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Tailoring Plasmonics & MOFs: Synergy for Odorant sensing

Tailoring Plasmonics & MOFs: Synergy for Odorant sensing

Juan Allegretto (ORCID: 0000-0002-7371-2610)
  • Grant DOI 10.55776/ESP93
  • Funding program ESPRIT
  • Status ongoing
  • Start February 1, 2023
  • End July 31, 2026
  • Funding amount € 294,016

Matching Funds - Niederösterreich

Disciplines

Chemistry (20%); Electrical Engineering, Electronics, Information Engineering (20%); Nanotechnology (10%); Materials Engineering (50%)

Keywords

    MOF, Films, SERS, ODORANT, PLASMONICS, SENSING

Abstract

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.

Research institution(s)
  • Danube Private University - 100%
Project participants
  • Jakub Dostalek, Danube Private University , mentor
  • Wolfgang Knoll, Danube Private University , national collaboration partner
International project participants
  • Omar Azzaroni, Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas - Argentina
  • Yi Wang, Wenzhou Medical University - China
  • Marc Lamy De La Chapelle, Le Mans Université - France

Research Output

  • 67 Citations
  • 2 Publications
  • 2 Datasets & models
  • 1 Scientific Awards
Publications
  • 2024
    Title Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology
    DOI 10.1021/acs.chemmater.4c01069
    Type Journal Article
    Author Allegretto J
    Journal Chemistry of Materials
    Pages 5814-5825
    Link Publication
  • 2024
    Title Metal–Organic Frameworks in Surface Enhanced Raman Spectroscopy–Based Analysis of Volatile Organic Compounds
    DOI 10.1002/advs.202401437
    Type Journal Article
    Author Allegretto J
    Journal Advanced Science
    Pages 2401437
    Link Publication
Datasets & models
  • 2024 Link
    Title Dataset of reports about MOF-based SERS substrates since 2011 until March 2023. Structure, characteristics, analytes, and performances.
    DOI 10.5281/zenodo.10580928
    Type Database/Collection of data
    Public Access
    Link Link
  • 2023 Link
    Title Dataset of reported synthetic conditions for ZIF-8 since 2006 and final characteristics and properties of the particles obtained
    DOI 10.5281/zenodo.8153451
    Type Database/Collection of data
    Public Access
    Link Link
Scientific Awards
  • 2024
    Title Tecnet Accent Innovation award 2024
    Type Research prize
    Level of Recognition Regional (any country)

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