A complex network approach for nanoparticle agglomeration analysis in nanoscale images
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Complex networks have been widely used in science and technology because of their ability to represent several systems. One of these systems is found in Biochemistry, in which the synthesis of new nanoparticles is a hot topic. However, the interpretation of experimental results in the search of new nanoparticles poses several challenges. This is due to the characteristics of nanoparticle images and due to their multiple intricate properties; one property of recurrent interest is the agglomeration of particles. Addressing this issue, this paper introduces an approach that uses complex networks to detect and describe nanoparticle agglomerates so to foster easier and more insightful analyses. In this approach, each detected particle in an image corresponds to a vertice and the distances between the particles define a criterion for creating edges. Edges are created if the distance is smaller than a radius of interest. Once this network is set, we calculate several discrete measures able to reveal the most outstanding agglomerates in a nanoparticle image. Experimental results using images of scanning tunneling microscopy (STM) of gold nanoparticles demonstrated the effectiveness of the proposed approach over several samples, as reflected by the separability between particles in three usual settings. The results also demonstrated efficacy for both convex and non-convex agglomerates.
KeywordsNanoparticle cluster Agglomeration analysis Complex networks Computer simulations
Compliance with ethical standards
Conflict of interests
The authors declare that they have no conflict of interest.
The authors are thankful to the AG workgroup for the STM images. B. Machado and J. Rodrigues-Jr were partially supported by FAPESP under grants 2011/02918-0 and 2016/02557-0, by CNPq under grant 444985/2014-0, and by funding agency FUNDECT.
- Antiqueira L, Oliveira-Jr O, da Fontoura Costa L, das Graças Volpe Nunes M (2009) A complex network approach to text summarization. Inf Sci 179(5):584–599. 10.1016/j.ins.2008.10.032, http://www.sciencedirect.com/science/article/pii/S0020025508004520 CrossRefGoogle Scholar
- Brunelli R (2009) Template matching techniques in computer vision: theory and practice. Wiley PublishingGoogle Scholar
- Emin S, Singh SP, Han L, Satoh N, Islam A (2011) Colloidal quantum dot solar cells. Solar Energy 85(6):1264–1282. doi: 10.1016/j.solener.2011.02.005. http://www.sciencedirect.com/science/article/pii/S0038092X11000338 CrossRefGoogle Scholar
- Erdos P, Renyi A (1960) On the evolution of random graphs. Publ Math Inst Hungar Acad Sci 5:17–61Google Scholar
- Eustace J, Wang X, Cui Y (2015) Community detection using local neighborhood in complex networks. Physica A 436:665–677. doi: 10.1016/j.physa.2015.05.044, http://www.sciencedirect.com/science/article/pii/S0378437115004598 CrossRefGoogle Scholar
- Gonçalves WN, Machado BB, Bruno OM (2015) A complex network approach for dynamic texture recognition. Neurocomputing 153:211–220. doi: 10.1016/j.neucom.2014.11.034, http://www.sciencedirect.com/science/article/pii/S0925231214015677 CrossRefGoogle Scholar
- Hassellöv M, Kaegi R (2009) Analysis and characterization of manufactured nanoparticles in aquatic environments Environmental and Human Health Impacts of Nanotechnology. Wiley, pp 211–266. doi: 10.1002/9781444307504.ch6
- Kim DA, Hwong AR, Stafford D, Hughes DA, O’Malley AJ, Fowler JH, Christakis NA (2015) Social network targeting to maximise population behaviour change: a cluster randomised controlled trial. The Lancet 386(9989):145–153. doi: 10.1016/S0140-6736(15)60095-2. http://www.sciencedirect.com/science/article/pii/S0140673615600952 CrossRefGoogle Scholar
- Larsen RJ, Marx ML (2012) An introduction to mathematical statistics and its applications, 5th ed. Prentice Hall, Boston, MAGoogle Scholar
- Liao M, Qian Zhao Y, Hua Li X, Shan Dai P, Wen Xu X, Kai Zhang J, Ji Zou B (2016) Automatic segmentation for cell images based on bottleneck detection and ellipse fitting. Neurocomputing 173(3):615–622. doi: 10.1016/j.neucom.2015.08.006, http://www.sciencedirect.com/science/article/pii/S0925231215011406 CrossRefGoogle Scholar
- Newman ME (2004) Who is the best connected scientist?a study of scientific coauthorship networks. In: Ben-Naim E, Frauenfelder H, Toroczkai Z (eds) Complex Networks, Lecture Notes in Physics, vol 650, Springer Berlin Heidelberg, pp 37–370. doi: 10.1007/978-3-540-44485-5_16
- Sugahara KN, Teesalu T, Karmali PP, Kotamraju VR, Agemy L, Girard OM, Hanahan D, Mattrey RF, Ruoslahti E (2009) Tissue-penetrating delivery of compounds and nanoparticles into tumors. Cancer Cell 16(6):510–520. doi: 10.1016/j.ccr.2009.10.013. http://www.sciencedirect.com/science/article/pii/S1535610809003821 CrossRefGoogle Scholar
- Tan PN, Steinbach M, Kumar V (2005) Introduction to data mining. Addison-Wesley Longman Publishing Co., Inc., BostonGoogle Scholar
- Tenenbaum JB, Silva Vd, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323. doi: 10.1126/science.290.5500.2319, http://science.sciencemag.org/content/290/5500/2319.full.pdf CrossRefGoogle Scholar
- Tyler JR, Wilkinson DM, Huberman BA (2003) Communities and technologies. Kluwer, B.V., Deventer, The Netherlands, The Netherlands, pp 81–96. http://dl.acm.org/citation.cfm?id=966263.966268x
- Zhang T, Jia W, Zhu Y, Yang J (2015) Automatic tracking of neural stem cells in sequential digital images. Biocybernetics Biomed Eng 36(1):66–75. doi: 10.1016/j.bbe.2015.10.001, http://www.sciencedirect.com/science/article/pii/S0208521615000728 CrossRefGoogle Scholar