Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.

Australian Institute for Bioengineering and Nanotechnology The University of Queensland Brisbane QLD Australia

Department of Materials Process Engineering Graduate School of Engineering Nagoya University Furo cho Chikusa ku Nagoya Japan

Department of Materials Science Institute of Pure and Applied Sciences University of Tsukuba Tsukuba Ibaraki Japan

Department of Solid State Engineering University of Chemistry and Technology Prague Czech Republic

National Institute for Materials Science Tsukuba Ibaraki Japan

Research School of Chemistry and Applied Biomedical Sciences Tomsk Polytechnic University Tomsk Russian Federation

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Lim XZ. Microplastics are everywhere - but are they harmful? Nature. 2021;593:22–25. doi: 10.1038/d41586-021-01143-3. PubMed DOI

Ivleva NP. Chemical analysis of microplastics and nanoplastics: challenges, advanced methods, and perspectives. Chem. Rev. 2021;121:11886–11936. doi: 10.1021/acs.chemrev.1c00178. PubMed DOI

Nguyen B, et al. Separation and analysis of microplastics and nanoplastics in complex environmental samples. Acc. Chem. Res. 2019;52:858–866. doi: 10.1021/acs.accounts.8b00602. PubMed DOI

Zhao S, Danley M, Ward JE, Li D, Mincer TJ. An approach for extraction, characterization and quantitation of microplastic in natural marine snow using Raman microscopy. Anal. Methods. 2017;9:1470–1478. doi: 10.1039/C6AY02302A. DOI

Dehaut A, et al. Microplastics in seafood: Benchmark protocol for their extraction and characterization. Environ. Pollut. 2016;215:223–233. doi: 10.1016/j.envpol.2016.05.018. PubMed DOI

Adhikari S, Kelkar V, Kumar R, Halden RU. Methods and challenges in the detection of microplastics and nanoplastics: a mini-review. Polym. Int. 2022;71:543–551. doi: 10.1002/pi.6348. DOI

Tokai T, Uchida K, Kuroda M, Isobe A. Mesh selectivity of neuston nets for microplastics. Mar. Pollut. Bull. 2021;165:112111. doi: 10.1016/j.marpolbul.2021.112111. PubMed DOI

Kotar S, et al. Quantitative assessment of visual microscopy as a tool for microplastic research: recommendations for improving methods and reporting. Chemosphere. 2022;308:136449. doi: 10.1016/j.chemosphere.2022.136449. PubMed DOI PMC

Lenz R, Enders K, Stedmon CA, MacKenzie DMA, Nielsen TG. A critical assessment of visual identification of marine microplastic using Raman spectroscopy for analysis improvement. Mar. Pollut. Bull. 2015;100:82–91. doi: 10.1016/j.marpolbul.2015.09.026. PubMed DOI

Blackie EJ, Le Ru EC, Etchegoin PG. Single-molecule surface-enhanced raman spectroscopy of nonresonant molecules. J. Am. Chem. Soc. 2009;131:14466–14472. doi: 10.1021/ja905319w. PubMed DOI

Barnes WL, Dereux A, Ebbesen TW. Surface plasmon subwavelength optics. Nature. 2003;424:824–830. doi: 10.1038/nature01937. PubMed DOI

Xu G, et al. Surface-enhanced raman spectroscopy facilitates the detection of microplastics <1 μm in the environment. Environ. Sci. Technol. 2020;54:15594–15603. doi: 10.1021/acs.est.0c02317. PubMed DOI

Stewart ME, et al. Quantitative multispectral biosensing and 1D imaging using quasi-3D plasmonic crystals. Proc. Natl. Acad. Sci. USA. 2006;103:17143–17148. doi: 10.1073/pnas.0606216103. PubMed DOI PMC

Kedzierski M, et al. A machine learning algorithm for high throughput identification of FTIR spectra: application on microplastics collected in the Mediterranean Sea. Chemosphere. 2019;234:242–251. doi: 10.1016/j.chemosphere.2019.05.113. PubMed DOI

Ballard Z, Brown C, Madni AM, Ozcan A. Machine learning and computation-enabled intelligent sensor design. Nat. Mach. Intell. 2021;3:556–565. doi: 10.1038/s42256-021-00360-9. DOI

Leong YX, et al. Surface-Enhanced Raman Scattering (SERS) Taster: a machine-learning-driven multireceptor platform for multiplex profiling of wine flavors. Nano Lett. 2021;21:2642–2649. doi: 10.1021/acs.nanolett.1c00416. PubMed DOI

Guselnikova O, et al. Label-free surface-enhanced Raman spectroscopy with artificial neural network technique for recognition photoinduced DNA damage. Biosens. Bioelectron. 2019;145:111718. doi: 10.1016/j.bios.2019.111718. PubMed DOI

Vaswani A, et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 2017;30:5999–6009.

Chen J, et al. Transformer for one stop interpretable cell type annotation. Nat. Commun. 2023;14:223. doi: 10.1038/s41467-023-35923-4. PubMed DOI PMC

Saito S, Motokado T, Obata KJ, Takahashi K. Capillary force with a concave probe-tip for micromanipulation. Appl. Phys. Lett. 2005;87:1–3. doi: 10.1063/1.2139848. DOI

Fan Z, et al. Capillary forces between concave gripper and spherical particle for micro-objects gripping. Micromachines. 2021;12:285. doi: 10.3390/mi12030285. PubMed DOI PMC

Galinski H, et al. Light manipulation in metallic nanowire networks with functional connectivity. Adv. Opt. Mater. 2017;5:1600580. doi: 10.1002/adom.201600580. DOI

Lim H, et al. A universal approach for the synthesis of mesoporous gold, palladium and platinum films for applications in electrocatalysis. Nat. Protoc. 2020;15:2980–3008. doi: 10.1038/s41596-020-0359-8. PubMed DOI

Li C, et al. Electrochemical synthesis of mesoporous gold films toward mesospace-stimulated optical properties. Nat. Commun. 2015;6:1–8. PubMed PMC

Lim H, et al. A mesopore-stimulated electromagnetic near-field: electrochemical synthesis of mesoporous copper films by micelle self-assembly. J. Mater. Chem. A. 2020;8:21016–21025. doi: 10.1039/D0TA06228F. DOI

Wang CB, Deo G, Wachs IE. Interaction of polycrystalline silver with oxygen, water, carbon dioxide, ethylene, and methanol: in situ raman and catalytic studies. J. Phys. Chem. B. 1999;103:5645–5656. doi: 10.1021/jp984363l. DOI

Beykal B, Herzberg M, Oren Y, Mauter MS. Influence of surface charge on the rate, extent, and structure of adsorbed bovine serum albumin to gold electrodes. J. Colloid Interface Sci. 2015;460:321–328. doi: 10.1016/j.jcis.2015.08.055. PubMed DOI

Hughes ZE, Wright LB, Walsh TR. Biomolecular adsorption at aqueous silver interfaces: First-principles calculations, polarizable force-field simulations, and comparisons with gold. Langmuir. 2013;29:13217–13229. doi: 10.1021/la402839q. PubMed DOI

Ahmad R, et al. Tailoring the surface chemistry of gold nanorods through Au-C/Ag-C covalent bonds using aryl diazonium salts. J. Phys. Chem. C. 2014;118:19098–19105. doi: 10.1021/jp504040d. DOI

Koishi T, Yasuoka K, Fujikawa S, Ebisuzaki T, Xiao CZ. Coexistence and transition between Cassie and Wenzel state on pillared hydrophobic surface. Proc. Natl. Acad. Sci. USA. 2009;106:8435–8440. doi: 10.1073/pnas.0902027106. PubMed DOI PMC

Magin CM, Cooper SP, Brennan AB. Non-toxic antifouling strategies. Mater. Today. 2010;13:36–44. doi: 10.1016/S1369-7021(10)70058-4. DOI

Wang BX, Liu MQ, Zhao CY, Fang X. Role of short-range order in manipulating light absorption in disordered media. JOSA B. 2018;35:504–513. doi: 10.1364/JOSAB.35.000504. DOI

Guselnikova O, et al. Surface filtration in mesoporous Au films decorated by Ag nanoparticles for solving SERS sensing small molecules in living cells. ACS Appl. Mater. Interfaces. 2022;14:41629–41639. doi: 10.1021/acsami.2c12804. PubMed DOI

Henzie J, Shuford KL, Kwak ES, Schatz GC, Odom TW. Manipulating the optical properties of pyramidal nanoparticle arrays. J. Phys. Chem. B. 2006;110:14028–14031. doi: 10.1021/jp063226i. PubMed DOI

He X, et al. Ultrasensitive detection of explosives via hydrophobic condensation effect on biomimetic SERS platforms. J. Mater. Chem. C. 2017;5:12384–12392. doi: 10.1039/C7TC04325B. DOI

Han Y, et al. Effect of oxidation on surface-enhanced raman scattering activity of silver nanoparticles: A quantitative correlation. Anal. Chem. 2011;83:5873–5880. doi: 10.1021/ac2005839. PubMed DOI

Geyer R, Jambeck JR, Law KL. Production, use, and fate of all plastics ever made. Sci. Adv. 2017;3:e1700782. doi: 10.1126/sciadv.1700782. PubMed DOI PMC

Barrows APW, Neumann CA, Berger ML, Shaw SD. Grab vs. neuston tow net: a microplastic sampling performance comparison and possible advances in the field. Anal. Methods. 2017;9:1446–1453. doi: 10.1039/C6AY02387H. DOI

Lindquist NC, Brolo AG. Ultra-high-speed dynamics in surface-enhanced Raman scattering. J. Phys. Chem. C. 2021;125:7523–7532. doi: 10.1021/acs.jpcc.0c11150. DOI

Zhang W, et al. A deep one-dimensional convolutional neural network for microplastics classification using Raman spectroscopy. Vib. Spectrosc. 2023;124:103487. doi: 10.1016/j.vibspec.2022.103487. DOI

Paul A, Wander L, Becker R, Goedecke C, Braun U. High-throughput NIR spectroscopic (NIRS) detection of microplastics in soil. Environ. Sci. Pollut. Res. 2019;26:7364–7374. doi: 10.1007/s11356-018-2180-2. PubMed DOI

de Back HM, Vargas Junior EC, Alarcon OE, Pottmaier D. Training and evaluating machine learning algorithms for ocean microplastics classification through vibrational spectroscopy. Chemosphere. 2022;287:131903. doi: 10.1016/j.chemosphere.2021.131903. PubMed DOI

Soares-Filho W, Manoel De Seixas J, Pereira Calôba L. Averaging spectra to improve the classification of the noise radiated by ships using neural networks. Proc. - Brazilian Symp. Neural Netw. 2000;1:156–161. doi: 10.1109/SBRN.2000.889731. DOI

Skvortsova A, et al. SERS and advanced chemometrics – utilization of Siamese neural network for picomolar identification of beta-lactam antibiotics resistance gene fragment. Anal. Chim. Acta. 2022;1192:339373. doi: 10.1016/j.aca.2021.339373. PubMed DOI

Yan X, Cao Z, Murphy A, Qiao Y. An ensemble machine learning method for microplastics identification with FTIR spectrum. J. Environ. Chem. Eng. 2022;10:108130. doi: 10.1016/j.jece.2022.108130. DOI

Ren L, et al. Identification of microplastics using a convolutional neural network based on micro-Raman spectroscopy. Talanta. 2023;260:124611. doi: 10.1016/j.talanta.2023.124611. PubMed DOI

Isobe A, Iwasaki S, Uchida K, Tokai T. Abundance of non-conservative microplastics in the upper ocean from 1957 to 2066. Nat. Commun. 2019;10:1–13. doi: 10.1038/s41467-019-08316-9. PubMed DOI PMC

Suzuki S, Sawada T, Serizawa T. Identification of water-soluble polymers through discrimination of multiple optical signals from a single peptide sensor. ACS Appl. Mater. Interfaces. 2021;13:55978–55987. doi: 10.1021/acsami.1c11794. PubMed DOI

Huang S, et al. Recent advances in sampling and sample preparation for effect-directed environmental analysis. TrAC Trends Anal. Chem. 2022;154:116654. doi: 10.1016/j.trac.2022.116654. DOI

Liu P, et al. Effect of aging on adsorption behavior of polystyrene microplastics for pharmaceuticals: adsorption mechanism and role of aging intermediates. J. Hazard. Mater. 2020;384:121193. doi: 10.1016/j.jhazmat.2019.121193. PubMed DOI

Pernetti M, Di Palma L. Experimental evaluation of inhibition effects of saline wastewater on activated sludge. Environ. Technol. 2010;26:695–704. doi: 10.1080/09593330.2001.9619509. PubMed DOI

Westgate PJ, Park C. Evaluation of proteins and organic nitrogen in wastewater treatment effluents. Environ. Sci. Technol. 2010;44:5352–5357. doi: 10.1021/es100244s. PubMed DOI

Rodrigues A, Brito A, Janknecht P, Proena MF, Nogueira R. Quantification of humic acids in surface water: effects of divalent cations, pH, and filtration. J. Environ. Monit. 2009;11:377–382. doi: 10.1039/B811942B. PubMed DOI

Chaisrikhwun B, Ekgasit S, Pienpinijtham P. Size-independent quantification of nanoplastics in various aqueous media using surfaced-enhanced Raman scattering. J. Hazard. Mater. 2023;442:130046. doi: 10.1016/j.jhazmat.2022.130046. PubMed DOI

Lv L, et al. In situ surface-enhanced Raman spectroscopy for detecting microplastics and nanoplastics in aquatic environments. Sci. Total Environ. 2020;728:138449. doi: 10.1016/j.scitotenv.2020.138449. PubMed DOI

Kim JY, et al. 3D plasmonic gold nanopocket structure for SERS machine learning-based microplastic detection. Adv. Funct. Mater. 2023;34:2307584. doi: 10.1002/adfm.202307584. DOI

Picó Y, Barceló D. Pyrolysis gas chromatography-mass spectrometry in environmental analysis: Focus on organic matter and microplastics. TrAC Trends Anal. Chem. 2020;130:115964. doi: 10.1016/j.trac.2020.115964. DOI

Saccone MA, Gallivan RA, Narita K, Yee DW, Greer JR. Additive manufacturing of micro-architected metals via hydrogel infusion. Nature. 2022;612:685–690. doi: 10.1038/s41586-022-05433-2. PubMed DOI PMC

Han X-L, et al. Deep learning based approach for automated characterization of large marine microplastic particles. Mar. Environ. Res. 2023;183:105829. doi: 10.1016/j.marenvres.2022.105829. PubMed DOI

Huang H, et al. Proceeding the categorization of microplastics through deep learning-based image segmentation. Sci. Total Environ. 2023;896:165308. doi: 10.1016/j.scitotenv.2023.165308. PubMed DOI

OpenRAMAN. https://www.open-raman.org/ (2024).

Leusch FDL, Ziajahromi S. Converting mg/L to Particles/L: reconciling the occurrence and toxicity literature on microplastics. Environ. Sci. Technol. 2021;55:11470–11472. doi: 10.1021/acs.est.1c04093. PubMed DOI

Lim H, et al. Synthesis of uniformly sized mesoporous silver films and their SERS application. J. Phys. Chem. C. 2020;124:23730–23737. doi: 10.1021/acs.jpcc.0c07234. DOI

Guselnikova O, et al. SERS platform for detection of lipids and disease markers prepared using modification of plasmonic-active gold gratings by lipophilic moieties. Sens. Actuat. B Chem. 2018;265:182–192. doi: 10.1016/j.snb.2018.03.016. DOI

Park H, et al. Mesoporous gold–silver alloy films towards amplification-free ultra-sensitive microRNA detection. J. Mater. Chem. B. 2020;8:9512–9523. doi: 10.1039/D0TB02003F. PubMed DOI

Fang J, et al. A general soft-enveloping strategy in the templating synthesis of mesoporous metal nanostructures. Nature. 2018;9:1–9. PubMed PMC

Skvortsova A, et al. SERS-CNN approach for non-invasive and non-destructive monitoring of stem cell growth on a universal substrate through an analysis of the cultivation medium. Sens. Actuat. B Chem. 2023;375:132812. doi: 10.1016/j.snb.2022.132812. DOI

Li, X., Bu, Y., Xie, J., Liang, J. & Xu, J. Determine the masses and ages of red giant branch stars from low-resolution LAMOST Spectra Using DenseNet. arXiv10.48550/arXiv.2106.04945 (2021).

Li L, Jamieson K, Rostamizadeh A, Talwalkar A. Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 2018;18:1–52.

Trelin, A. Microplastics-raman-spectra. Kagglehttps://www.kaggle.com/datasets/andriitrelin/microplastics-raman-spectra (2023).

Baek SJ, Park A, Ahn YJ, Choo J. Baseline correction using asymmetrically reweighted penalized least squares smoothing. Analyst. 2014;140:250–257. doi: 10.1039/C4AN01061B. PubMed DOI

Bestuzheva, K. et al. The SCIP Optimization Suite 8.0. arXiv10.48550/arXiv.2112.08872 (2021).

Foret, P., Kleiner, A., Mobahi, H. & Neyshabur, B. Sharpness-Aware Minimization for Efficiently Improving Generalization. arXiv10.48550/arXiv.2010.01412 (2020).

Loshchilov, I. & Hutter, F. Decoupled Weight Decay Regularization. 7th Int. Conf. Learn. Represent. ICLR 2019 (2017).

Trelin, A. Trel725/SpecATNet: First public release (1.0). Zenodo10.5281/ZENODO.10571618 (2024).

Bujacz A. Structures of bovine, equine and leporine serum albumin. Acta Cryst. 2012;D68:1278–1289. PubMed

Bujacz, A. & Bujacz, G. Crystal Structure of Bovine Serum Albumin, PDB ID: 4F5S. 10.2210/pdb4F5S/pdb (2012).