{"title":"Front Cover, Volume 4, Number 1, January 2025","authors":"Negar Danesh, Matin Torabinia, Hyejin Moon","doi":"10.1002/dro2.167","DOIUrl":null,"url":null,"abstract":"<p><b>Front Cover</b>: The cover image is based on the Research Article <i>Droplet menisci recognition by deep learning for digital microfluidics applications</i> by Danesh et al.</p><p>Cover description: This work showcases the use of a U-Net deep learning model to accurately identify droplet menisci in electrowetting-on-dielectric (EWOD) systems. By achieving high precision, even with complex or low-quality images, the model enhances droplet control and reveals critical insights into fluid properties, reaction kinetics, and dynamic behaviors, advancing the performance and reliability of EWOD microfluidic devices. (DOI: 10.1002/dro2.151)\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":100381,"journal":{"name":"Droplet","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/dro2.167","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Droplet","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/dro2.167","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Front Cover: The cover image is based on the Research Article Droplet menisci recognition by deep learning for digital microfluidics applications by Danesh et al.
Cover description: This work showcases the use of a U-Net deep learning model to accurately identify droplet menisci in electrowetting-on-dielectric (EWOD) systems. By achieving high precision, even with complex or low-quality images, the model enhances droplet control and reveals critical insights into fluid properties, reaction kinetics, and dynamic behaviors, advancing the performance and reliability of EWOD microfluidic devices. (DOI: 10.1002/dro2.151)
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