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Surface-enhanced Raman spectroscopy and artificial neural networks for detection of MXene flakes' surface terminations

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    0585536 - FZÚ 2025 RIV US eng J - Journal Article
    Trelin, A. - Skvortsova, A. - Olshtrem, A. - Chertopalov, Sergii - Mareš, D. - Lapčák, L. - Vondráček, Martin - Sajdl, P. - Jeřábek, V. - Maixner, J. - Lančok, Ján - Šofer, Z. - Regner, J. - Kolská, Z. - Švorčík, V. - Lyutakov, O.
    Surface-enhanced Raman spectroscopy and artificial neural networks for detection of MXene flakes' surface terminations.
    Journal of Physical Chemistry C. Roč. 128, č. 16 (2024), s. 6780-6787. ISSN 1932-7447. E-ISSN 1932-7455
    R&D Projects: GA ČR(CZ) GA21-09277S; GA MŠMT(CZ) EF16_019/0000760; GA MŠMT EH22_008/0004558
    Grant - others:OP VVV - SOLID21(XE) CZ.02.1.01/0.0/0.0/16_019/0000760
    Institutional support: RVO:68378271
    Keywords : SERS * MXene flakes * surface termination
    OECD category: Condensed matter physics (including formerly solid state physics, supercond.)
    Impact factor: 3.7, year: 2022
    Method of publishing: Open access

    The properties of MXene flakes, a new class of twodimensional materials, are strictly determined by their surface termination. The most common termination groups are oxygencontaining ( O or −OH) and fluorine (−F), and their relative ratio is closely related to flake stability and catalytic activity. The surface termination can vary significantly among MXene flakes depending on the preparation route and is commonly determined after flake preparation by using X-ray photoelectron spectroscopy (XPS). In this paper, as an alternative approach, we propose the combination of surface-enhanced Raman spectroscopy (SERS) and artificial neural networks (ANN) for the precise and reliable determination of MXene flakes’ (Ti3C2Tx) surface chemistry. Ti3C2Tx flakes were independently prepared by three scientific groups and subsequently measured using three different Raman spectrometers, employing resonant excitation wavelengths.
    Permanent Link: https://hdl.handle.net/11104/0353234


    Research data: Zenodo
     
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