{"title":"基于高光谱和神经网络的 P3HT:PCBM 原位降解过程表征","authors":"","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":null,"pages":null},"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\":\"\",\"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\":null,\"pages\":null},\"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}
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.
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.