in various topics. In particular drawn to the medical applications of physics she did a research internship at the Swinburne University of Technology in Melbourne, Australia, where she studied the cell uptake of ligand functionalized gold nanospheres using high-order image correlation spectroscopy. For her graduation project she went to the University of Minnesota in Minneapolis, USA, to investigate the morphology and the effect of plasma on the gas flow in cold atmospheric pressure plasma jets impinging on a substrate. She got excited by the combination of research and the application of technology for medical purposes and joined the neuromorphic engineering group in January 2019. Her goal is to create a microfluidic chip with integrated sensors able to detect and classify biological cells using machine-learning.
A retrainable neuromorphic biosensor for on-chip learning and classificationNature Electronics (2023)
Organic neuromorphic computing(2023)
High-Performance Organic Electrochemical Transistors and Neuromorphic Devices Comprising Naphthalenediimide-Dialkoxybithiazole Copolymers Bearing Glycol Ether Pendant GroupsAdvanced Functional Materials (2022)
Adaptive Biosensing and Neuromorphic Classification Based on an Ambipolar Organic Mixed Ionic–Electronic ConductorAdvanced Materials (2022)
Towards organic neuromorphic devices for adaptive sensing and novel computing paradigms in bioelectronicsJournal of Materials Chemistry C (2019)
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