Pub Date : 2021-06-20DOI: 10.1109/PVSC43889.2021.9518702
Zubair Abdullah‐Vetter, Yoann Buratti, P. Dwivedi, A. Sowmya, T. Trupke, Z. Hameiri
Defect detection is a critical aspect of assuring the quality and reliability of silicon solar cells and modules. Luminescence imaging has been widely adopted as a fast method for analyzing photovoltaic devices and detecting faults. However, visual inspection of luminescence images is too slow for the expected manufacturing throughput. In this study, we propose a deep learning approach that identifies and localizes defects in electroluminescence images. Images are split into 16 tiles prior to training and treated as separate images for classification. The classified tiles provide both defect labels and their positions within the cell. We demonstrate the use of this novel approach to replace visual inspection of luminescence images in photovoltaic manufacturing lines to achieve fast and accurate defect detection.
{"title":"Localization of defects in solar cells using luminescence images and deep learning","authors":"Zubair Abdullah‐Vetter, Yoann Buratti, P. Dwivedi, A. Sowmya, T. Trupke, Z. Hameiri","doi":"10.1109/PVSC43889.2021.9518702","DOIUrl":"https://doi.org/10.1109/PVSC43889.2021.9518702","url":null,"abstract":"Defect detection is a critical aspect of assuring the quality and reliability of silicon solar cells and modules. Luminescence imaging has been widely adopted as a fast method for analyzing photovoltaic devices and detecting faults. However, visual inspection of luminescence images is too slow for the expected manufacturing throughput. In this study, we propose a deep learning approach that identifies and localizes defects in electroluminescence images. Images are split into 16 tiles prior to training and treated as separate images for classification. The classified tiles provide both defect labels and their positions within the cell. We demonstrate the use of this novel approach to replace visual inspection of luminescence images in photovoltaic manufacturing lines to achieve fast and accurate defect detection.","PeriodicalId":6788,"journal":{"name":"2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)","volume":"10 1","pages":"0745-0749"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85950522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-20DOI: 10.1109/PVSC43889.2021.9518487
Francisco Araya, H. Colin
Half-cell modules are claimed to offer advantages over full cell modules due to reduced resistive losses and a better behavior regarding shading. For bifacial modules of this kind, inhomogeneity of the rear side irradiance, due to their environment, is investigated in order to be well representative in a simulation tool, developed at INES, dedicated to bifacial PV. The tool relies on a ray tracing optical model, enabling the calculation of the rear irradiance on each cell, and an electrical model, calculating the resulting IV curve of each cell. Some experiments have been conducted on specific modules to assess the quality of simulation (of both of the models), in normal and shadowed configurations, and its ability to reflect the mismatch of performance within the cells. Present results are promising: good accuracy is obtained on rather cloudy days, but for clear days further improvements and experimental validation are required to better take into account the real rear irradiance received on each cell.
{"title":"Behaviour of half-cell modules regarding inhomogeneous irradiance on the rear-face","authors":"Francisco Araya, H. Colin","doi":"10.1109/PVSC43889.2021.9518487","DOIUrl":"https://doi.org/10.1109/PVSC43889.2021.9518487","url":null,"abstract":"Half-cell modules are claimed to offer advantages over full cell modules due to reduced resistive losses and a better behavior regarding shading. For bifacial modules of this kind, inhomogeneity of the rear side irradiance, due to their environment, is investigated in order to be well representative in a simulation tool, developed at INES, dedicated to bifacial PV. The tool relies on a ray tracing optical model, enabling the calculation of the rear irradiance on each cell, and an electrical model, calculating the resulting IV curve of each cell. Some experiments have been conducted on specific modules to assess the quality of simulation (of both of the models), in normal and shadowed configurations, and its ability to reflect the mismatch of performance within the cells. Present results are promising: good accuracy is obtained on rather cloudy days, but for clear days further improvements and experimental validation are required to better take into account the real rear irradiance received on each cell.","PeriodicalId":6788,"journal":{"name":"2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)","volume":"45 1","pages":"1073-1079"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79816031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-20DOI: 10.1109/PVSC43889.2021.9519077
Kevin Anderson, J. Kemnitz, Matthew Boyd
Capacity testing is a routine procedure for assessing a photovoltaic system’s performance relative to expectations. The most common test method involves fitting a regression model that predicts system output power using operating weather conditions including wind speed. Structural modifications to the regression model to incorporate wind in different ways improved the model’s ability to fit measured system performance, but the observed improvements were small and unlikely to change the result of a capacity test. However, the results showed that the choice of reporting wind speed and inclusion or exclusion of wind speed in the performance model used as the test benchmark can significantly change the test result.
{"title":"Evaluating cell temperature models and the effect of wind speed in PV system capacity testing","authors":"Kevin Anderson, J. Kemnitz, Matthew Boyd","doi":"10.1109/PVSC43889.2021.9519077","DOIUrl":"https://doi.org/10.1109/PVSC43889.2021.9519077","url":null,"abstract":"Capacity testing is a routine procedure for assessing a photovoltaic system’s performance relative to expectations. The most common test method involves fitting a regression model that predicts system output power using operating weather conditions including wind speed. Structural modifications to the regression model to incorporate wind in different ways improved the model’s ability to fit measured system performance, but the observed improvements were small and unlikely to change the result of a capacity test. However, the results showed that the choice of reporting wind speed and inclusion or exclusion of wind speed in the performance model used as the test benchmark can significantly change the test result.","PeriodicalId":6788,"journal":{"name":"2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)","volume":"41 1","pages":"1663-1669"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86911125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-20DOI: 10.1109/PVSC43889.2021.9518879
Karan Rane, Navni N. Verma, Ardeshir Contractor, N. Shiradkar
This paper describes the procedure to build a finite element (FE) model for computation of module temperature distribution for given irradiance, wind speed, and ambient temperature. Temperature calculation using the Sandia model and the temperature computation using a physics based model assuming constant heat transfer coefficient are compared with the results obtained using a multiphysics solver. The multiphysics based model was found to closely match the mean field measurement. Such a model can be useful to accurately quantify the benefits in energy yield achieved through cooling of the module due to new bill of material components such as conductive backsheets under various field conditions.
{"title":"Finite Element Analysis Model of a PV module for Thermal Assessment","authors":"Karan Rane, Navni N. Verma, Ardeshir Contractor, N. Shiradkar","doi":"10.1109/PVSC43889.2021.9518879","DOIUrl":"https://doi.org/10.1109/PVSC43889.2021.9518879","url":null,"abstract":"This paper describes the procedure to build a finite element (FE) model for computation of module temperature distribution for given irradiance, wind speed, and ambient temperature. Temperature calculation using the Sandia model and the temperature computation using a physics based model assuming constant heat transfer coefficient are compared with the results obtained using a multiphysics solver. The multiphysics based model was found to closely match the mean field measurement. Such a model can be useful to accurately quantify the benefits in energy yield achieved through cooling of the module due to new bill of material components such as conductive backsheets under various field conditions.","PeriodicalId":6788,"journal":{"name":"2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)","volume":"42 1","pages":"2555-2558"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87099589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-20DOI: 10.1109/PVSC43889.2021.9518787
H. A. Roy, Aysha Alremeithi, Mohammad Atyani, Mario Farina, A. Alnuaimi
Evaluation of field performance of photovoltaic (PV) systems is crucial to ensure efficiency and reliability during operational lifetime of the PV plant. This is particularly significant for utility-scale PV projects considering the technical and economic consequences of performance degradation. The study evaluates the performance of two photovoltaic systems which form a part of the largest PV rooftop installation in the Middle East. The IEC 61724 standard provides the framework to gather, organize and effectively analyze the field data.
{"title":"Field Performance Evaluation of the Largest Rooftop PV Project in the Middle East","authors":"H. A. Roy, Aysha Alremeithi, Mohammad Atyani, Mario Farina, A. Alnuaimi","doi":"10.1109/PVSC43889.2021.9518787","DOIUrl":"https://doi.org/10.1109/PVSC43889.2021.9518787","url":null,"abstract":"Evaluation of field performance of photovoltaic (PV) systems is crucial to ensure efficiency and reliability during operational lifetime of the PV plant. This is particularly significant for utility-scale PV projects considering the technical and economic consequences of performance degradation. The study evaluates the performance of two photovoltaic systems which form a part of the largest PV rooftop installation in the Middle East. The IEC 61724 standard provides the framework to gather, organize and effectively analyze the field data.","PeriodicalId":6788,"journal":{"name":"2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)","volume":"67 1","pages":"0468-0471"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86087323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-20DOI: 10.1109/PVSC43889.2021.9518440
P. Keelin, A. Kubiniec, A. Bhat, Marc J. R. Perez, J. Dise, R. Perez, J. Schlemmer
2020 was the most active wildfire season in recent history. This study leverages solar models to quantify the solar impacts of wildfire smoke in western North America 2001 – November 2020. We observe a sharp increase in the number of days impacted by aerosol events. Record deviations in clear sky DNI are found at the Hanford, CA, Boulder, CO, and Desert Rock, NV study locations. Total sunlight (GHI) for September was diminished by up to 20% in some locations; California’s Central Valley and parts of the Columbia River Basin were hardest hit by the smoke. At the Hanford study location, the Aug.- Oct. 2020 deviations in modeled energy output totaled -5.9% of the historical annual average (2001-2019). The analysis demonstrates that wildfires are an important risk to production for solar projects in western North America.
{"title":"Quantifying the solar impacts of wildfire smoke in western North America","authors":"P. Keelin, A. Kubiniec, A. Bhat, Marc J. R. Perez, J. Dise, R. Perez, J. Schlemmer","doi":"10.1109/PVSC43889.2021.9518440","DOIUrl":"https://doi.org/10.1109/PVSC43889.2021.9518440","url":null,"abstract":"2020 was the most active wildfire season in recent history. This study leverages solar models to quantify the solar impacts of wildfire smoke in western North America 2001 – November 2020. We observe a sharp increase in the number of days impacted by aerosol events. Record deviations in clear sky DNI are found at the Hanford, CA, Boulder, CO, and Desert Rock, NV study locations. Total sunlight (GHI) for September was diminished by up to 20% in some locations; California’s Central Valley and parts of the Columbia River Basin were hardest hit by the smoke. At the Hanford study location, the Aug.- Oct. 2020 deviations in modeled energy output totaled -5.9% of the historical annual average (2001-2019). The analysis demonstrates that wildfires are an important risk to production for solar projects in western North America.","PeriodicalId":6788,"journal":{"name":"2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)","volume":"92 1","pages":"1401-1404"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86173935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-20DOI: 10.1109/PVSC43889.2021.9518450
Hannes Wagner-Mohnsen, S. Esefelder, B. Klöter, B. Mitchell, C. Schinke, Dennis Bredemeier, P. Jäger, R. Brendel
Advanced mathematical methods, like machine learning or genetic algorithms, have the potential to further accelerate the computer-aided optimization of processes. In this paper we combine the power of sophisticated numerical simulations with these modern concepts. The goal is to combine the strength of both approaches, high predictive quality from numerical models and fast prediction power of machine learning and genetic algorithms. We demonstrate this on a POCl3 diffusion process and optimize an industry relevant PERC solar cell up to 23.4%. This approach is not limited to POCl3 or PECR cells and can be applied to other cell architectures or processes.
{"title":"Combining Numerical Simulations, Machine Learning and Genetic Algorithms for Optimizing a POCl3 Diffusion Process","authors":"Hannes Wagner-Mohnsen, S. Esefelder, B. Klöter, B. Mitchell, C. Schinke, Dennis Bredemeier, P. Jäger, R. Brendel","doi":"10.1109/PVSC43889.2021.9518450","DOIUrl":"https://doi.org/10.1109/PVSC43889.2021.9518450","url":null,"abstract":"Advanced mathematical methods, like machine learning or genetic algorithms, have the potential to further accelerate the computer-aided optimization of processes. In this paper we combine the power of sophisticated numerical simulations with these modern concepts. The goal is to combine the strength of both approaches, high predictive quality from numerical models and fast prediction power of machine learning and genetic algorithms. We demonstrate this on a POCl3 diffusion process and optimize an industry relevant PERC solar cell up to 23.4%. This approach is not limited to POCl3 or PECR cells and can be applied to other cell architectures or processes.","PeriodicalId":6788,"journal":{"name":"2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)","volume":"24 1","pages":"0528-0531"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86553853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-20DOI: 10.1109/PVSC43889.2021.9518837
N. Jost, S. Askins, R. Dixon, Mathieu Ackermann, C. Domínguez, I. Antón
Micro-concentrator photovoltaics (micro-CPV) consists of the reduction in size of the components of the conventional concentrator photovoltaic (CPV) technology, attaining equally high efficiencies and reducing material costs and manufacturing costs. In this publication we focus on the implementation of high throughput manufacturing methods for the interconnection of the solar cells. The goal is to enable large area interconnection of thousands of micro-solar cells with a low cost of 3€/m2. A proof-of-concept was achieved for interconnection via directly printing onto the front contact pads. Prototypes using two different cell technologies where manufactured achieving good results. The highest achieved fill factor (FF) is of 84.3% at 200X with a short circuit current (Isc) of 2.4mA and open circuit voltage (Voc) of 3.41V.
{"title":"Novel Interconnection Method for Micro-CPV Solar Cells","authors":"N. Jost, S. Askins, R. Dixon, Mathieu Ackermann, C. Domínguez, I. Antón","doi":"10.1109/PVSC43889.2021.9518837","DOIUrl":"https://doi.org/10.1109/PVSC43889.2021.9518837","url":null,"abstract":"Micro-concentrator photovoltaics (micro-CPV) consists of the reduction in size of the components of the conventional concentrator photovoltaic (CPV) technology, attaining equally high efficiencies and reducing material costs and manufacturing costs. In this publication we focus on the implementation of high throughput manufacturing methods for the interconnection of the solar cells. The goal is to enable large area interconnection of thousands of micro-solar cells with a low cost of 3€/m2. A proof-of-concept was achieved for interconnection via directly printing onto the front contact pads. Prototypes using two different cell technologies where manufactured achieving good results. The highest achieved fill factor (FF) is of 84.3% at 200X with a short circuit current (Isc) of 2.4mA and open circuit voltage (Voc) of 3.41V.","PeriodicalId":6788,"journal":{"name":"2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)","volume":"78 1","pages":"1166-1169"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83749695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-20DOI: 10.1109/PVSC43889.2021.9519122
Sagarika Kumar, Hebatalla Alhamadani, Shaikha Hassan, Ahmad Alheloo, H. Hanifi, Jim Joseph John, G. Mathiak, V. Alberts
In 2015, a comparative study of different PV modules was started at the Outdoor Test Field (OTF) in the DEWA R&D Center at the MBR Solar Park, Dubai. Five monofacial module types (multi- and monocrystalline silicon) were investigated. Various non-destructive techniques such as current-voltage analysis, electroluminescence and ultraviolet fluorescence imaging, microscopic visual inspection and quantum efficiency analysis were used. The findings show the presence of signature patterns to identify encapsulant degradation in ultraviolet fluorescence images, with different shapes and severities. Microscopic visual inspection was also used to examine glass abrasion and yellowing. Quantum efficiency measurements at short wavelengths showed UV-blocker penetration.
{"title":"Comparative Investigation and Analysis of Encapsulant Degradation and Glass Abrasion in Desert Exposed Photovoltaic Modules","authors":"Sagarika Kumar, Hebatalla Alhamadani, Shaikha Hassan, Ahmad Alheloo, H. Hanifi, Jim Joseph John, G. Mathiak, V. Alberts","doi":"10.1109/PVSC43889.2021.9519122","DOIUrl":"https://doi.org/10.1109/PVSC43889.2021.9519122","url":null,"abstract":"In 2015, a comparative study of different PV modules was started at the Outdoor Test Field (OTF) in the DEWA R&D Center at the MBR Solar Park, Dubai. Five monofacial module types (multi- and monocrystalline silicon) were investigated. Various non-destructive techniques such as current-voltage analysis, electroluminescence and ultraviolet fluorescence imaging, microscopic visual inspection and quantum efficiency analysis were used. The findings show the presence of signature patterns to identify encapsulant degradation in ultraviolet fluorescence images, with different shapes and severities. Microscopic visual inspection was also used to examine glass abrasion and yellowing. Quantum efficiency measurements at short wavelengths showed UV-blocker penetration.","PeriodicalId":6788,"journal":{"name":"2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)","volume":"1971 1","pages":"0793-0798"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82734091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-20DOI: 10.1109/PVSC43889.2021.9518817
A. A. Baloch, Omar Albadwawi, J. John, S. Bhattacharya, V. Alberts
An optics-based thermal model was developed to assess the spectral heating and thermal losses in industrial standard solar cells due to albedo. On average, bifacial silicon-heterojunction (B-SHJ) showed a power gain of 26.5% relative to monofacial heterojunction (M-SHJ) and 56.7% relative to monofacial BSF cells considered. However, heat content in B-SHJ was also found to be highest for all spectral albedos, i.e. thermal gain was 14% relative to monofacial-BSF and 4% more relative to M-SHJ. The results demonstrated that thermal losses in bifacial solar cells has a strong dependence on spectral albedo and should therefore be considered for accurate energy yield analysis.
{"title":"Study of thermal losses in bifacial solar cells due to various spectral albedoes","authors":"A. A. Baloch, Omar Albadwawi, J. John, S. Bhattacharya, V. Alberts","doi":"10.1109/PVSC43889.2021.9518817","DOIUrl":"https://doi.org/10.1109/PVSC43889.2021.9518817","url":null,"abstract":"An optics-based thermal model was developed to assess the spectral heating and thermal losses in industrial standard solar cells due to albedo. On average, bifacial silicon-heterojunction (B-SHJ) showed a power gain of 26.5% relative to monofacial heterojunction (M-SHJ) and 56.7% relative to monofacial BSF cells considered. However, heat content in B-SHJ was also found to be highest for all spectral albedos, i.e. thermal gain was 14% relative to monofacial-BSF and 4% more relative to M-SHJ. The results demonstrated that thermal losses in bifacial solar cells has a strong dependence on spectral albedo and should therefore be considered for accurate energy yield analysis.","PeriodicalId":6788,"journal":{"name":"2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)","volume":"142 1","pages":"0771-0773"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88972750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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