Role of 5 mol% rare earth (La3+) on the structural, microstructural, and magnetic properties of cobalt chromite ceramics

Choudapur, V. H.; Angadi, V. Jagadeesha; Ayachit, N. H.; Ubaidullah, Mohd; Pandit, Bidhan

doi:10.1016/j.ceramint.2023.08.089

Cited by 9 publications

(3 citation statements)

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“…Surface area (nm 2 ) = 68,45,000 nm 2 × 3,14,238 Pixels 5,24,28 Pixels (12) The surface area of segmented image is determined to be 41,02,628 nm 2 by Equation 12.…”

Section: Results Of Segmentationmentioning

confidence: 99%

“…concentrations by solution combustion method. The details of synthesis is published elsewhere [12]. Figure 2 shows some of the SEM images which are collected.…”

Section: Methodsmentioning

confidence: 99%

“…The technique systematically determines the count of ones in the 8 th bit plane for each pixel inside a block using nested loops over the picture blocks depending on the provided block size and appends these counts to the extracted_values list (lines 3-10). A following check verifies that the program delivers a list of retrieved values if the image is not empty, or None if no relevant information was extracted (lines [11][12][13][14][15]. This program provides a systematic framework for examining pixel-level data, allowing for deeper insights into the distribution of number of 1's in the 8th bit plane across image blocks.…”

Section: Low Complexity Algorithmmentioning

confidence: 99%

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Classification of FESEM Images of Nanostructures by Noval Deep Learning Algorithm

Patil,

Choudapur

et al. 2024

Preprint

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SEM (Scanning Electron Microscopy) takes nanoscale pictures, whereas DL (Deep Learning) analyses data using neural networks. Image interpretation is streamlined by the collaboration of SEM and DL, which automates SEM image analysis and material characterisation. This integration improves productivity by quickly extracting relevant information from enormous datasets, highlighting subtle patterns that would otherwise be difficult to detect manually. Authors of this article consider 3 classes of SEM images of various magnifications and a total of 93 images is being produced for analysis and processing. Authors intend to enhance, categorize, and segment the pictures collected. SEM pictures are classified based on doping substance with the host material pure Cobalt Chromite, Neodymium doped Cobalt Chromite and Lanthanum doped Co-balt Chromite. Image quality and appearance are improved via augmentation techniques. The similarity and complexity of images in all three classes and the inclusion of images of different magnification posed a challenge in classification which hinder the accuracy rate of classification process. So authors use the statistical results of the tests to create semi-automatic method for classifying and labeling pictures generated by the SEM. Authors propose a low complexity algorithm, aimed to extract features and increase model performance. This approach offers efficiency gains by minimizing time and computational resources compared to pre-trained models, while maintaining consistent classification results. Achieved a training and testing accuracy of 100% and 78.26% respectively. SEM pictures are also classified using CNN and pre-trained models VGG16, Inception v3. To evaluate and compare performance, comparative studies are used to measure model correctness. SEM images are segmented into regions of interest using an integrated technique that combined the watershed and contours. A segmented picture is used to calculate the surface area of a Cobalt Chromite sample. The surface area of material is determined to be 41,02,628 nm 2 .

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“…Surface area (nm 2 ) = 68,45,000 nm 2 × 3,14,238 Pixels 5,24,28 Pixels (12) The surface area of segmented image is determined to be 41,02,628 nm 2 by Equation 12.…”

Section: Results Of Segmentationmentioning

confidence: 99%

“…concentrations by solution combustion method. The details of synthesis is published elsewhere [12]. Figure 2 shows some of the SEM images which are collected.…”

Section: Methodsmentioning

confidence: 99%