Prediction of grain boundary of a composite microstructure using digital image processing: A comparative study

Biswal, Sweta Rani; Sahoo, Tapasmini; Sahoo, S.

doi:10.1016/j.matpr.2020.09.559

Cited by 12 publications

(2 citation statements)

References 14 publications

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“…The main process involves digitization of the image (11,12) which can be graphically represented by Figure 1. Such favorable characteristics of image processing led researchers to realize its potential for providing valuable microstructural information in the process of improving performance and efficiency of clean energy systems (13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Automatic Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Data Processing Framework

Batool¹,

Godoy²,

Birnbach³

et al. 2021

ECS Trans.

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Computer-aided data acquisition, analysis and interpretation have been employed in various research areas generating useful information. Among these, image processing is most often implemented for post-processing of material characterization data. However, to tackle ambiguity of multi-component materials analysis, spectral data analysis can be used instead. The current study introduces a unique Python-based data processing method for indepth analysis of energy dispersive spectroscopy (EDS) elemental maps to analyze agglomerate size distribution, average area of each component and their overlap. The framework developed in this study is applied to examine interaction of Cerium (Ce) and Palladium (Pd) particles in membrane electrode assembly (MEA) of Anion-Exchange Membrane Fuel Cell (AEMFC) and investigate if this approach can be utilized to predict the fuel cell's electrochemical behavior. The study concludes that the developed framework is a promising method for automatic data extraction and can be beneficial for use in a variety of clean energy applications.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Automatic Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Data Processing Framework

Batool¹,

Godoy²,

Birnbach³

et al. 2021

ECS Trans.

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“…1. 15,16 However, although computer-based automated image processing has its challenges, it is far superior and has unarguably overshadowed manual image processing techniques in recent decades.…”

mentioning

confidence: 99%

Evaluation of Semi-Automatic Compositional and Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Image and Data Processing Framework for Fuel Cell Applications

Batool¹,

Godoy²,

Birnbach³

et al. 2023

J. Electrochem. Soc.

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Computer-aided data acquisition, analysis, and interpretation are rapidly gaining traction in numerous facets of research. One of the subsets of this field, image processing, is most often implemented for post-processing material microstructural characterization data to understand better and predict materials' features, properties, and behaviors at multiple scales. However, to tackle the ambiguity of multi-component materials analysis, spectral data can be used in combination with image processing. The current study introduces a novel Python-based image and data processing method for in-depth analysis of energy dispersive spectroscopy (EDS) elemental maps to analyze multi-component agglomerate size distribution, the average area of each component, and their overlap. The framework developed in this study is applied to examine the interaction of cerium oxide and palladium particles in the membrane electrode assembly of an anion-exchange membrane fuel cell and to investigate if this approach can be correlated to the cell performance. The study also performs a sensitivity analysis of several parameters and their effect on the computed results. The developed framework is a promising method for semi-automatic data processing and can be further advanced towards a fully automatic analysis of similar data types in the field of clean energy materials and broader.

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Self-lubricating Composite: A New Generation Metal Metrix Composite for Efficient Tribological Application

Biswal,

Sahoo

2023

Springer Proceedings in Materials

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Prediction of grain boundary of a composite microstructure using digital image processing: A comparative study

Cited by 12 publications

References 14 publications

Evaluation of Automatic Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Data Processing Framework

Evaluation of Automatic Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Data Processing Framework

Evaluation of Semi-Automatic Compositional and Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Image and Data Processing Framework for Fuel Cell Applications

Self-lubricating Composite: A New Generation Metal Metrix Composite for Efficient Tribological Application

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