Hee-Beom Lee, Min-Hyoung Jung, Young-Hoon Kim, Eun-Byeol Park, Woo-Sung Jang, Seon-Je Kim, Ki-ju Choi, Ji-young Park, Kee-bum Hwang, Jae-Hyun Shim, Songhun Yoon, Young-Min Kim
{"title":"基于深度学习图像分割的高容量镍基氧化物阴极二次粒子孔隙度可靠测量","authors":"Hee-Beom Lee, Min-Hyoung Jung, Young-Hoon Kim, Eun-Byeol Park, Woo-Sung Jang, Seon-Je Kim, Ki-ju Choi, Ji-young Park, Kee-bum Hwang, Jae-Hyun Shim, Songhun Yoon, Young-Min Kim","doi":"10.1186/s40543-023-00407-z","DOIUrl":null,"url":null,"abstract":"The optimization of geometrical pore control in high-capacity Ni-based cathode materials is required to enhance the cyclic performance of lithium-ion batteries. Enhanced porosity improves lithium-ion mobility by increasing the electrode–electrolyte contact area and reducing the number of ion diffusion pathways. However, excessive porosity can diminish capacity, thus necessitating optimizing pore distribution to compromise the trade-off relation. Accordingly, a statistically meaningful porosity estimation of electrode materials is required to engineer the local pore distribution inside the electrode particles. Conventional scanning electron microscopy (SEM) image-based porosity measurement can be used for this purpose. However, it is labor-intensive and subjected to human bias for low-contrast pore images, thereby potentially lowering measurement accuracy. To mitigate these difficulties, we propose an automated image segmentation method for the reliable porosity measurement of cathode materials using deep convolutional neural networks specifically trained for the analysis of porous cathode materials. Combined with the preprocessed SEM image datasets, the model trained for 100 epochs exhibits an accuracy of > 97% for feature segmentation with regard to pore detection on the input datasets. This automated method considerably reduces manual effort and human bias related to the digitization of pore features in serial section SEM image datasets used in 3D electron tomography. ","PeriodicalId":14967,"journal":{"name":"Journal of Analytical Science and Technology","volume":"56 5","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning image segmentation for the reliable porosity measurement of high-capacity Ni-based oxide cathode secondary particles\",\"authors\":\"Hee-Beom Lee, Min-Hyoung Jung, Young-Hoon Kim, Eun-Byeol Park, Woo-Sung Jang, Seon-Je Kim, Ki-ju Choi, Ji-young Park, Kee-bum Hwang, Jae-Hyun Shim, Songhun Yoon, Young-Min Kim\",\"doi\":\"10.1186/s40543-023-00407-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The optimization of geometrical pore control in high-capacity Ni-based cathode materials is required to enhance the cyclic performance of lithium-ion batteries. Enhanced porosity improves lithium-ion mobility by increasing the electrode–electrolyte contact area and reducing the number of ion diffusion pathways. However, excessive porosity can diminish capacity, thus necessitating optimizing pore distribution to compromise the trade-off relation. Accordingly, a statistically meaningful porosity estimation of electrode materials is required to engineer the local pore distribution inside the electrode particles. Conventional scanning electron microscopy (SEM) image-based porosity measurement can be used for this purpose. However, it is labor-intensive and subjected to human bias for low-contrast pore images, thereby potentially lowering measurement accuracy. To mitigate these difficulties, we propose an automated image segmentation method for the reliable porosity measurement of cathode materials using deep convolutional neural networks specifically trained for the analysis of porous cathode materials. 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Deep learning image segmentation for the reliable porosity measurement of high-capacity Ni-based oxide cathode secondary particles
The optimization of geometrical pore control in high-capacity Ni-based cathode materials is required to enhance the cyclic performance of lithium-ion batteries. Enhanced porosity improves lithium-ion mobility by increasing the electrode–electrolyte contact area and reducing the number of ion diffusion pathways. However, excessive porosity can diminish capacity, thus necessitating optimizing pore distribution to compromise the trade-off relation. Accordingly, a statistically meaningful porosity estimation of electrode materials is required to engineer the local pore distribution inside the electrode particles. Conventional scanning electron microscopy (SEM) image-based porosity measurement can be used for this purpose. However, it is labor-intensive and subjected to human bias for low-contrast pore images, thereby potentially lowering measurement accuracy. To mitigate these difficulties, we propose an automated image segmentation method for the reliable porosity measurement of cathode materials using deep convolutional neural networks specifically trained for the analysis of porous cathode materials. Combined with the preprocessed SEM image datasets, the model trained for 100 epochs exhibits an accuracy of > 97% for feature segmentation with regard to pore detection on the input datasets. This automated method considerably reduces manual effort and human bias related to the digitization of pore features in serial section SEM image datasets used in 3D electron tomography.
期刊介绍:
The Journal of Analytical Science and Technology (JAST) is a fully open access peer-reviewed scientific journal published under the brand SpringerOpen. JAST was launched by Korea Basic Science Institute in 2010. JAST publishes original research and review articles on all aspects of analytical principles, techniques, methods, procedures, and equipment. JAST’s vision is to be an internationally influential and widely read analytical science journal. Our mission is to inform and stimulate researchers to make significant professional achievements in science. We aim to provide scientists, researchers, and students worldwide with unlimited access to the latest advances of the analytical sciences.