Citation:
WANG Yu, FANG Qun. Application of Artificial Intelligence in Microfluidic Systems[J]. Chinese Journal of Analytical Chemistry,
;2020, 48(4): 439-448.
doi:
10.19756/j.issn.0253-3820.191682
-
Microfluidic systems are widely applied in many fields including chemistry, biology, medicine, and pharmacy, because of their precise control ability to microfluids. In recent years, artificial intelligence technology has achieved leap-forward development with great advantages in dealing with the analysis and mining of massive data. The application of artificial intelligence technology in microfluidic systems has shown great potential in many fields such as biological research, medical diagnosis, and drug discovery, and so on. This paper reviews several typical artificial intelligence models and their applications in microfluidic systems. It focuses on the progress of artificial intelligence in target detection, correlation prediction and result classification of microfluidic systems, and forecasts the future development trend based on its application status.
-
Keywords:
- Microfluidics,
- Artificial intelligence,
- Data mining,
- Big data,
- Review
-
-
-
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
-
[8]
-
[9]
-
[10]
-
[11]
-
[12]
-
[13]
-
[14]
-
[15]
-
[16]
-
[17]
-
[18]
-
[19]
-
[20]
-
[21]
-
[22]
-
[23]
-
[24]
-
[25]
-
[26]
-
[27]
-
[28]
-
[29]
-
[30]
-
[31]
-
[32]
-
[33]
-
[34]
-
[35]
-
[36]
-
[37]
-
[38]
-
[39]
-
[40]
-
[41]
-
[42]
-
[43]
-
[1]
-
-
-
[1]
Xiaojun Liu , Lang Qin , Yanlei Yu . Dynamic Manipulation of Photonic Bandgaps in Cholesteric Liquid Crystal Microdroplets for Applications. Acta Physico-Chimica Sinica, 2024, 40(5): 2305018-0. doi: 10.3866/PKU.WHXB202305018
-
[2]
Meirong Cui , Mo Xie , Jie Chao . Design and Reflections on the Integration of Artificial Intelligence in Physical Chemistry Laboratory Courses. University Chemistry, 2025, 40(5): 291-300. doi: 10.12461/PKU.DXHX202412015
-
[3]
Cheng-an Tao , Jian Huang , Yujiao Li . Exploring the Application of Artificial Intelligence in University Chemistry Laboratory Instruction. University Chemistry, 2025, 40(9): 5-10. doi: 10.12461/PKU.DXHX202408132
-
[4]
Lei Qin , Kai Guo . Application of Generative Artificial Intelligence in the Simulation of Acid-Base Titration Images. University Chemistry, 2025, 40(9): 11-18. doi: 10.12461/PKU.DXHX202408123
-
[5]
Xiao Ma , Junjie Wang , Xin Chen , Jingcheng Li , Lihong Zhao , Xueping Sun , Shaojuan Cheng , Fang Wang . Exploring Innovative Approaches to Chemistry Instructional Organization Driven by Artificial Intelligence. University Chemistry, 2025, 40(9): 99-106. doi: 10.12461/PKU.DXHX202410085
-
[6]
Run Yang , Huajie Pang , Huiping Zang , Ruizhong Zhang , Zhicheng Zhang , Xiyan Li , Libing Zhang . Artificial Intelligence-Enabled DNA Computing: Exploring New Frontiers in Bioinformatics. University Chemistry, 2025, 40(9): 107-117. doi: 10.12461/PKU.DXHX202412135
-
[7]
Yan Zhang , Limin Zhou , Xiaoyan Cao , Mutai Bao . Exploring the Application of Artificial Intelligence in Marine-Themed Integrated Physical Chemistry Experiments. University Chemistry, 2025, 40(9): 118-125. doi: 10.12461/PKU.DXHX202503062
-
[8]
Wuyi Feng , Di Zhao . Significance and Measures of Integrating Artificial Intelligence Technology into College Chemistry Teaching. University Chemistry, 2025, 40(9): 156-163. doi: 10.12461/PKU.DXHX202502107
-
[9]
Lingli Wu , Shengbin Lei . Generative AI-Driven Innovative Chemistry Teaching: Current Status and Future Prospects. University Chemistry, 2025, 40(9): 206-219. doi: 10.12461/PKU.DXHX202503069
-
[10]
Weigang Zhu , Jianfeng Wang , Qiang Qi , Jing Li , Zhicheng Zhang , Xi Yu . Curriculum Development for Cheminformatics and AI-Driven Chemistry Theory toward an Intelligent Era. University Chemistry, 2025, 40(9): 34-42. doi: 10.12461/PKU.DXHX202412002
-
[11]
Haoran Zhang , Yaxin Jin , Peng Kang , Sheng Zhang . The Convergence and Innovative Application of Artificial Intelligence in Scientific Research: A Case Study of Electrocatalytic Carbon Dioxide Reduction in the Context of the Dual-Carbon Strategy. University Chemistry, 2025, 40(9): 148-155. doi: 10.12461/PKU.DXHX202412099
-
[12]
Ping Li , Chao Yin . Teaching Exploration and Practical Innovation of General Education Courses in the Context of Artificial Intelligence. University Chemistry, 2024, 39(10): 402-407. doi: 10.12461/PKU.DXHX202403075
-
[13]
Yifan Liu , Haonan Peng . AI-Assisted New Era in Chemistry: A Review of the Application and Development of Artificial Intelligence in Chemistry. University Chemistry, 2025, 40(7): 189-199. doi: 10.12461/PKU.DXHX202405182
-
[14]
Yu Fang . AI-Empowered Education: A Case Study of Self-Directed Learning with ChatGPT-4. University Chemistry, 2025, 40(9): 1-4. doi: 10.12461/PKU.DXHX202502013
-
[15]
Tianlong Zhang , Rongling Zhang , Hongsheng Tang , Yan Li , Hua Li . Exploration on the Integration Mode of Instrumental Analysis with Science and Education under the Background of Artificial Intelligence Era. University Chemistry, 2024, 39(8): 365-374. doi: 10.12461/PKU.DXHX202403014
-
[16]
Liangjun Chen , Yu Zhang , Zhicheng Zhang , Yongwu Peng . AI-Empowering Reform in University Chemistry Education: Practical Exploration of Cultivating Informationization and Intelligent Literacy. University Chemistry, 2025, 40(9): 220-227. doi: 10.12461/PKU.DXHX202503124
-
[17]
Jia Zhou . Design and Practice of a Comprehensive Computational Chemistry Experiment Based on High-Throughput Computation and Machine Learning. University Chemistry, 2025, 40(9): 69-75. doi: 10.12461/PKU.DXHX202411067
-
[18]
Ying Zhang , Fang Ge , Zhimin Luo . AI-Driven Biochemical Teaching Research: Predicting the Functional Effects of Gene Mutations. University Chemistry, 2025, 40(3): 277-284. doi: 10.12461/PKU.DXHX202412104
-
[19]
Haolin Zhan , Qiyuan Fang , Jiawei Liu , Xiaoqi Shi , Xinyu Chen , Yuqing Huang , Zhong Chen . Noise Reduction of Nuclear Magnetic Resonance Spectroscopy Using Lightweight Deep Neural Network. Acta Physico-Chimica Sinica, 2025, 41(2): 100017-0. doi: 10.3866/PKU.WHXB202310045
-
[20]
Xintian Xie , Sicong Ma , Yefei Li , Cheng Shang , Zhipan Liu . Application of Machine Learning Potential-based Theoretical Simulations in Undergraduate Teaching Laboratory Course Design. University Chemistry, 2025, 40(3): 140-147. doi: 10.12461/PKU.DXHX202405164
-
[1]
Metrics
- PDF Downloads(47)
- Abstract views(1083)
- HTML views(155)
Login In
DownLoad: