基于高光谱和神经网络的 P3HT:PCBM 原位降解过程表征

IF 5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
Yang Wang , Zhao Ding , Junli Li , Ting Yang , Jianfeng Chen , Lifeng Bian , Chen Yang
{"title":"基于高光谱和神经网络的 P3HT:PCBM 原位降解过程表征","authors":"Yang Wang ,&nbsp;Zhao Ding ,&nbsp;Junli Li ,&nbsp;Ting Yang ,&nbsp;Jianfeng Chen ,&nbsp;Lifeng Bian ,&nbsp;Chen Yang","doi":"10.1016/j.polymertesting.2024.108606","DOIUrl":null,"url":null,"abstract":"<div><div>In situ online observation of surface morphology during degradation processes is of paramount importance for exploring the stability of organic photovoltaic materials. In this study, we designed an in situ online characterization system based on hyperspectral and neural network technologies, and observed the degradation processes of P3HT:PCBM thin film materials. The system is capable of collecting hyperspectral image data from 101 channels within the 400–700 nm wavelength range for characterizing detailed surface features of materials. Additionally, to automate the processing of hyperspectral image data, we designed a spectral image segmentation algorithm based on neural networks and proposed a foreground attention mechanism to improve the segmentation accuracy of the algorithm. The experimental results indicate that the system can achieve high spectral characterization of P3HT:PCBM thin film materials and automate image data processing through artificial intelligence algorithms, with an image segmentation accuracy of 99.62 %. Furthermore, owing to the higher spectral resolution of this system and its computer-assisted analysis capabilities for material image data, not only are the in-situ variations in size, density, and formation rate of aggregates formed during the thermal degradation process of P3HT:PCBM thin film materials experimentally analyzed, but also the fluorescence changes at the edges of aggregates during the photodegradation process are revealed. The reliable code can be found at the following link: <span><span>https://github.com/HyperSystemAndImageProc/IONFMDP-UHHNN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":20628,"journal":{"name":"Polymer Testing","volume":"140 ","pages":"Article 108606"},"PeriodicalIF":5.0000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characterization of the in-situ degradation process of P3HT:PCBM based on hyperspectral and neural networks\",\"authors\":\"Yang Wang ,&nbsp;Zhao Ding ,&nbsp;Junli Li ,&nbsp;Ting Yang ,&nbsp;Jianfeng Chen ,&nbsp;Lifeng Bian ,&nbsp;Chen Yang\",\"doi\":\"10.1016/j.polymertesting.2024.108606\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In situ online observation of surface morphology during degradation processes is of paramount importance for exploring the stability of organic photovoltaic materials. In this study, we designed an in situ online characterization system based on hyperspectral and neural network technologies, and observed the degradation processes of P3HT:PCBM thin film materials. The system is capable of collecting hyperspectral image data from 101 channels within the 400–700 nm wavelength range for characterizing detailed surface features of materials. Additionally, to automate the processing of hyperspectral image data, we designed a spectral image segmentation algorithm based on neural networks and proposed a foreground attention mechanism to improve the segmentation accuracy of the algorithm. The experimental results indicate that the system can achieve high spectral characterization of P3HT:PCBM thin film materials and automate image data processing through artificial intelligence algorithms, with an image segmentation accuracy of 99.62 %. Furthermore, owing to the higher spectral resolution of this system and its computer-assisted analysis capabilities for material image data, not only are the in-situ variations in size, density, and formation rate of aggregates formed during the thermal degradation process of P3HT:PCBM thin film materials experimentally analyzed, but also the fluorescence changes at the edges of aggregates during the photodegradation process are revealed. The reliable code can be found at the following link: <span><span>https://github.com/HyperSystemAndImageProc/IONFMDP-UHHNN</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":20628,\"journal\":{\"name\":\"Polymer Testing\",\"volume\":\"140 \",\"pages\":\"Article 108606\"},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Polymer Testing\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0142941824002836\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, CHARACTERIZATION & TESTING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Polymer Testing","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0142941824002836","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0

摘要

原位在线观测降解过程中的表面形态对于探索有机光伏材料的稳定性至关重要。在本研究中,我们设计了一种基于高光谱和神经网络技术的原位在线表征系统,并观测了 P3HT:PCBM 薄膜材料的降解过程。该系统能够从 101 个通道收集 400-700 纳米波长范围内的高光谱图像数据,用于表征材料的详细表面特征。此外,为了自动处理高光谱图像数据,我们设计了一种基于神经网络的光谱图像分割算法,并提出了一种前景关注机制,以提高算法的分割精度。实验结果表明,该系统可以实现 P3HT:PCBM 薄膜材料的高光谱表征,并通过人工智能算法实现图像数据的自动化处理,图像分割准确率达到 99.62 %。此外,由于该系统具有较高的光谱分辨率和对材料图像数据的计算机辅助分析能力,不仅能对 P3HT:PCBM 薄膜材料热降解过程中形成的聚集体的尺寸、密度和形成率的原位变化进行实验分析,还能揭示光降解过程中聚集体边缘的荧光变化。可靠的代码见以下链接:https://github.com/HyperSystemAndImageProc/IONFMDP-UHHNN。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characterization of the in-situ degradation process of P3HT:PCBM based on hyperspectral and neural networks
In situ online observation of surface morphology during degradation processes is of paramount importance for exploring the stability of organic photovoltaic materials. In this study, we designed an in situ online characterization system based on hyperspectral and neural network technologies, and observed the degradation processes of P3HT:PCBM thin film materials. The system is capable of collecting hyperspectral image data from 101 channels within the 400–700 nm wavelength range for characterizing detailed surface features of materials. Additionally, to automate the processing of hyperspectral image data, we designed a spectral image segmentation algorithm based on neural networks and proposed a foreground attention mechanism to improve the segmentation accuracy of the algorithm. The experimental results indicate that the system can achieve high spectral characterization of P3HT:PCBM thin film materials and automate image data processing through artificial intelligence algorithms, with an image segmentation accuracy of 99.62 %. Furthermore, owing to the higher spectral resolution of this system and its computer-assisted analysis capabilities for material image data, not only are the in-situ variations in size, density, and formation rate of aggregates formed during the thermal degradation process of P3HT:PCBM thin film materials experimentally analyzed, but also the fluorescence changes at the edges of aggregates during the photodegradation process are revealed. The reliable code can be found at the following link: https://github.com/HyperSystemAndImageProc/IONFMDP-UHHNN.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Polymer Testing
Polymer Testing 工程技术-材料科学:表征与测试
CiteScore
10.70
自引率
5.90%
发文量
328
审稿时长
44 days
期刊介绍: Polymer Testing focuses on the testing, analysis and characterization of polymer materials, including both synthetic and natural or biobased polymers. Novel testing methods and the testing of novel polymeric materials in bulk, solution and dispersion is covered. In addition, we welcome the submission of the testing of polymeric materials for a wide range of applications and industrial products as well as nanoscale characterization. The scope includes but is not limited to the following main topics: Novel testing methods and Chemical analysis • mechanical, thermal, electrical, chemical, imaging, spectroscopy, scattering and rheology Physical properties and behaviour of novel polymer systems • nanoscale properties, morphology, transport properties Degradation and recycling of polymeric materials when combined with novel testing or characterization methods • degradation, biodegradation, ageing and fire retardancy Modelling and Simulation work will be only considered when it is linked to new or previously published experimental results.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信