{"title":"人工智能在液晶应用中的应用:综述","authors":"Sarah Chattha, Philip K. Chan, Simant R. Upreti","doi":"10.1002/cjce.25452","DOIUrl":null,"url":null,"abstract":"<p>Recent advancements in artificial intelligence (AI) have significantly influenced scientific discovery and analysis, including liquid crystals. This paper reviews the use of AI in predicting the properties of liquid crystals and improving their sensing applications. Typically, liquid crystals are utilized as sensors in biomedical detection and diagnostics, and in the detection of heavy metal ions and gases. Traditional methods of analysis used in these applications are often subjective, expensive, and time-consuming. To surmount these challenges, AI methods such as convolutional neural networks (CNN) and support vector machines (SVM) have been recently utilized to predict liquid crystal properties and improve the resulting performance of the sensing applications. Large amounts of data are, however, required to fully realize the potential of AI methods, which would also need adequate ethical oversight. In addition to experiments, modelling approaches utilizing first principles as well as AI may be employed to supplement and furnish the data. In summary, the review indicates that AI methods hold great promise in the further development of the liquid crystal technology.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 3","pages":"1060-1082"},"PeriodicalIF":1.6000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjce.25452","citationCount":"0","resultStr":"{\"title\":\"The use of artificial intelligence in liquid crystal applications: A review\",\"authors\":\"Sarah Chattha, Philip K. Chan, Simant R. Upreti\",\"doi\":\"10.1002/cjce.25452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Recent advancements in artificial intelligence (AI) have significantly influenced scientific discovery and analysis, including liquid crystals. This paper reviews the use of AI in predicting the properties of liquid crystals and improving their sensing applications. Typically, liquid crystals are utilized as sensors in biomedical detection and diagnostics, and in the detection of heavy metal ions and gases. Traditional methods of analysis used in these applications are often subjective, expensive, and time-consuming. To surmount these challenges, AI methods such as convolutional neural networks (CNN) and support vector machines (SVM) have been recently utilized to predict liquid crystal properties and improve the resulting performance of the sensing applications. Large amounts of data are, however, required to fully realize the potential of AI methods, which would also need adequate ethical oversight. In addition to experiments, modelling approaches utilizing first principles as well as AI may be employed to supplement and furnish the data. In summary, the review indicates that AI methods hold great promise in the further development of the liquid crystal technology.</p>\",\"PeriodicalId\":9400,\"journal\":{\"name\":\"Canadian Journal of Chemical Engineering\",\"volume\":\"103 3\",\"pages\":\"1060-1082\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjce.25452\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Canadian Journal of Chemical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cjce.25452\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Journal of Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cjce.25452","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
The use of artificial intelligence in liquid crystal applications: A review
Recent advancements in artificial intelligence (AI) have significantly influenced scientific discovery and analysis, including liquid crystals. This paper reviews the use of AI in predicting the properties of liquid crystals and improving their sensing applications. Typically, liquid crystals are utilized as sensors in biomedical detection and diagnostics, and in the detection of heavy metal ions and gases. Traditional methods of analysis used in these applications are often subjective, expensive, and time-consuming. To surmount these challenges, AI methods such as convolutional neural networks (CNN) and support vector machines (SVM) have been recently utilized to predict liquid crystal properties and improve the resulting performance of the sensing applications. Large amounts of data are, however, required to fully realize the potential of AI methods, which would also need adequate ethical oversight. In addition to experiments, modelling approaches utilizing first principles as well as AI may be employed to supplement and furnish the data. In summary, the review indicates that AI methods hold great promise in the further development of the liquid crystal technology.
期刊介绍:
The Canadian Journal of Chemical Engineering (CJChE) publishes original research articles, new theoretical interpretation or experimental findings and critical reviews in the science or industrial practice of chemical and biochemical processes. Preference is given to papers having a clearly indicated scope and applicability in any of the following areas: Fluid mechanics, heat and mass transfer, multiphase flows, separations processes, thermodynamics, process systems engineering, reactors and reaction kinetics, catalysis, interfacial phenomena, electrochemical phenomena, bioengineering, minerals processing and natural products and environmental and energy engineering. Papers that merely describe or present a conventional or routine analysis of existing processes will not be considered.