Influence Of Anodizing Time On Porosity Of Nanopore Structures Grown On Flexible TLC Aluminium Films and Analysis Of Images Using MATLAB Software

Vidyasagar, C. C.; Bannigidad, Parashuram; Muralidhara, H. B.

doi:10.5185/amlett.2016.5967

Cited by 8 publications

(5 citation statements)

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“…These values of nanopore wall thickness, nanopore size and porosity are tabulated in Table 1, Table 2 and Table 3 respectively. Further, the error for each geometrical characteristic in all the images for every segmentation technique is computed using equation (1). The calculated error for nanopore wall thickness, nanopore size and porosity for all the images with different segmentation technique is projected in Table 4, Table 5 and Table 6 respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Further, the proposed values are compared with the manual results and the variation in the results, referred to as an error is computed. The error for each characteristic is computed using equation (1).…”

Section: Proposed Methodsmentioning

confidence: 99%

“…For decades, the researchers have worked on various segmentation techniques. Obtaining nanostructure using global thresholding [1], time effect on Al 2 O 3 [2] was presented by P. Bannigidad. Morphological segmentation of FIB-SEM data is proposed by T. Prill [3].…”

Section: Introductionmentioning

confidence: 99%

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Characterization of Aluminium Oxide Nanoporous Images using different Segmentation Techniques

Bannigidad¹,

Udoshi*²,

Vidyasagar³

2019

IJITEE

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The characterizations of the nanoporous membrane require appropriate segmentation of the pores. The segmentation technique used for one category or class of image may not be suitably applied to the other class of images. Selecting the ubiquitous segmentation technique that can be applied to all image types is a challenging issue. In spite of several decades of research, there is no ubiquitously accepted method for image segmentation and therefore it remains a challenge in digital image processing. The objective of the present study is to automate the system with five different segmentation techniques; global thresholding, active contour, K-means, region growing and watershed to extort the pore from the experimental Al2O3 FESEM images, synthesized using different anodizing parameters. The geometrical features; nanopore wall thickness, pore size and porosity are computed for all the experimental images with the proposed segmentation techniques and are compared with the manual results. The variation in the proposed and manual results, referred to as error is computed. It is observed that the average error for the segmented pore characteristics using global thresholding, active contour, K-means, region growing and watershed are 7%, 14%, 13%, 18% and 20% respectively. The analysis predicts, error in using global thresholding segmentation technique is least, as compared to other methods and thus, it is considered the most appropriate segmentation technique among the five methods mentioned in the present study to segment the experimental Al2O3 FESEM images.

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

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Characterization of Aluminium Oxide Nanoporous Images using different Segmentation Techniques

Bannigidad¹,

Udoshi*²,

Vidyasagar³

2019

IJITEE

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“…These results indicate that there are the cylindrical pores in the AAO films. In the second anodisation for 2 and 3 h, a single cavity has many sub‐cavities inside, and after 4 h, the sub‐cavities combine into a deeper single hole [27, 28]. In Fig.…”

Section: Resultsmentioning

confidence: 99%

Hot embossing of anodic aluminium oxide on high‐density polyethylene: a deeper understanding based on hard surface coating

Boonniyom

Osotchan

Subannajui

2018

Micro & Nano Letters

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A thick anodic aluminium oxide (AAO) was used as a laminated hard coating film on high-density polyethylene (HDPE) without a binder. This thick alumina surface has a large number of polymeric nanofibers rooting inside which makes it a composite between polymer fibre and alumina. The AAO-coated film was crystalline gamma-Al 2 O 3 and its Vickers hardness was higher than super alloy and martensitic stainless steel which has a durable and wear resistance surface despite the substrate is still a lightweight polymer. The film surface was firmly adhered to the polymer substrate. Therefore, it can be applied in many applications which require an extreme hard coating such as a transparent hard film or a special made polymer knife in which the sharpness is hardly worn out.

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“…Parashuram and Vidyasagar [11] used morphological and global thresholding for obtaining nanopore diameter and statistical features. Same authors with Muralidhara [12] using same operations to obtain perimeter of the nanopores. It can be realized that all above methods using methods that need trial and error parameters to give proper results.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Nano-Pore Size Using Image Processing

Ismail

Barzinjy

Jabbar

2017

UHDJST

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A B S T R A C TNanopores, which are nanometer-sized holes, have been utilized in apparatus that point toward sensing a range of molecules such as DNA and RNA and single proteins The important factor for sensing molecules is diameters of nanopores which can be found through a substantial process called segmenting for nanopores of scanning electron microscope (SEM) images. In this investigation, four segmentation methods, namely, threshold, bilateral filter, k-means, and expectation maximizationGaussian mixture model (EM-GMM) which has been utilized to segment three SEM images of nanopores efficiently. The quality of segmentation evaluated objectively through computing Rand index among them. Consequently, the nanopore size of Al 2 O 3 films computed by means of SEM images. This study found that EM-GMM segmenting method gives promising results among other examined methods. It is for their high R-index, minimum adjustment parameters (just one variable which set usually 2), and low consuming time. Hence, it can be used efficiently for computing nanopore count and size.

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Influence Of Anodizing Time On Porosity Of Nanopore Structures Grown On Flexible TLC Aluminium Films and Analysis Of Images Using MATLAB Software

Cited by 8 publications

References 0 publications

Characterization of Aluminium Oxide Nanoporous Images using different Segmentation Techniques

Characterization of Aluminium Oxide Nanoporous Images using different Segmentation Techniques

Hot embossing of anodic aluminium oxide on high‐density polyethylene: a deeper understanding based on hard surface coating

Estimation of Nano-Pore Size Using Image Processing

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