Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
28 May 2024
28 May 2024
Historique:
received:
22
07
2023
accepted:
21
04
2024
medline:
29
5
2024
pubmed:
29
5
2024
entrez:
28
5
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38806498
doi: 10.1038/s41467-024-48148-w
pii: 10.1038/s41467-024-48148-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4351Subventions
Organisme : MEXT | JST | Exploratory Research for Advanced Technology (ERATO)
ID : JPMJER2003
Organisme : MEXT | Japan Society for the Promotion of Science (JSPS)
ID : 20K05453
Organisme : MEXT | Japan Society for the Promotion of Science (JSPS)
ID : 20K05453
Organisme : Korea Institute of Industrial Technology (KITECH)
ID : JE210028
Organisme : Russian Science Foundation (RSF)
ID : 23-73-00117
Informations de copyright
© 2024. The Author(s).
Références
Lim, X. Z. Microplastics are everywhere - but are they harmful? Nature 593, 22–25 (2021).
pubmed: 33947993
doi: 10.1038/d41586-021-01143-3
Ivleva, N. P. Chemical analysis of microplastics and nanoplastics: challenges, advanced methods, and perspectives. Chem. Rev. 121, 11886–11936 (2021).
pubmed: 34436873
doi: 10.1021/acs.chemrev.1c00178
Nguyen, B. et al. Separation and analysis of microplastics and nanoplastics in complex environmental samples. Acc. Chem. Res. 52, 858–866 (2019).
pubmed: 30925038
doi: 10.1021/acs.accounts.8b00602
Zhao, S., Danley, M., Ward, J. E., Li, D. & Mincer, T. J. An approach for extraction, characterization and quantitation of microplastic in natural marine snow using Raman microscopy. Anal. Methods 9, 1470–1478 (2017).
doi: 10.1039/C6AY02302A
Dehaut, A. et al. Microplastics in seafood: Benchmark protocol for their extraction and characterization. Environ. Pollut. 215, 223–233 (2016).
pubmed: 27209243
doi: 10.1016/j.envpol.2016.05.018
Adhikari, S., Kelkar, V., Kumar, R. & Halden, R. U. Methods and challenges in the detection of microplastics and nanoplastics: a mini-review. Polym. Int. 71, 543–551 (2022).
doi: 10.1002/pi.6348
Tokai, T., Uchida, K., Kuroda, M. & Isobe, A. Mesh selectivity of neuston nets for microplastics. Mar. Pollut. Bull. 165, 112111 (2021).
pubmed: 33588104
doi: 10.1016/j.marpolbul.2021.112111
Kotar, S. et al. Quantitative assessment of visual microscopy as a tool for microplastic research: recommendations for improving methods and reporting. Chemosphere 308, 136449 (2022).
pubmed: 36115477
doi: 10.1016/j.chemosphere.2022.136449
Lenz, R., Enders, K., Stedmon, C. A., MacKenzie, D. M. A. & Nielsen, T. G. A critical assessment of visual identification of marine microplastic using Raman spectroscopy for analysis improvement. Mar. Pollut. Bull. 100, 82–91 (2015).
pubmed: 26455785
doi: 10.1016/j.marpolbul.2015.09.026
Blackie, E. J., Le Ru, E. C. & Etchegoin, P. G. Single-molecule surface-enhanced raman spectroscopy of nonresonant molecules. J. Am. Chem. Soc. 131, 14466–14472 (2009).
pubmed: 19807188
doi: 10.1021/ja905319w
Barnes, W. L., Dereux, A. & Ebbesen, T. W. Surface plasmon subwavelength optics. Nature 424, 824–830 (2003).
pubmed: 12917696
doi: 10.1038/nature01937
Xu, G. et al. Surface-enhanced raman spectroscopy facilitates the detection of microplastics <1 μm in the environment. Environ. Sci. Technol. 54, 15594–15603 (2020).
pubmed: 33095569
doi: 10.1021/acs.est.0c02317
Stewart, M. E. et al. Quantitative multispectral biosensing and 1D imaging using quasi-3D plasmonic crystals. Proc. Natl. Acad. Sci. USA. 103, 17143–17148 (2006).
pubmed: 17085594
pmcid: 1634412
doi: 10.1073/pnas.0606216103
Kedzierski, M. et al. A machine learning algorithm for high throughput identification of FTIR spectra: application on microplastics collected in the Mediterranean Sea. Chemosphere 234, 242–251 (2019).
pubmed: 31226506
doi: 10.1016/j.chemosphere.2019.05.113
Ballard, Z., Brown, C., Madni, A. M. & Ozcan, A. Machine learning and computation-enabled intelligent sensor design. Nat. Mach. Intell. 3, 556–565 (2021).
doi: 10.1038/s42256-021-00360-9
Leong, Y. X. et al. Surface-Enhanced Raman Scattering (SERS) Taster: a machine-learning-driven multireceptor platform for multiplex profiling of wine flavors. Nano Lett. 21, 2642–2649 (2021).
pubmed: 33709720
doi: 10.1021/acs.nanolett.1c00416
Guselnikova, O. et al. Label-free surface-enhanced Raman spectroscopy with artificial neural network technique for recognition photoinduced DNA damage. Biosens. Bioelectron. 145, 111718 (2019).
pubmed: 31561094
doi: 10.1016/j.bios.2019.111718
Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5999–6009 (2017).
Chen, J. et al. Transformer for one stop interpretable cell type annotation. Nat. Commun. 14, 223 (2023).
pubmed: 36641532
pmcid: 9840170
doi: 10.1038/s41467-023-35923-4
Saito, S., Motokado, T., Obata, K. J. & Takahashi, K. Capillary force with a concave probe-tip for micromanipulation. Appl. Phys. Lett. 87, 1–3 (2005).
doi: 10.1063/1.2139848
Fan, Z. et al. Capillary forces between concave gripper and spherical particle for micro-objects gripping. Micromachines 12, 285 (2021).
pubmed: 33800478
pmcid: 8001796
doi: 10.3390/mi12030285
Galinski, H. et al. Light manipulation in metallic nanowire networks with functional connectivity. Adv. Opt. Mater. 5, 1600580 (2017).
doi: 10.1002/adom.201600580
Lim, H. et al. A universal approach for the synthesis of mesoporous gold, palladium and platinum films for applications in electrocatalysis. Nat. Protoc. 15, 2980–3008 (2020).
pubmed: 32839575
doi: 10.1038/s41596-020-0359-8
Li, C. et al. Electrochemical synthesis of mesoporous gold films toward mesospace-stimulated optical properties. Nat. Commun. 6, 1–8 (2015).
Lim, H. et al. A mesopore-stimulated electromagnetic near-field: electrochemical synthesis of mesoporous copper films by micelle self-assembly. J. Mater. Chem. A 8, 21016–21025 (2020).
doi: 10.1039/D0TA06228F
Wang, C. B., Deo, G. & Wachs, I. E. Interaction of polycrystalline silver with oxygen, water, carbon dioxide, ethylene, and methanol: in situ raman and catalytic studies. J. Phys. Chem. B 103, 5645–5656 (1999).
doi: 10.1021/jp984363l
Beykal, B., Herzberg, M., Oren, Y. & Mauter, M. S. Influence of surface charge on the rate, extent, and structure of adsorbed bovine serum albumin to gold electrodes. J. Colloid Interface Sci. 460, 321–328 (2015).
pubmed: 26348658
doi: 10.1016/j.jcis.2015.08.055
Hughes, Z. E., Wright, L. B. & Walsh, T. R. Biomolecular adsorption at aqueous silver interfaces: First-principles calculations, polarizable force-field simulations, and comparisons with gold. Langmuir 29, 13217–13229 (2013).
pubmed: 24079907
doi: 10.1021/la402839q
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 118, 19098–19105 (2014).
doi: 10.1021/jp504040d
Koishi, T., Yasuoka, K., Fujikawa, S., Ebisuzaki, T. & Xiao, C. Z. Coexistence and transition between Cassie and Wenzel state on pillared hydrophobic surface. Proc. Natl. Acad. Sci. USA. 106, 8435–8440 (2009).
pubmed: 19429707
pmcid: 2688995
doi: 10.1073/pnas.0902027106
Magin, C. M., Cooper, S. P. & Brennan, A. B. Non-toxic antifouling strategies. Mater. Today 13, 36–44 (2010).
doi: 10.1016/S1369-7021(10)70058-4
Wang, B. X., Liu, M. Q., Zhao, C. Y. & Fang, X. Role of short-range order in manipulating light absorption in disordered media. JOSA B 35, 504–513 (2018).
doi: 10.1364/JOSAB.35.000504
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 14, 41629–41639 (2022).
pubmed: 36043945
doi: 10.1021/acsami.2c12804
Henzie, J., Shuford, K. L., Kwak, E. S., Schatz, G. C. & Odom, T. W. Manipulating the optical properties of pyramidal nanoparticle arrays. J. Phys. Chem. B 110, 14028–14031 (2006).
pubmed: 16854094
doi: 10.1021/jp063226i
He, X. et al. Ultrasensitive detection of explosives via hydrophobic condensation effect on biomimetic SERS platforms. J. Mater. Chem. C 5, 12384–12392 (2017).
doi: 10.1039/C7TC04325B
Han, Y. et al. Effect of oxidation on surface-enhanced raman scattering activity of silver nanoparticles: A quantitative correlation. Anal. Chem. 83, 5873–5880 (2011).
pubmed: 21644591
doi: 10.1021/ac2005839
Geyer, R., Jambeck, J. R. & Law, K. L. Production, use, and fate of all plastics ever made. Sci. Adv. 3, e1700782 (2017).
pubmed: 28776036
pmcid: 5517107
doi: 10.1126/sciadv.1700782
Barrows, A. P. W., Neumann, C. A., Berger, M. L. & Shaw, S. D. Grab vs. neuston tow net: a microplastic sampling performance comparison and possible advances in the field. Anal. Methods 9, 1446–1453 (2017).
doi: 10.1039/C6AY02387H
Lindquist, N. C. & Brolo, A. G. Ultra-high-speed dynamics in surface-enhanced Raman scattering. J. Phys. Chem. C 125, 7523–7532 (2021).
doi: 10.1021/acs.jpcc.0c11150
Zhang, W. et al. A deep one-dimensional convolutional neural network for microplastics classification using Raman spectroscopy. Vib. Spectrosc. 124, 103487 (2023).
doi: 10.1016/j.vibspec.2022.103487
Paul, A., Wander, L., Becker, R., Goedecke, C. & Braun, U. High-throughput NIR spectroscopic (NIRS) detection of microplastics in soil. Environ. Sci. Pollut. Res. 26, 7364–7374 (2019).
doi: 10.1007/s11356-018-2180-2
de Back, H. M., Vargas Junior, E. C., Alarcon, O. E. & Pottmaier, D. Training and evaluating machine learning algorithms for ocean microplastics classification through vibrational spectroscopy. Chemosphere 287, 131903 (2022).
pubmed: 34455125
doi: 10.1016/j.chemosphere.2021.131903
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. 1, 156–161 (2000).
doi: 10.1109/SBRN.2000.889731
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 1192, 339373 (2022).
pubmed: 35057931
doi: 10.1016/j.aca.2021.339373
Yan, X., Cao, Z., Murphy, A. & Qiao, Y. An ensemble machine learning method for microplastics identification with FTIR spectrum. J. Environ. Chem. Eng. 10, 108130 (2022).
doi: 10.1016/j.jece.2022.108130
Ren, L. et al. Identification of microplastics using a convolutional neural network based on micro-Raman spectroscopy. Talanta 260, 124611 (2023).
pubmed: 37163925
doi: 10.1016/j.talanta.2023.124611
Isobe, A., Iwasaki, S., Uchida, K. & Tokai, T. Abundance of non-conservative microplastics in the upper ocean from 1957 to 2066. Nat. Commun. 10, 1–13 (2019).
doi: 10.1038/s41467-019-08316-9
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 13, 55978–55987 (2021).
pubmed: 34735134
doi: 10.1021/acsami.1c11794
Huang, S. et al. Recent advances in sampling and sample preparation for effect-directed environmental analysis. TrAC Trends Anal. Chem. 154, 116654 (2022).
doi: 10.1016/j.trac.2022.116654
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. 384, 121193 (2020).
pubmed: 31610348
doi: 10.1016/j.jhazmat.2019.121193
Pernetti, M. & Di Palma, L. Experimental evaluation of inhibition effects of saline wastewater on activated sludge. Environ. Technol. 26, 695–704 (2010).
doi: 10.1080/09593330.2001.9619509
Westgate, P. J. & Park, C. Evaluation of proteins and organic nitrogen in wastewater treatment effluents. Environ. Sci. Technol. 44, 5352–5357 (2010).
pubmed: 20557127
doi: 10.1021/es100244s
Rodrigues, A., Brito, A., Janknecht, P., Proena, M. F. & Nogueira, R. Quantification of humic acids in surface water: effects of divalent cations, pH, and filtration. J. Environ. Monit. 11, 377–382 (2009).
pubmed: 19212596
doi: 10.1039/B811942B
Chaisrikhwun, B., Ekgasit, S. & Pienpinijtham, P. Size-independent quantification of nanoplastics in various aqueous media using surfaced-enhanced Raman scattering. J. Hazard. Mater. 442, 130046 (2023).
pubmed: 36182893
doi: 10.1016/j.jhazmat.2022.130046
Lv, L. et al. In situ surface-enhanced Raman spectroscopy for detecting microplastics and nanoplastics in aquatic environments. Sci. Total Environ. 728, 138449 (2020).
pubmed: 32353796
doi: 10.1016/j.scitotenv.2020.138449
Kim, J. Y. et al. 3D plasmonic gold nanopocket structure for SERS machine learning-based microplastic detection. Adv. Funct. Mater. 34, 2307584 (2023).
doi: 10.1002/adfm.202307584
Picó, Y. & Barceló, D. Pyrolysis gas chromatography-mass spectrometry in environmental analysis: Focus on organic matter and microplastics. TrAC Trends Anal. Chem. 130, 115964 (2020).
doi: 10.1016/j.trac.2020.115964
Saccone, M. A., Gallivan, R. A., Narita, K., Yee, D. W. & Greer, J. R. Additive manufacturing of micro-architected metals via hydrogel infusion. Nature 612, 685–690 (2022).
pubmed: 36265511
pmcid: 9713131
doi: 10.1038/s41586-022-05433-2
Han, X.-L. et al. Deep learning based approach for automated characterization of large marine microplastic particles. Mar. Environ. Res. 183, 105829 (2023).
pubmed: 36495654
doi: 10.1016/j.marenvres.2022.105829
Huang, H. et al. Proceeding the categorization of microplastics through deep learning-based image segmentation. Sci. Total Environ. 896, 165308 (2023).
pubmed: 37414186
doi: 10.1016/j.scitotenv.2023.165308
OpenRAMAN. https://www.open-raman.org/ (2024).
Leusch, F. D. L. & Ziajahromi, S. Converting mg/L to Particles/L: reconciling the occurrence and toxicity literature on microplastics. Environ. Sci. Technol. 55, 11470–11472 (2021).
pubmed: 34370451
doi: 10.1021/acs.est.1c04093
Lim, H. et al. Synthesis of uniformly sized mesoporous silver films and their SERS application. J. Phys. Chem. C 124, 23730–23737 (2020).
doi: 10.1021/acs.jpcc.0c07234
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. 265, 182–192 (2018).
doi: 10.1016/j.snb.2018.03.016
Park, H. et al. Mesoporous gold–silver alloy films towards amplification-free ultra-sensitive microRNA detection. J. Mater. Chem. B 8, 9512–9523 (2020).
pubmed: 32996976
doi: 10.1039/D0TB02003F
Fang, J. et al. A general soft-enveloping strategy in the templating synthesis of mesoporous metal nanostructures. Nature 9, 1–9 (2018).
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. 375, 132812 (2023).
doi: 10.1016/j.snb.2022.132812
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. arXiv https://doi.org/10.48550/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. 18, 1–52 (2018).
Trelin, A. Microplastics-raman-spectra. Kaggle https://www.kaggle.com/datasets/andriitrelin/microplastics-raman-spectra (2023).
Baek, S. J., Park, A., Ahn, Y. J. & Choo, J. Baseline correction using asymmetrically reweighted penalized least squares smoothing. Analyst 140, 250–257 (2014).
doi: 10.1039/C4AN01061B
Bestuzheva, K. et al. The SCIP Optimization Suite 8.0. arXiv https://doi.org/10.48550/arXiv.2112.08872 (2021).
Foret, P., Kleiner, A., Mobahi, H. & Neyshabur, B. Sharpness-Aware Minimization for Efficiently Improving Generalization. arXiv https://doi.org/10.48550/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). Zenodo https://doi.org/10.5281/ZENODO.10571618 (2024).
Bujacz, A. Structures of bovine, equine and leporine serum albumin. Acta Cryst. D68, 1278–1289 (2012).
Bujacz, A. & Bujacz, G. Crystal Structure of Bovine Serum Albumin, PDB ID: 4F5S. https://doi.org/10.2210/pdb4F5S/pdb (2012).