Structural machine learning for high resolution tip-enhanced Raman spectroscopy

Description of the granted funding

Optical spectroscopy has long been an important characterization technique capable of providing chemical and structural information from molecules and nano materials in a non-destructive manner. However, due to the Abbe diffraction limit, propagating light cannot be localized in a region smaller than approximately half of its wavelength. For example, the wavelength of green light is 500 nm, resulting in a spatial resolution of ~ 250 nm, way larger than typical interatomic distances. To overcome this, scientists have been illuminating samples with a light coming from a source way smaller than the typical wavelength of light, which enables them to get amazing Å-scale resolution images. In this project, we will focus on simulating an experiment where the light is trapped between a very sharp tip and the sample creating a plasmonic field, which is then interpreted by a Raman spectrometer. This technique is called tip enhanced Raman spectroscopy, or TERS for short.
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Starting year

2025

End year

2029

Granted funding

Orlando Silveira Júnior Orcid -palvelun logo
754 253 €

Funder

Research Council of Finland

Funding instrument

Academy research fellows

Decision maker

Scientific Council for Natural Sciences and Engineering
12.06.2025

Other information

Funding decision number

371875

Fields of science

Physical sciences

Research fields

Fysiikka

Identified topics

photonics, optics, laser