{"title":"基于物理引导的贝叶斯主动学习加速发现稳定锌水电池的溶剂化结构工程(Small 23/2025)","authors":"Minsu Kim, Minji Lee, Inyoung Choi, Jihye Oh, Sanga Paik, Areum Han, Sinae Lee, Hyerim Hwang, Jonggeol Na, Kwan Woo Nam","doi":"10.1002/smll.202570176","DOIUrl":null,"url":null,"abstract":"<p><b>Aqueous Zinc Batteries</b></p><p>Aqueous zinc batteries, despite their advantages in safety and performance, suffer from water-based side reactions like hydrogen evolution and corrosion. In article number 2411632, Jonggeol Na, Kwan Woo Nam, and co-workers propose a novel electrolyte that enhances stability by suppressing these side reactions and maximizing performance through solvation structure optimization. In particular, it is noteworthy that only 26 experiments were conducted based on physics-guided Bayesian active learning.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":228,"journal":{"name":"Small","volume":"21 23","pages":""},"PeriodicalIF":13.0000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smll.202570176","citationCount":"0","resultStr":"{\"title\":\"Accelerated Discovery of Solvation Structure Engineering for Stable Aqueous Rechargeable Zinc Batteries via Physics-Guided Bayesian Active Learning (Small 23/2025)\",\"authors\":\"Minsu Kim, Minji Lee, Inyoung Choi, Jihye Oh, Sanga Paik, Areum Han, Sinae Lee, Hyerim Hwang, Jonggeol Na, Kwan Woo Nam\",\"doi\":\"10.1002/smll.202570176\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><b>Aqueous Zinc Batteries</b></p><p>Aqueous zinc batteries, despite their advantages in safety and performance, suffer from water-based side reactions like hydrogen evolution and corrosion. In article number 2411632, Jonggeol Na, Kwan Woo Nam, and co-workers propose a novel electrolyte that enhances stability by suppressing these side reactions and maximizing performance through solvation structure optimization. In particular, it is noteworthy that only 26 experiments were conducted based on physics-guided Bayesian active learning.\\n\\n <figure>\\n <div><picture>\\n <source></source></picture><p></p>\\n </div>\\n </figure></p>\",\"PeriodicalId\":228,\"journal\":{\"name\":\"Small\",\"volume\":\"21 23\",\"pages\":\"\"},\"PeriodicalIF\":13.0000,\"publicationDate\":\"2025-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smll.202570176\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Small\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/smll.202570176\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Small","FirstCategoryId":"88","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/smll.202570176","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Accelerated Discovery of Solvation Structure Engineering for Stable Aqueous Rechargeable Zinc Batteries via Physics-Guided Bayesian Active Learning (Small 23/2025)
Aqueous Zinc Batteries
Aqueous zinc batteries, despite their advantages in safety and performance, suffer from water-based side reactions like hydrogen evolution and corrosion. In article number 2411632, Jonggeol Na, Kwan Woo Nam, and co-workers propose a novel electrolyte that enhances stability by suppressing these side reactions and maximizing performance through solvation structure optimization. In particular, it is noteworthy that only 26 experiments were conducted based on physics-guided Bayesian active learning.
期刊介绍:
Small serves as an exceptional platform for both experimental and theoretical studies in fundamental and applied interdisciplinary research at the nano- and microscale. The journal offers a compelling mix of peer-reviewed Research Articles, Reviews, Perspectives, and Comments.
With a remarkable 2022 Journal Impact Factor of 13.3 (Journal Citation Reports from Clarivate Analytics, 2023), Small remains among the top multidisciplinary journals, covering a wide range of topics at the interface of materials science, chemistry, physics, engineering, medicine, and biology.
Small's readership includes biochemists, biologists, biomedical scientists, chemists, engineers, information technologists, materials scientists, physicists, and theoreticians alike.