Maßgeschneiderte Plasmonik & MOFs: Synergie für Geruchssensoren
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A good cup of coffee freshly cut grass a rainy day Some things and situations are engraved in our minds, and we recognize them at any moment by just their smell. Our sense of smell is capable of identifying a huge variety of odors, which are in reality a combination of molecules in different and yet specific proportions. Despite this, the capabilities of our sense of smell is far behind that of, for example, animals such as dogs that can identify odors associated with even subtle changes in our body chemistry, and thus reveal a medical crisis, or even help the police find drugs or explosives. To smell something, there is a need for an odor (the previously mentioned combination of molecules); something that entraps them; and something that detects them. In humans, our nose "entraps" the molecules and a set of detectors (bigger molecules called proteins) analyze them and send the information to our brain. In our project, we are aiming to develop a platform that uses a porous material capable of entrapping those odor molecules, and the so-called SERS technology, that uses light to detect and quantify them. Our porous materials are called MOFs, and they offer the possibility of creating an on-demand environment, thanks to their vast chemistry and diverse properties. SERS technology relies on converting the information on entrapped odors to a highly magnified optical signal, allowing us to detect and quantify them in very low concentrations. Pushing our boundaries of odor sensing and developing tools with better and higher capabilities has been in the spot of the scientific community as one of the thrilling challenges of bioinspired developments. By carefully combining the MOFs properties with the characteristics of SERS sensors, we are aiming to develop a versatile platform suitable for odorant detection with high sensitivity, with the potential of changing the paradigm of odorant sensing.
| Title | Year(s) | DOI / Link |
|---|---|---|
| Metal–Organic Frameworks in Surface Enhanced Raman Spectroscopy–Based Analysis of Volatile Organic CompoundsAdvanced Science | 2024 | 10.1002/advs.202401437 |
| Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology |
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Research Fields
| 2024 |
| 10.1021/acs.chemmater.4c01069 |