{"title":"利用进动电子衍射数据的晶体变分图","authors":"Marcus H. Hansen","doi":"10.20517/microstructures.2023.17","DOIUrl":null,"url":null,"abstract":"In this work, we developed three methods to map crystallographic variants of samples at the nanoscale by analyzing precession electron diffraction data using a high-temperature shape memory alloy and a VO2 thin film on sapphire as the model systems. The three methods are (I) a user-selecting-reference pattern approach, (II) an algorithm-selecting-reference-pattern approach, and (III) a k-means approach. In the first two approaches, Euclidean distance, Cosine, and Structural Similarity (SSIM) algorithms were assessed for the diffraction pattern similarity quantification. We demonstrated that the Euclidean distance and SSIM methods outperform the Cosine algorithm. We further revealed that the random noise in the diffraction data can dramatically affect similarity quantification. Denoising processes could improve the crystallographic mapping quality. With the three methods mentioned above, we were able to map the crystallographic variants in different materials systems, thus enabling fast variant number quantification and clear variant distribution visualization. The advantages and disadvantages of each approach are also discussed. We expect these methods to benefit researchers who work on martensitic materials, in which the variant information is critical to understand their properties and functionalities.","PeriodicalId":22044,"journal":{"name":"Superlattices and Microstructures","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Crystallographic variant mapping using precession electron diffraction data\",\"authors\":\"Marcus H. Hansen\",\"doi\":\"10.20517/microstructures.2023.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we developed three methods to map crystallographic variants of samples at the nanoscale by analyzing precession electron diffraction data using a high-temperature shape memory alloy and a VO2 thin film on sapphire as the model systems. The three methods are (I) a user-selecting-reference pattern approach, (II) an algorithm-selecting-reference-pattern approach, and (III) a k-means approach. In the first two approaches, Euclidean distance, Cosine, and Structural Similarity (SSIM) algorithms were assessed for the diffraction pattern similarity quantification. We demonstrated that the Euclidean distance and SSIM methods outperform the Cosine algorithm. We further revealed that the random noise in the diffraction data can dramatically affect similarity quantification. Denoising processes could improve the crystallographic mapping quality. With the three methods mentioned above, we were able to map the crystallographic variants in different materials systems, thus enabling fast variant number quantification and clear variant distribution visualization. The advantages and disadvantages of each approach are also discussed. We expect these methods to benefit researchers who work on martensitic materials, in which the variant information is critical to understand their properties and functionalities.\",\"PeriodicalId\":22044,\"journal\":{\"name\":\"Superlattices and Microstructures\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Superlattices and Microstructures\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.20517/microstructures.2023.17\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, CONDENSED MATTER\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Superlattices and Microstructures","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.20517/microstructures.2023.17","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, CONDENSED MATTER","Score":null,"Total":0}
Crystallographic variant mapping using precession electron diffraction data
In this work, we developed three methods to map crystallographic variants of samples at the nanoscale by analyzing precession electron diffraction data using a high-temperature shape memory alloy and a VO2 thin film on sapphire as the model systems. The three methods are (I) a user-selecting-reference pattern approach, (II) an algorithm-selecting-reference-pattern approach, and (III) a k-means approach. In the first two approaches, Euclidean distance, Cosine, and Structural Similarity (SSIM) algorithms were assessed for the diffraction pattern similarity quantification. We demonstrated that the Euclidean distance and SSIM methods outperform the Cosine algorithm. We further revealed that the random noise in the diffraction data can dramatically affect similarity quantification. Denoising processes could improve the crystallographic mapping quality. With the three methods mentioned above, we were able to map the crystallographic variants in different materials systems, thus enabling fast variant number quantification and clear variant distribution visualization. The advantages and disadvantages of each approach are also discussed. We expect these methods to benefit researchers who work on martensitic materials, in which the variant information is critical to understand their properties and functionalities.
期刊介绍:
Micro and Nanostructures is a journal disseminating the science and technology of micro-structures and nano-structures in materials and their devices, including individual and collective use of semiconductors, metals and insulators for the exploitation of their unique properties. The journal hosts papers dealing with fundamental and applied experimental research as well as theoretical studies. Fields of interest, including emerging ones, cover:
• Novel micro and nanostructures
• Nanomaterials (nanowires, nanodots, 2D materials ) and devices
• Synthetic heterostructures
• Plasmonics
• Micro and nano-defects in materials (semiconductor, metal and insulators)
• Surfaces and interfaces of thin films
In addition to Research Papers, the journal aims at publishing Topical Reviews providing insights into rapidly evolving or more mature fields. Written by leading researchers in their respective fields, those articles are commissioned by the Editorial Board.
Formerly known as Superlattices and Microstructures, with a 2021 IF of 3.22 and 2021 CiteScore of 5.4