The design of synthetic MC systems provides the basis for the envisioned healthcare and environmental applications. Our research focus lies on the development of physical layer techniques for synthetic MC systems, such as channel modeling and the design of new modulation, detection, and estimation schemes.

In some of the envisioned applications (e.g., drug delivery) the deployed bio-nanomachines are moving. Thus, the statistics of the propagation channel are changing, which makes the communication even more challenging. In order to design a reliable mobile MC system it is crucial to characterize the stochastic behavior of the channel. In this research track, we develop such stochastic channel models and based on these models we design new modulation, detection, estimation, and error correcting schemes.

The transposition (or crossover) effect is unique to diffusive MC systems, causing severe performance degradation. It occurs when a sequence of molecules arrives out of order at the receiver, which happens due to the stochastic nature of the propagation channels. In this research track, we analyze the impact of transposition errors in mobile and non-mobile MC systems. Moreover, we develop various techniques (e.g., error correcting codes) to overcome this problem.

Mesoporous silica nanoparticles (MSNP) are a promising candidate for carrying drug molecules, due to their high loading capacity and plethora of surface modifications. Although, drug delivery has been discussed as envisioned application for MC, MSNP has not been investigated under the MC paradigm. Thus, in this research track we model the drug release process of MSNPs (i.e., impulse response), which then allows us to design and analyze drug delivery systems (e.g., controlled-release drug delivery system) within the MC framework.