Nanoparticles Ordering Classification Using Deep Convolutional Neural Networks

Abstract:

Intelligent neural networks are used efficiently for image classification and recognition in various scientific areas. One of the most important of these areas is nanoscience. Researchers are currently seeking to apply various deep learning neural networks models for fast and intelligent prediction and recognition of nanostructures based on scanning electron microscopy images. Models of Deep Convolutional Neural Networks (DCNN) have reached a high accuracy rate in nanoparticles classification and recognition. In fact, the improvement of the classification accuracy strongly relies on the perfect fine-tuning of image data and model parameters and that is what this research has worked for. The aim of this paper is to present a model, specifically the VGG16 convolutional neural network model, for high accurate nanoparticles ordering classification. The model has been used to classify the nanoparticles ordering using a typical dataset of electron microscopy images. In this research, an experiment has been carried out to achieve better accuracy rate in comparison to previously recorded accuracy rates. Data augmentation, modification techniques, and model tuning parameters are applied to excess the ability of the model for classifying the input image to ordered or non-ordered nanoparticles. Compared to the related works, the presented model has outperformed the pervious by achieving an accuracy rate of 97%. In this work, it has been observed that training iterations and balanced training data significantly improve the model performance and enhance the accuracy rate.

• Periodical: Journal of Nano Research (Volume 86)
• Pages: 57-66
• DOI: https://doi.org/10.4028/p-5Rz56j
• Online since: December 2024
• Authors: Mabroukah Amarif*, Asmaah Aejaal, Haleemah Ateeyah
• Keywords: Classification, Deep Convolutional Neural Networks, Nanoparticles, Ordering

References

[1] V.J. Ramsden, Nanotechnology. An Introduction, Elsevier Inc., 2011.

[2] C.P. Poole, Jr. and F.J. Owens, Introduction to Nanotechnology, John Wiley & Sons, Inc., 2003.

[3] A. Nouailhat, An Introduction to Nanoscience and Nanotechnology, John Wiley & Sons, Inc. 2008.

[4] Kashin, A. S., Degtyareva, E. S., Eremin, D. B. and Ananikov, V. P. Exploring the performance of nanostructured reagents with organic-group-defined morphology in cross-coupling reaction. Nat. Commun. 9, 2936 (2018).

DOI: 10.1038/s41467-018-05350-x

[5] Gacutan, E. M. et al. Nanostructured carbon-supported Pd electrocatalysts for ethanol oxidation: synthesis and characterization. Adv. Nat. Sci. Nanosci. Nanotechnol. 3, 045016 (2012).

DOI: 10.1088/2043-6262/3/4/045016

[6] Kashin, A. S. & Ananikov, V. P. Monitoring chemical reactions in liquid media using electron microscopy. Nat. Rev. Chem. 3, 624–637 (2019).

DOI: 10.1038/s41570-019-0133-z

[7] Chen, J., Natte, K. & Wu, X.-F. Pd/C-catalyzed carbonylative C–H activation with DMF as the CO source. Tetrahedron Lett. 56, 6413–6416 (2015).

DOI: 10.1016/j.tetlet.2015.09.142

[8] Felpin, F. X. & Landais, Y. Practical Pd/C-Mediated Allylic Substitution in Water. J. Org. Chem. 70, 6441–6446 (2005).

DOI: 10.1021/jo050952t

[9] Trost, B. M., Kaneko, T., Andersen, N. G., Tappertzhofen, C. & Fahr, B. Total synthesis of aeruginosin 98B. J. Am. Chem. Soc. 134, 18944–18947 (2012).

DOI: 10.1021/ja309947n

[10] Mubeen, S., Zhang, T., Yoo, B., Deshusses, M. A. & Myung, N. V. Palladium Nanoparticles Decorated Single-Walled Carbon Nanotube Hydrogen Sensor. J. Phys. Chem. C 111, 6321–6327 (2007).

DOI: 10.1021/jp067716m

[11] Jia, X. et al. Synthesis of Palladium/Helical Carbon Nanofiber Hybrid Nanostructures and Their Application for Hydrogen Peroxide and Glucose Detection. ACS Appl. Mater. Interfaces 5, 12017–12022 (2013).

DOI: 10.1021/am4037383

[12] Liu, X. & Astruc, D. Development of the Applications of Palladium on Charcoal in Organic Synthesis. Advanced Synthesis and Catalysis, https://doi.org/10.1002/adsc.201800343 (2018).

[13] D.A. Boiko, E.O. Pentsak, V.A. Cherepanova and V.P. Ananikov, Electron microscopy dataset for the recognition of nanoscale ordering effects and location of nanoparticles, J. Scientific data, 7(1), (2020) 101-108.

DOI: 10.1038/s41597-020-0439-1

[14] G. Dahy, M.M. Soliman, H. Alshater, A. Slowik and A.E. Hassanien, Optimized deep networks for the classification of nanoparticles in scanning electron microscopy imaging, Computational Materials Science 223 (2023) 112135.

DOI: 10.1016/j.commatsci.2023.112135

[15] D.A. Boiko, E.O. Pentsak, V.A. Cherepanova, E.G. Gordeev and V.P. Ananikov, Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials, Chemical Science 12.21 (2021): 7428-7441.

DOI: 10.1039/d0sc05696k

[16] Toscano, M.D. Torre, B.A. Juárez, and G.A.G. Mireles, Deep Learning for Nanoparticle Classification in SEM Micrographs: Systematic Mapping, In the 11th International Conference On Software Process Improvement (CIMPS), IEEE-publishing, 2022, pp.119-125.

DOI: 10.1109/cimps57786.2022.10035714

[17] Weilie, Z. and Zhong L. W. Scanning Microscopy for Nanotechnology Scanning Microscopy for Nanotechnology: Techniques and Applications. (Springer New York, 2007).

[18] Reimer, L. Scanning Electron Microscopy: Physics of Image Formation and Microanalysis 2nd edn (Springer-Verlag Berlin Heidelberg,1998).

[19] Singh, A. K. Experimental Methodologies for the Characterization of Nanoparticles. In Engineered Nanoparticles 125–170 (Elsevier, 2016).

[20] O. P. Vilela Neto, Intelligent Computational Nanotechnology: The Role of Computational Intelligence in the Development of Nanoscience and Nanotechnology, J. of Computational and Theoretical Nanoscience, 11, 928–944 (2014).

DOI: 10.1166/jctn.2014.3446

[21] W. Rawat and Z. Wang, Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review, Neural computation 29.9 (2017): 2352-2449.

DOI: 10.1162/neco_a_00990

[22] Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014).

[23] Jia, X. et al. Synthesis of Palladium/Helical Carbon Nanofiber Hybrid Nanostructures and Their Application for Hydrogen Peroxide and Glucose Detection. ACS Appl. Mater. Interfaces 5, 12017–12022 (2013).

DOI: 10.1021/am4037383

[24] Liu, X. and Astruc, D. Development of the Applications of Palladium on Charcoal in Organic Synthesis. Advanced Synthesis and Catalysis, https://doi.org/10.1002/adsc.201800343 (2018).

[25] M.H. Modarres, R. Aversa, S. Cozzini, R. Ciancio, A. Leto, and G.P Brandino, Neural network for nanoscience scanning electron microscope image recognition, Scientific reports 7 (2017): 13282.

DOI: 10.1038/s41598-017-13565-z

[26] D. Kolenov, D. Davidse, J. Le Cam, and S. F. Pereira, Convolutional neural network applied for nanoparticle classification using coherent scatterometry data, Applied Optics 59, no. 27 (2020): 8426-8433.

DOI: 10.1364/ao.399894

[27] Dataset 127:

[28] Dataset 228:

[29] He, K., Zhang, X., Ren, S., and Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (2016): 770-778.

DOI: 10.1109/cvpr.2016.90