• Photonics Research
  • Vol. 9, Issue 2, B38 (2021)
Zhenyu Li1、2、†, Hui Zhang2、†, Binh Thi Thanh Nguyen2, Shaobo Luo2, Patricia Yang Liu2, Jun Zou2, Yuzhi Shi2, Hong Cai3, Zhenchuan Yang1, Yufeng Jin1, Yilong Hao1、5、*, Yi Zhang2、4、6、*, and Ai-Qun Liu2、7、*
Author Affiliations
  • 1National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Institute of Microelectronics, Peking University, Beijing 100871, China
  • 2Quantum Science and Engineering Centre, Nanyang Technological University, Singapore 639798, Singapore
  • 3Institute of Microelectronics, A*STAR (Agency for Science, Technology and Research), Singapore 138634, Singapore
  • 4School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
  • 5e-mail: haoyl@pku.edu.cn
  • 6e-mail: yi_zhang@ntu.edu.sg
  • 7e-mail: eaqliu@ntu.edu.sg
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    DOI: 10.1364/PRJ.411825 Cite this Article Set citation alerts
    Zhenyu Li, Hui Zhang, Binh Thi Thanh Nguyen, Shaobo Luo, Patricia Yang Liu, Jun Zou, Yuzhi Shi, Hong Cai, Zhenchuan Yang, Yufeng Jin, Yilong Hao, Yi Zhang, Ai-Qun Liu. Smart ring resonator–based sensor for multicomponent chemical analysis via machine learning[J]. Photonics Research, 2021, 9(2): B38 Copy Citation Text show less
    References

    [1] E. H. Mordan, J. H. Wade, Z. S. B. Wiersma, E. Pearce, T. O. Pangburn, A. W. deGroot, D. M. Meunier, R. C. Bailey. Silicon photonic microring resonator arrays for mass concentration detection of polymers in isocratic separations. Anal. Chem., 91, 1011-1018(2019).

    [2] R. M. Graybill, C. S. Para, R. C. Bailey. PCR-free, multiplexed expression profiling of microRNAs using silicon photonic microring resonators. Anal. Chem., 88, 10347-10351(2016).

    [3] J. H. Wade, A. T. Alsop, N. R. Vertin, H. Yang, M. D. Johnson, R. C. Bailey. Rapid, multiplexed phosphoprotein profiling using silicon photonic sensor arrays. ACS Cent. Sci., 1, 374-382(2015).

    [4] J. H. Wade, R. C. Bailey. Applications of optical microcavity resonators in analytical chemistry. Annu. Rev. Anal. Chem., 9, 1-25(2016).

    [5] Y. Sun, X. Fan. Optical ring resonators for biochemical and chemical sensing. Anal. Bioanal. Chem., 399, 205-211(2011).

    [6] C. D. K. Sloan, M. T. Marty, S. G. Sligar, R. C. Bailey. Interfacing lipid bilayer nanodiscs and silicon photonic sensor arrays for multiplexed protein-lipid and protein-membrane protein interaction screening. Anal. Chem., 85, 2970-2976(2013).

    [7] W. W. Shia, R. C. Bailey. Single domain antibodies for the detection of ricin using silicon photonic microring resonator arrays. Anal. Chem., 85, 805-810(2013).

    [8] D. Patra, A. Mishra. Recent developments in multi-component synchronous fluorescence scan analysis. TrAC Trends Anal. Chem., 21, 787-798(2002).

    [9] A. H. Kamal, S. F. El-Malla, S. F. Hammad. A review on UV spectrophotometric methods for simultaneous multicomponent analysis. Eur. J. Pharm. Med. Res., 3, 348-360(2016).

    [10] S. J. Barton, B. M. Hennelly, T. Ward, K. Domijan, J. Lowry. A review of Raman for multicomponent analysis. Proc. SPIE, 9129, 91290C(2014).

    [11] I. Toumi, S. Caldarelli, B. Torrésani. A review of blind source separation in NMR spectroscopy. Prog. Nucl. Magn. Reson. Spectrosc., 81, 37-64(2014).

    [12] P. Geladi, B. R. Kowalski. Partial least-squares regression: a tutorial. Anal. Chim. Acta, 185, 1-17(1986).

    [13] R. Wehrens, B.-H. Mevik. The pls package: principal component and partial least squares regression in R. J. Stat. Softw., 18, 1-24(2007).

    [14] M. C. U. Araújo, T. C. B. Saldanha, R. K. H. Galvão, T. Yoneyama, H. C. Chame, V. Visani. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics Intellig. Lab. Syst., 57, 65-73(2001).

    [15] Y. Roggo, P. Chalus, L. Maurer, C. Lema-Martinez, A. Edmond, N. Jent. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J. Pharm. Biomed. Anal., 44, 683-700(2007).

    [16] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436-444(2015).

    [17] L. Deng, D. J. F. Yu. Deep learning: methods and applications. Found. Trends Signal Process., 7, 197-387(2014).

    [18] J. J. N. Schmidhuber. Deep learning in neural networks: an overview. Neural Netw., 61, 85-117(2015).

    [19] H. M. Robison, P. Escalante, E. Valera, C. L. Erskine, L. Auvil, H. C. Sasieta, C. Bushell, M. Welge, R. C. Bailey. Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification. Integr. Biol., 11, 16-25(2019).

    [20] J. Vamathevan, D. Clark, P. Czodrowski, I. Dunham, E. Ferran, G. Lee, B. Li, A. Madabhushi, P. Shah, M. Spitzer, S. Zhao. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov., 18, 463-477(2019).

    [21] F. Cheng, Z. Zhao. Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J. Am. Med. Inform. Assoc., 21, e278-e286(2014).

    [22] C. A. Ronao, S.-B. Cho. Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst. Appl., 59, 235-244(2016).

    [23] W. Zhao, A. Bhushan, A. D. Santamaria, M. G. Simon, C. E. Davis. Machine learning: a crucial tool for sensor design. Algorithms, 1, 130-152(2008).

    [24] A. Moraru, M. Pesko, M. Porcius, C. Fortuna, D. J. Mladenic. Using machine learning on sensor data. J. Comput. Inf. Syst., 18, 341-347(2010).

    [25] M. A. Alsheikh, S. Lin, D. Niyato, H.-P. Tan. Machine learning in wireless sensor networks: algorithms, strategies, and applications. Commun. Surveys Tuts., 16, 1996-2018(2014).

    [26] Z. Hou, T. Tang, J. Shen, C. Li, F. Li. Prediction network of metamaterial with split ring resonator based on deep learning. Nanoscale Res. Lett., 15, 83(2020).

    [27] Y. Chen, J. Zhu, Y. Xie, N. Feng, Q. H. Liu. Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network. Nanoscale, 11, 9749-9755(2019).

    [28] W. Ma, F. Cheng, Y. Liu. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano, 12, 6326-6334(2018).

    [29] J. S. T. Smalley, Y. Zhao, A. A. Nawaz, Q. Hao, Y. Ma, I.-C. Khoo, T. J. Huang. High contrast modulation of plasmonic signals using nanoscale dual-frequency liquid crystals. Opt. Express, 19, 15265-15274(2011).

    [30] M. Ian Lapsley, A. Shahravan, Q. Hao, B. Krishna Juluri, S. Giardinelli, M. Lu, Y. Zhao, I.-K. Chiang, T. Matsoukas, T. J. Huang. Shifts in plasmon resonance due to charging of a nanodisk array in argon plasma. Appl. Phys. Lett., 100, 101903(2012).

    [31] R. A. Potyrailo, J. E. Brewer, B. Cheng, M. Carpenter, N. M. Houlihan, A. Kolmakov. Bio-inspired gas sensing: boosting performance with sensor optimization guided by ‘machine learning’. Faraday Discuss., 223, 161-182(2020).

    [32] Z. S. Ballard, D. Shir, A. Bhardwaj, S. Bazargan, S. Sathianathan, A. Ozcan. Computational sensing using low-cost and mobile plasmonic readers designed by machine learning. ACS Nano, 11, 2266-2274(2017).

    [33] X. Feng, G. Zhang, L. K. Chin, A. Q. Liu, B. Liedberg. Highly sensitive, label-free detection of 2,4-dichlorophenoxyacetic acid using an optofluidic chip. ACS Sens., 2, 955-960(2017).

    [34] Y. Shi, H. Zhao, K. T. Nguyen, Y. Zhang, L. K. Chin, T. Zhu, Y. Yu, H. Cai, P. H. Yap, P. Y. Liu, S. Xiong, J. Zhang, C.-W. Qiu, C. T. Chan, A. Q. Liu. Nanophotonic array-induced dynamic behavior for label-free shape-selective bacteria sieving. ACS Nano, 13, 12070-12080(2019).

    [35] Y. Shi, H. Zhao, L. K. Chin, Y. Zhang, P. H. Yap, W. Ser, C.-W. Qiu, A. Q. Liu. Optical potential-well array for high-selectivity, massive trapping and sorting at nanoscale. Nano Lett., 20, 5193-5200(2020).

    [36] Z. Li, J. Zou, H. Zhu, B. T. T. Nguyen, Y. Shi, P. Y. Liu, R. C. Bailey, J. Zhou, H. Wang, Z. Yang, Y. Jin, P. H. Yap, H. Cai, Y. Hao, A. Q. Liu. Biotoxoid photonic sensors with temperature insensitivity using a cascade of ring resonator and Mach-Zehnder interferometer. ACS Sens., 5, 2448-2456(2020).

    [37] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard. TensorFlow: a system for large-scale machine learning. 12th USENIX Symposium on Operating Systems Design and Implementation, 265-283(2016).

    [38] H. Zhang, M. F. Karim, S. Zheng, H. Cai, Y. Gu, S. S. Chen, H. Yu, A. Q. Liu. A high-resolution dual-microring-based silicon photonic sensor using electronic integrated circuit. CLEO: Applications and Technology, ATh4O.4(2018).

    [39] H. Zhang, M. F. Karim, S. Zheng, H. Cai, Y. Gu, S. S. Chen, H. Yu, A. Q. Liu. Machine learning and silicon photonic sensor for complex chemical components determination. CLEO: Science and Innovations, JW2A.54(2018).

    [40] M. C. Cardenosa-Rubio, H. M. Robison, R. C. Bailey. Recent advances in environmental and clinical analysis using microring resonator-based sensors. Curr. Opin. Environ. Sci. Health, 10, 38-46(2019).

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    Zhenyu Li, Hui Zhang, Binh Thi Thanh Nguyen, Shaobo Luo, Patricia Yang Liu, Jun Zou, Yuzhi Shi, Hong Cai, Zhenchuan Yang, Yufeng Jin, Yilong Hao, Yi Zhang, Ai-Qun Liu. Smart ring resonator–based sensor for multicomponent chemical analysis via machine learning[J]. Photonics Research, 2021, 9(2): B38
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