人工智能在液晶应用中的应用:综述

IF 1.6 4区 工程技术 Q3 ENGINEERING, CHEMICAL
Sarah Chattha, Philip K. Chan, Simant R. Upreti
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引用次数: 0

摘要

人工智能(AI)的最新进展极大地影响了包括液晶在内的科学发现和分析。本文回顾了人工智能在预测液晶特性和改进其传感应用方面的应用。液晶通常用作生物医学检测和诊断以及重金属离子和气体检测中的传感器。这些应用中使用的传统分析方法往往主观、昂贵且耗时。为了克服这些挑战,最近人们利用卷积神经网络(CNN)和支持向量机(SVM)等人工智能方法来预测液晶特性,从而提高传感应用的性能。然而,要充分发挥人工智能方法的潜力,需要大量的数据,这也需要充分的道德监督。除实验外,还可采用利用第一原理和人工智能的建模方法来补充和提供数据。总之,综述表明,人工智能方法在液晶技术的进一步发展中大有可为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The use of artificial intelligence in liquid crystal applications: A review

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.

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来源期刊
Canadian Journal of Chemical Engineering
Canadian Journal of Chemical Engineering 工程技术-工程:化工
CiteScore
3.60
自引率
14.30%
发文量
448
审稿时长
3.2 months
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.
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