Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-28T10:13:46.868Z Has data issue: false hasContentIssue false
  • English
  • Français

Quantification and Morphology Studies of Nanoporous Alumina Membranes: A New Algorithm for Digital Image Processing

Published online by Cambridge University Press:  24 May 2013

Khoobaram S. Choudhari ,
Pacheeripadikkal Jidesh ,
Parampalli Sudheendra  and
Suresh D. Kulkarni
Khoobaram S. Choudhari*
Affiliation:
Centre for Atomic and Molecular Physics, Manipal University, Manipal, Karnataka 576104, India
Pacheeripadikkal Jidesh
Affiliation:
Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Surathkal, Karnataka 575025, India
Parampalli Sudheendra
Affiliation:
Department of Metallurgical and Materials Engineering, National Institute of Technology Karnataka, Surathkal, Karnataka 575025, India
Suresh D. Kulkarni
Affiliation:
Centre for Atomic and Molecular Physics, Manipal University, Manipal, Karnataka 576104, India
*
*Corresponding author. E-mail: [email protected]
Get access

Abstract

A new mathematical algorithm is reported for the accurate and efficient analysis of pore properties of nanoporous anodic alumina (NAA) membranes using scanning electron microscope (SEM) images. NAA membranes of the desired pore size were fabricated using a two-step anodic oxidation process. Surface morphology of the NAA membranes with different pore properties was studied using SEM images along with computerized image processing and analysis. The main objective was to analyze the SEM images of NAA membranes quantitatively, systematically, and quickly. The method uses a regularized shock filter for contrast enhancement, mathematical morphological operators, and a segmentation process for efficient determination of pore properties. The algorithm is executed using MATLAB, which generates a statistical report on the morphology of NAA membrane surfaces and performs accurate quantification of the parameters such as average pore-size distribution, porous area fraction, and average interpore distances. A good comparison between the pore property measurements was obtained using our algorithm and ImageJ software. This algorithm, with little manual intervention, is useful for optimizing the experimental process parameters during the fabrication of such nanostructures. Further, the algorithm is capable of analyzing SEM images of similar or asymmetrically porous nanostructures where sample and background have distinguishable contrast.

Keywords

nanoporous anodic aluminapore size distributionscanning electron microscopyimage processingmathematical morphologyregularized shock filter
Type
Materials Applications
Information
Microscopy and Microanalysis , Volume 19 , Issue 4 , August 2013 , pp. 1061 - 1072
Copyright
Copyright © Microscopy Society of America 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alvarez, L. & Mazorra, L. (1994). Signal and image restoration using shock filters and anisotropic diffusion. Siam J Numer Anal 31, 590–605.Google Scholar
Belwalkar, A., Grasing, E., Van Geertruyden, W., Huang, Z. & Misiolek, W.Z. (2008). Effect of processing parameters on pore structure and thickness of anodic aluminum oxide (AAO) tubular membranes. J Membr Sci 319, 192–198.CrossRefGoogle ScholarPubMed
Bertholdo, R., Assis, M.C., Hammer, P., Pulcinelli, H.S. & Santilli, C.V. (2010). Controlled growth of anodic aluminium oxide films with hexagonal array of nanometer-sized pores filled with textured copper nanowires. J Eur Ceram Soc 30, 181–186.Google Scholar
Caselles, V., Catte, F., Coll, T. & Dibos, F. (1993). A geometric model for active contours in image processing. Numer Math 66, 1–31.Google Scholar
Caselles, V., Kimmel, R. & Sapiro, G. (1997). Geodesic active contours. Int J Comput Vision 22, 61–79.Google Scholar
Chan, T.F. & Vese, L.A. (2001). Active contours without edges. IEEE Trans Image Process 10, 266–277.Google Scholar
Choi, D.H., Lee, P.S., Hwang, W., Lee, K.H. & Park, H.C. (2006). Measurement of the pore sizes for anodic aluminum oxide (AAO). Curr Appl Phys 6(Suppl 1), e125–e129.Google Scholar
Choudhari, K.S., Jidesh, P. & Udayashankar, N.K. (2012a). Fabrication of nanoporous alumina and their structural characteristics study using SEM image processing and analysis. Syn React Inorg Met 42, 369–375.Google Scholar
Choudhari, K.S., Sudheendra, P. & Udayashankar, N.K. (2012b). Fabrication and high-temperature structural characterization study of porous anodic alumina membranes. J Porous Mat 19, 1053–1062.CrossRefGoogle Scholar
Gong, J., Butler, W.H. & Zangari, G. (2010). Tailoring morphology in free-standing anodic aluminium oxide: Control of barrier layer opening down to the sub-10 nm diameter. Nanoscale 2, 778–785.Google Scholar
Gonzalez, R.C. & Woods, R.E. (2001). Digital Image Processing. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Hernandez, A., Calvo, J.I., Pradanos, P., Palacio, L., Rodriguez, M.L. & De Saja, J.A. (1997). Surface structure of microporous membranes by computerized SEM image analysis applied to anopore filters. J Membr Sci 137, 89–97.Google Scholar
Kass, M., Witkin, A. & Terzopoulos, D. (1988). Snakes: Active contour models. Int J Comput Vision 1, 321–331.CrossRefGoogle Scholar
Li, X., Wang, D., Tang, L., Dong, K., Wu, Y., Yang, P. & Zhang, P. (2009). Controllable synthesis of Ag nanorods using a porous anodic aluminum. Appl Surf Sci 255, 7529–7531.Google Scholar
Malladi, R., Sethian, J.A. & Vemuri, B.C. (1995). Shape modeling with front propagation: A level set approach. IEEE Trans Pattern Anal 17, 158–175.Google Scholar
Masselin, I., Durand-Bourlier, L., Laine, J.M., Sizaret, P.Y., Chasseray, X. & Lemordant, D. (2001). Membrane characterization using microscopic image analysis. J Membr Sci 186, 85–96.Google Scholar
Masuda, H. & Fukuda, K. (1995). Ordered metal nanohole arrays made by a two-step replication of honeycomb structures of anodic alumina. Science 268, 1466–1468.Google Scholar
Nasirpouri, F., Abdollahzadeh, M., Almasi, M.J. & Parvini-Ahmadi, N. (2009). A comparison between self-ordering of nanopores in aluminium oxide films achieved by two- and three-step anodic oxidation. Curr Appl Phys 9, S91–S94.CrossRefGoogle Scholar
Nielsch, K., Choi, J., Schwirn, K., Wehrspohn, R.B. & Gosele, U. (2002). Self-ordering regimes of porous alumina: 10% porosity rule. Nano Lett 2, 677–680.Google Scholar
Osher, S. & Sethian, J.A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J Comput Phys 79, 12–49.Google Scholar
Osmanbeyoglu, H.U., Hur, T.B. & Kim, H.K. (2009). Thin alumina nanoporous membranes for similar size biomolecule separation. J Membr Sci 343, 1–6.Google Scholar
Raimundo, D.S., Caliope, P.B., Huanca, D.R. & Salcedo, W.J. (2009). Anodic porous alumina structural characteristics study based on SEM image processing and analysis. Microelectr J 40, 844–847.Google Scholar
Sahu, G., Wang, K., Gordon, S.W., Zhou, W. & Tarr, M.A. (2012). Core-shell Au–TiO2 nanoarchitectures formed by pulsed laser deposition for enhanced efficiency in dye sensitized solar cells. RSC Adv 2, 3791–3800.Google Scholar
Sulka, G.D., Berzozka, A., Zaraska, L. & Jaskula, M. (2010). Through-hole membranes of nanoporous alumina formed by anodizing in oxalic acid and their applications in fabrication of nanowire arrays. Electrochim Acta 55, 4368–4376.Google Scholar
Sun, W., Chen, T., Chen, C. & Li, J. (2007). A study on membrane morphology by digital image processing. J Membr Sci 305, 93–102.Google Scholar
Thongmee, S., Pang, H.L., Ding, J. & Lin, J.Y. (2009). Fabrication and magnetic properties of metallic nanowires via AAO templates. J Magn Magn Mater 321, 2712–2716.Google Scholar
Torras, C. & Garcia-Valls, R. (2004). Quantification of membrane morphology by interpretation of scanning electron microscopy images. J Membr Sci 233, 119–127.Google Scholar
Zhao, H.K., Chan, T., Merriman, B. & Osher, S. (1996). A variational level set approach to multiphase motion. J Comput Phys 127, 179–195.Google Scholar
Ziel, R., Haus, A. & Tulke, A. (2008). Quantification of the pore size distribution (porosity profiles) in microfiltration membranes by SEM, TEM and computer image analysis. J Membr Sci 323, 241–246.Google Scholar
Supplementary material: PDF

Choudhari Supplementary Material

Appendix

Download Choudhari Supplementary Material(PDF)
PDF 872.7 KB