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| Naji, M. |
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| Mohamed, Tarek |
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| Petrov, R. H. | Madrid |
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| Azevedo, Nuno Monteiro |
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| Rignanese, Gian-Marco |
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Hillenbrand, Rainer
Ikerbasque
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Topics
Publications (9/9 displayed)
- 2024snompy: a package for modelling scattering-type scanning near-field optical microscopycitations
- 2023Probing optical anapoles with fast electron beamscitations
- 2022Phonon-Enhanced Mid-Infrared CO2 Gas Sensing Using Boron Nitride Nanoresonatorscitations
- 2022Tailoring photoluminescence by strain-engineering in layered perovskite flakescitations
- 2021Plasmonic Metasurface Resonators to Enhance Terahertz Magnetic Fields for High‐Frequency Electron Paramagnetic Resonancecitations
- 2018Vibrational electron energy loss spectroscopy in truncated dielectric slabscitations
- 2018Surface-enhanced molecular electron energy loss spectroscopycitations
- 2017Probing low-energy hyperbolic polaritons in van der Waals crystals with an electron microscope
- 2016An Alternative Approach for the Incorporation of Cellulose Nanocrystals in Flexible Polyurethane Foams Based On Renewably Sourced Polyolscitations
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document
snompy: a package for modelling scattering-type scanning near-field optical microscopy
Abstract
Scattering-type scanning near-field optical microscopy (s-SNOM) is a powerful technique for extreme subwavelength imaging and spectroscopy, with around 20 nm spatial resolution. But quantitative relationships between experiment and material properties requires modelling, which can be computationally and conceptually challenging. In this work, we present snompy an open-source Python library which contains implementations of two of the most common s-SNOM models, the finite dipole model (FDM) and the point dipole model (PDM). We show a series of typical uses for this package with demonstrations including simulating nano-Fourier transform infrared (FTIR) spectra and recovering permittivity from experimental s-SNOM data. We also discuss the challenges faced with this sort of modelling, such as competing descriptions of the models in literature, and finite size effects. We hope that snompy will make quantitative s-SNOM modelling more accessible to the wider research community, which will further empower the use of s-SNOM for investigating nanoscale material properties.