4.8 Article

Fluorescent nanoparticles for reliable communication among implantable medical devices

Journal

CARBON
Volume 190, Issue -, Pages 262-275

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.carbon.2022.01.016

Keywords

Information nanoparticles; Fluorescence; Body fluids; Fluidodynamics

Funding

  1. University of Catania

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Molecular Communication (MoCo) is a novel method that enables secure communication in wave-denied environments by using molecular messengers in fluids. It provides a secure communication solution for implantable medical devices without the risk of eavesdropping or interference.
Molecular Communication (MoCo) is a bio-inspired methodology enabling secure communication in wave-denied environments like intrabody communication among implantable medical devices. MoCo is based on the exchange, through fluids, of molecular messengers that carry the message instead of using electromagnetic waves. The transmitter emits the symbol by releasing a certain amount of information carrier into the carrier fluid traveling to the receiver, which, located downstream, waits for detection. This innovative approach enables secure communication between active implantable medical devices with no risk of eavesdropping or interference. Here a novel modulation method is devised, theoretically simulated, and experimentally demonstrated. The information is carried by fluorescent carbon nanoparticles. The modulation of the signal is obtained by exploiting the instability effect of an interfacial phenomenon, known as viscosity fingering, which occurs when two miscible liquids having different viscosity or strong density variation makes contact. This modulation is called Interfacial Shift Keying (ISK). The multinary-ISK version realized by combining multiple effects of interfacial difference has allowed secure communication in a testbed simulated bionic pancreas arrangement. Values of glucose variation in the body in the hours following a meal were successfully communicated by ISK modulated MoCo. Decoding of the symbol by machine learning methods has also been comprehensively reported. (c) 2022 Elsevier Ltd. All rights reserved.

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