Plasmonic Carbonaceous Nanotemplates for Microplastics Raman Detection
Authors: Shvalya Vasyl , Santhosh Neelakandan M , Kosicek Martin , Vengust Damjan , Olenik Jaka , Podlogar Mateja , Zavašnik Janez , Filipič Gregor , Modic Martina , Hojnik Nataša , Cvelbar Uros 
Published: July 2021

In the late XX and early XXI century, the uncontrollable and irreversible worldwide spread of macro- (>5 mm), micro- (5 µm–5 mm), and nanoplastic (<5 µm) pollutants have represented a growing concern. Recent ‘food for thought’ research provided by Isobe et al. offers a pessimistic forecast, suggesting the amount of non-conservative microplastics will increase twofold and fourfold by 2030 and 2060, respectively.[1] Freshly published reports point to the fact that human-made polymer debris can be detected virtually anywhere on the globe.[2, 3] The capabilities of today’s analytic optical instrumentation, such as Fourier-transform infrared spectroscopy and linear Raman spectroscopy far exceed requirements for chemical characterization of polymer pollutants. Recently it was shown that nanoplasmonic materials could be used to advance the vibrational spectroscopy considerably.
A combination of nanostructured metallic surfaces and Raman scattering represents a powerful analytic tool, known as surface-enhanced Raman spectroscopy (SERS). In the current report, we demonstrate that carbon-based nanostructures are an excellent accessory for microplastic recognition in Raman spectroscopy. The primary reason for this great success is attributed to extraordinary electronic properties, which facilitate ultrafast charge transfer reactions and π-plasmon resonance. A second reason is related to specific their chemical features from the affinity of carbon atoms to hybridization in sp, sp2, and sp3 electronic states allowing to form different morphologies such as of vertical nanowalls, aligned nanotubes, and nanocones. On the base of the last one, we showed that electromagnetic enhancement mechanism, which is more pronounced at the vicinity of sharp tips and thin edges, improves the intensity of peaks within a vibrational spectrum of plastic micro-fragments.
Prepared with the assistance of low-pressure radio-frequency (RF) inductively coupled plasma-enhanced chemical vapour deposition (PE-CVD) system and using Ni-film as a supporting-catalytic material the vertically aligned carbon nanocones (VACNs) revealed the high stability and prominent surface roughness at the nanoscale. By Raman and XPS analysis was suggested the designed sample possesses high I
D’/I
G ratio, which is mainly attributed to the presence of structural defects and microcrystalline graphitic edges that is accompanied by a dominant contribution of sp2 carbon surface component. After deposition of gold, the dark-field microscopy confirmed a high plasmonic activity in a visible spectral range favouring the use of greenish excitation wavelength. Employing SERS measurement and taking a Rhodamine 6G as a marker compound was established, the enhancement factor achieves a value of about 107 stating the excellent analytical performance.
Fully characterized substrate was used to enhance Raman scattering cross-section of the polypropylene (PP), polyethylene terephthalate (PET), polyurethane (PU), and polyamide (PA) microparticles. First, to prove the SERS effect, we compared the vibrational spectra of the tiniest plastic species collected from the plasmonic conical nanostructure (VACNs) and the glass slice. The selected PP fragments were ∼10 µm big (Figure 1 f). Surprisingly, by reducing exposure time down to 0.25 s the PP signal from the nanoconical texture was still readable. It was observed that Raman scattering intensity of PP from a plasmonic substrate is approximately an order of magnitude higher than from glass for the shortest illumination time.
To identify a polymer type, we designed a simple strategy. At first, we collected Raman vibrational spectra from all the samples and removed the accompanying background noise by applying a locally estimated scatterplot smoothing (LOESS) filter. Then, signal intensities were normalized to 100. The method was applied for all high-quality SERS spectra from each microplastic fragment. Acquired graphs were analyzed with the orthogonal partial least squares discriminant analysis algorithm.
In the plot displayed in Figure 1 g, five nicely separated data clusters are seen. Two data groups colored ‘blue’ and ‘violet’ that correspond to PU and PA reveal the most similar features of Raman spectra; however, they are still far apart Based on eigenvalues, the average identification accuracy of the proposed PCA method reaches a value above 97.5%. Among the total 97 microplastic fragments tested, the precise quantity of PP is 27, PA is 36, PU is 15, and PET fibers is 11. The other eight species did not cluster close to any other polymer, suggesting their different origin of appearance on the experimental table.

Figure 1. A summary of the research. (a–e) A set of optical images showing the general look of tested polymer micro-fragments. (f) - High-resolution spectra of PP particle with an approximate size of ~10 microns (see inserted digital images) on the conical SERS nanostructure taken at different exposure times. (g) - Results of PCA displayed via a loading graph plotted as PC2 vs PC3, revealing clustered spectra that are related to the type of polymer. (h-i) - SEM images showing surface features of the vertically aligned carbon nanocones before and after sputtering of 100 nm-thick gold film.
Acknowledgements
This work was supported by the Slovenian Research Agency ARRS grants NI-0001, and N2-0091.. The work was partially performed under the framework of the PEGASUS project, funded by the European Union's Horizon research and innovation program under grant agreement No. 766894. NMS acknowledges the support of AD FUTURA, Public Scholarship, Development, Disability, and Maintenance Fund of the Republic of Slovenia.
References
1] A. Isobe, S. Iwasaki, K. Uchida, T. Tokai, Nat. Commun.
2019, 10, 1.
[2] B. Boots, C. W. Russell, D. S. Green, Environ. Sci. Technol.
2019, 53, 11496.
[3] S. Ziajahromi, P. A. Neale, L. Rintoul, F. D. L. Leusch, Water Res.
2017, 112, 93.

Figure 1