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

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.

Sensing Ability of Ferroelectric Oxide Nanowires Grown in Templates of Nanopores

“…Materials 2020, 13, x FOR PEER REVIEW 4 of 10 theoretical values in nanometers [26], is shown in Figure 2d. It should be noted that most of the nanopores had a geometry close to the theoretical predictions, with 95% coincidence.…”

“…Figure 2a-c shows the top view of the AAO after 30, 45, and 60 min of KNbO 3 sputtering, which resulted in different degrees of gradually-filling nanopores-from fully opened (Figure 2a), through partially opened ( Figure 2b) to fully closed ( Figure 2c). A histogram of the relative nanopore diameters and cell areas (nanopores and separating walls), with respect to the theoretical values in nanometers [26], is shown in Figure 2d. It should be noted that most of the nanopores had a geometry close to the theoretical predictions, with 95% coincidence.…”

Toward the prediction of porous membrane permeability from morphological data

“…But the image itself does not provide numerical results and, in consequence, the image requires further interpretation to obtain them. Texture recognition and other image processing algorithms are used to accomplish this objective . The use of texture recognition methodologies in membrane technology is being more introduced in the last years for several purposes, such as fouling characterization .…”

Quantification of the porosity of membranes by digital images analysis techniques

“…To overcome these lim imaging processing has been implemented analysis and yield the statistically significan that image processing, including fast fourier filtering [7], thresholding, and Hough tra become an essential tool for quantitative SE past decade [9][10][11][12][13][14][15][16].…”