{"title":"Revolutionizing electronics with advanced interfacial heat management","authors":"Yen-Ju Wu","doi":"10.1038/s44287-024-00077-y","DOIUrl":null,"url":null,"abstract":"Efficient heat dissipation is crucial for electronics. Interfacial thermal resistance (ITR) poses considerable challenges that require innovative solutions. Machine learning approaches could enhance ITR predictions by analysing large datasets to guide the development of inorganic, amorphous and 2D materials for advanced thermal management in next-generation electronic devices.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 8","pages":"489-490"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44287-024-00077-y","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
Efficient heat dissipation is crucial for electronics. Interfacial thermal resistance (ITR) poses considerable challenges that require innovative solutions. Machine learning approaches could enhance ITR predictions by analysing large datasets to guide the development of inorganic, amorphous and 2D materials for advanced thermal management in next-generation electronic devices.
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