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Chen YX, Zhang FY, Dang ZB, He X, Luo CX et al. Chiral detection of biomolecules based on reinforcement learning. Opto-Electron Sci 2, 220019 (2023). doi: 10.29026/oes.2023.220019
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Article Open Access
Chiral detection of biomolecules based on reinforcement learning
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Abstract
Chirality plays an important role in biological processes, and enantiomers often possess similar physical properties and different physiologic functions. In recent years, chiral detection of enantiomers become a popular topic. Plasmonic metasurfaces enhance weak inherent chiral effects of biomolecules, so they are used in chiral detection. Artificial intelligence algorithm makes a lot of contribution to many aspects of nanophotonics. Here, we propose a nanostructure design method based on reinforcement learning and devise chiral nanostructures to distinguish enantiomers. The algorithm finds out the metallic nanostructures with a sharp peak in circular dichroism spectra and emphasizes the frequency shifts caused by nearfield interaction of nanostructures and biomolecules. Our work inspires universal and efficient machine-learning methods for nanophotonic design.-
Keywords:
- chiral detection /
- metasurface /
- deep learning /
- cathodoluminescence
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References
[1] Kuang H, Xu CL, Tang ZY. Emerging chiral materials. Adv Mater 32, 2005110 (2020). doi: 10.1002/adma.202005110 [2] Ma W, Xu LG, De Moura AF, Wu XL, Kuang H et al. Chiral inorganic nanostructures. Chem Rev 117, 8041–8093 (2017). doi: 10.1021/acs.chemrev.6b00755 [3] Mun J, Kim M, Yang Y, Badloe T, Ni JC et al. Electromagnetic chirality: from fundamentals to nontraditional chiroptical phenomena. Light Sci Appl 9, 139 (2020). doi: 10.1038/s41377-020-00367-8 [4] Ma W, Xu LG, Wang LB, Xu CL, Kuang H. Chirality-based biosensors. Adv Funct Mater 29, 1805512 (2019). doi: 10.1002/adfm.201805512 [5] Valev VK, Baumberg JJ, Sibilia C, Verbiest T. Chirality and chiroptical effects in plasmonic nanostructures: fundamentals, recent progress, and outlook. Adv Mater 25, 2517–2534 (2013). doi: 10.1002/adma.201205178 [6] Yoo S, Park QH. Metamaterials and chiral sensing: a review of fundamentals and applications. Nanophotonics 8, 249–261 (2019). doi: 10.1515/nanoph-2018-0167 [7] Cao ZL, Gao H, Qiu M, Jin W, Dem sub-nanometer biochemical molecules to sub-micrometer plasmonic metastructures:physiochemical mechanisms, biosensing, and bioimaging opportuniting SZ et al. Chirality transfer froes. Adv Mater 32, 1907151 (2020). doi: 10.1002/adma.201907151 [8] Ren MX, Plum E, Xu JJ, Zheludev NI. Giant nonlinear optical activity in a plasmonic metamaterial. Nat Commun 3, 833 (2012). doi: 10.1038/ncomms1805 [9] Chen SM, Zeuner F, Weismann M, Reineke B, Li GX et al. Giant nonlinear optical activity of achiral origin in planar metasurfaces with quadratic and cubic nonlinearities. Adv Mater 28, 2992–2999 (2016). doi: 10.1002/adma.201505640 [10] Plum E, Fedotov VA, Zheludev NI. Specular optical activity of achiral metasurfaces. Appl Phys Lett 108, 141905 (2016). doi: 10.1063/1.4944775 [11] Gigli C, Leo G. All-dielectric χ(2) metasurfaces: recent progress. Opto-Electron Adv 5, 210093 (2022). doi: 10.29026/oea.2022.210093 [12] Tanaka K, Arslan D, Fasold S, Steinert M, Sautter J et al. Chiral bilayer all-dielectric metasurfaces. ACS Nano 14, 15926–15935 (2020). doi: 10.1021/acsnano.0c07295 [13] Zhao Y, Askarpour AN, Sun LY, Shi JW, Li XQ et al. Chirality detection of enantiomers using twisted optical metamaterials. Nat Commun 8, 14180 (2017). doi: 10.1038/ncomms14180 [14] Droulias S, Bougas L. Chiral sensing with achiral anisotropic metasurfaces. Phys Rev B 104, 075412 (2021). doi: 10.1103/PhysRevB.104.075412 [15] Solomon ML, Hu J, Lawrence M, García-Etxarri A, Dionne JA. Enantiospecific optical enhancement of chiral sensing and separation with dielectric metasurfaces. ACS Photonics 6, 43–49 (2019). [16] Chen Y, Zhao C, Zhang YZ, Qiu CW. Integrated molar chiral sensing based on high-Q metasurface. Nano Lett 20, 8696–8703 (2020). doi: 10.1021/acs.nanolett.0c03506 [17] Solomon ML, Saleh AAE, Poulikakos LV, Abendroth JM, Tadesse LF et al. Nanophotonic platforms for chiral sensing and separation. Acc Chem Res 53, 588–598 (2020). doi: 10.1021/acs.accounts.9b00460 [18] Ben-Moshe A, Teitelboim A, Oron D, Markovich G. Probing the interaction of quantum dots with chiral capping molecules using circular dichroism spectroscopy. Nano Lett 16, 7467–7473 (2016). doi: 10.1021/acs.nanolett.6b03143 [19] Palermo G, Lio GE, Esposito M, Ricciardi L, Manoccio M et al. Biomolecular sensing at the interface between chiral metasurfaces and hyperbolic metamaterials. ACS Appl Mater Interfaces 12, 30181–30188 (2020). doi: 10.1021/acsami.0c07415 [20] Plum E, Liu XX, Fedotov VA, Chen Y, Tsai DP et al. Metamaterials: optical activity without chirality. Phys Rev Lett 102, 113902 (2009). doi: 10.1103/PhysRevLett.102.113902 [21] Karimullah AS, Jack C, Tullius R, Rotello VM, Cooke G et al. Disposable plasmonics: plastic templated plasmonic metamaterials with tunable chirality. Adv Mater 27, 5610–5616 (2015). doi: 10.1002/adma.201501816 [22] Hajji M, Cariello M, Gilroy C, Kartau M, Syme CD et al. Chiral quantum metamaterial for hypersensitive biomolecule detection. ACS Nano 15, 19905–19916 (2021). doi: 10.1021/acsnano.1c07408 [23] Zhao Y, Xu LG, Ma W, Wang LB, Kuang H et al. Shell-engineered chiroplasmonic assemblies of nanoparticles for zeptomolar DNA detection. Nano Lett 14, 3908–3913 (2014). doi: 10.1021/nl501166m [24] Zheng GC, He JJ, Kumar V, Wang SL, Pastoriza-Santos I et al. Discrete metal nanoparticles with plasmonic chirality. Chem Soc Rev 50, 3738–3754 (2021). doi: 10.1039/C9CS00765B [25] Chen Z, Wang Q, Wu X, Li Z, Jiang YB. Optical chirality sensing using macrocycles, synthetic and supramolecular oligomers/polymers, and nanoparticle based sensors. Chem Soc Rev 44, 4249–4263 (2015). doi: 10.1039/C4CS00531G [26] Gwak J, Park SJ, Choi HY, Lee JH, Jeong KJ et al. Plasmonic enhancement of chiroptical property in enantiomers using a helical array of magnetoplasmonic nanoparticles for ultrasensitive chiral recognition. ACS Appl Mater Interfaces 13, 46886–46893 (2021). doi: 10.1021/acsami.1c14047 [27] Hendry E, Carpy T, Johnston J, Popland M, Mikhaylovskiy RV et al. Ultrasensitive detection and characterization of biomolecules using superchiral fields. Nat Nanotechnol 5, 783–787 (2010). doi: 10.1038/nnano.2010.209 [28] Wang N, Yan W, Qu YR, Ma SQ, Li SZ et al. Intelligent designs in nanophotonics: from optimization towards inverse creation. PhotoniX 2, 22 (2021). doi: 10.1186/s43074-021-00044-y [29] Fan YL, Xu YK, Qiu M, Jin W, Zhang L et al. Phase-controlled metasurface design via optimized genetic algorithm. Nanophotonics 9, 3931–3939 (2020). doi: 10.1515/nanoph-2020-0132 [30] Chen YQ, Hu YQ, Zhao JY, Deng YS, Wang ZL et al. Topology optimization-based inverse design of plasmonic nanodimer with maximum near-field enhancement. Adv Funct Mater 30, 2000642 (2020). doi: 10.1002/adfm.202000642 [31] Fan YL, Chen MK, Qiu M, Lin RJ, Xu YK et al. Experimental demonstration of genetic algorithm based metalens design for generating side-lobe-suppressed, large depth-of-focus light sheet. Laser Photonics Rev 16, 2100425 (2022). doi: 10.1002/lpor.202100425 [32] Jing JX, Yiu YC, Chen C, Lei DY, Shao L et al. A data-mining-assisted design of structural colors on diamond metasurfaces. Adv Photonics Res 3, 2100292 (2022). doi: 10.1002/adpr.202100292 [33] Khatib O, Ren SM, Malof J, Padilla WJ. Deep learning the electromagnetic properties of metamaterials—A comprehensive review. Adv Funct Mater 31, 2101748 (2021). doi: 10.1002/adfm.202101748 [34] Ma TG, Tobah M, Wang HZ, Guo LJ. Benchmarking deep learning-based models on nanophotonic inverse design problems. Opto-Electron Sci 1, 210012 (2022). doi: 10.29026/oes.2022.210012 [35] Krasikov S, Tranter A, Bogdanov A, Kivshar Y. Intelligent metaphotonics empowered by machine learning. Opto-Electron Adv 5, 210147 (2022). doi: 10.29026/oea.2022.210147 [36] Mansouree M, McClung A, Samudrala S, Arbabi A. Large-scale parametrized metasurface design using adjoint optimization. ACS Photonics 8, 455–463 (2021). [37] Jiang JQ, Sell D, Hoyer S, Hickey J, Yang JJ et al. Free-form diffractive metagrating design based on generative adversarial networks. ACS Nano 13, 8872–8878 (2019). doi: 10.1021/acsnano.9b02371 [38] Inampudi S, Mosallaei H. Neural network based design of metagratings. Appl Phys Lett 112, 241102 (2018). doi: 10.1063/1.5033327 [39] Jiang JQ, Fan JA. Global optimization of dielectric metasurfaces using a physics-driven neural network. Nano Lett 19, 5366–5372 (2019). doi: 10.1021/acs.nanolett.9b01857 [40] Gostimirovic D, Ye WN. An open-source artificial neural network model for polarization-insensitive silicon-on-insulator subwavelength grating couplers. IEEE J Sel Top Quantum Electron 25, 8200205 (2019). [41] Melati D, Grinberg Y, Dezfouli MK, Janz S, Cheben P et al. Mapping the global design space of nanophotonic components using machine learning pattern recognition. Nat Commun 10, 4775 (2019). doi: 10.1038/s41467-019-12698-1 [42] Liu ZH, Liu XH, Xiao ZY, Lu CC, Wang HQ et al. Integrated nanophotonic wavelength router based on an intelligent algorithm. Optica 6, 1367–1373 (2019). doi: 10.1364/OPTICA.6.001367 [43] Lu CC, Liu ZH, Wu Y, Xiao ZY, Yu DY et al. Nanophotonic polarization routers based on an intelligent algorithm. Adv Opt Mater 8, 1902018 (2020). doi: 10.1002/adom.201902018 [44] Minkov M, Williamson IAD, Andreani LC, Gerace D, Lou BC et al. Inverse design of photonic crystals through automatic differentiation. ACS Photonics 7, 1729–1741 (2020). doi: 10.1021/acsphotonics.0c00327 [45] Rodier M, Keijzer C, Milner J, Karimullah AS, Roszak AW et al. Biomacromolecular charge chirality detected using chiral plasmonic nanostructures. Nanoscale Horiz 5, 336–344 (2020). doi: 10.1039/C9NH00525K [46] Xu LG, Sun MZ, Cheng P, Gao R, Wang H et al. 2D chiroptical nanostructures for high-performance photooxidants. Adv Funct Mater 28, 1707237 (2018). doi: 10.1002/adfm.201707237 [47] Li GC, Zhang Q, Maier SA, Lei DY. Plasmonic particle-on-film nanocavities: a versatile platform for Plasmon-enhanced spectroscopy and photochemistry. Nanophotonics 7, 1865–1889 (2018). doi: 10.1515/nanoph-2018-0162 [48] Bao ZY, Dai JY, Zhang Q, Ho KH, Li SQ et al. Geometric modulation of induced plasmonic circular dichroism in nanoparticle assemblies based on backaction and field enhancement. Nanoscale 10, 19684–19691 (2018). doi: 10.1039/C8NR07300G -
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Chen YX, Zhang FY, Dang ZB, He X, Luo CX et al. Chiral detection of biomolecules based on reinforcement learning. Opto-Electron Sci 2, 220019 (2023). doi: 10.29026/oes.2023.220019
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Figure 1.
A design algorithm based on reinforcement learning. The entire workflow is a circle of four steps (a-d). (a) Dataset composed of nanostructures and corresponding spectra. The spectra are calculated with electromagnetic simulations. (b) Train different ANNs. (c) Possible optimized structures proposed by ANNs. (d) Classifications for possible optimized structures. Compare the results from different ANNs. The spectra are accurate and the structures are optimized when the ANNs give the same predictions. Other structures are totally new for the existed data and added to dataset.
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Figure 2.
Details of the deep learning algorithm involved. We show the geometry of nanostructures (a–c), parameterization (c), the structure of ANNs (d) and performances of the ANNs (e–f). (a) Periodic arrays of chiral nanostructures. (b) The side view of a structure. (c) The top view of a structure and parameterization. The geometry is encoded to 0 and 1. (d) ANN structure. ANNs analyze parameterized geometry and predict reflective spectra with a right-circularly polarized incidence. ANNs mainly consist of multiple convolutional (Conv) layers and fully connection (FC) layers. (e) ANNs’ performance in prediction of reflective spectra with a right-circularly polarized incidence. (f) ANNs’ performance in prediction of CD spectra.
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Figure 3.
Experiment results for optimized structures at the target wavelength of 850 nm (a–d), 750 nm (e–h) and 925 nm (i–l). (a, e, i) SEM images. (b, f, j) CD spectra of experiments and simulations. (c, g, k) The LCP component of CL mapping. (d, h, l) The RCP component of CL mapping.
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Figure 4.
Differentiation of enantiomers. (a) Structures of microfluidic chips. (b–d) CD spectra of different solutions on metasurfaces with a CD peak at the wavelength of 750 nm, 850 nm and 925 nm. The contrast of solutions of L-glucose, NaCl and D-glucose present an obvious frequency shift
