Citation: Qingyu Yang, Yuanhai Yu, Yanliu Wu, Ting Yang, Le Zhong, Wenhong Ruan, Jie Li. Digital Intelligence-Empowered Exploration of Copolymerized Polyacrylonitrile Synthesis and Application Experiments for Carbon Fiber Precursors[J]. University Chemistry, ;2026, 41(1): 41-56. doi: 10.12461/PKU.DXHX202506010 shu

Digital Intelligence-Empowered Exploration of Copolymerized Polyacrylonitrile Synthesis and Application Experiments for Carbon Fiber Precursors

School of Chemistry, Sun Yat-Sen University, Guangzhou 510006, China

Corresponding author: Wenhong Ruan, Jie Li,
Received Date: 3 June 2025
Revised Date: 9 September 2025

As a critical material in national defense, aerospace, and rail transportation sectors, carbon fiber has been included in China’s strategic development plan. The structural composition of its precursors, particularly copolymerized polyacrylonitrile, serves as the key factor of carbon fiber’s structure and performance. However, China currently faces technological bottlenecks in synthesizing and applying carbon fiber copolymer precursors, necessitating the cultivation of interdisciplinary talents with both professional knowledge and innovative capabilities through undergraduate teaching experiments. Presently, polymer chemistry laboratory courses predominantly focus on homopolymer radical polymerization experiments using single-variable controlled, non-exploratory approaches. The incorporation of copolymer synthesis experiments—which hold significant practical applications—into traditional curricula remains challenging due to time constraints, complex monomer selection and ratio determination, and limited instrument availability. The rapid advancement of digital technologies, particularly artificial intelligence (AI), offers promising solutions. This study designs a digital experimental teaching program for copolymer synthesis and application, leveraging open-source databases to train neural networks. Through AI-assisted predictions of various synthesis strategies, students can optimize parameters on a virtual platform to simulate the complete synthesis process and performance testing of polyacrylonitrile-based carbon fibers. These virtual experiments then guide physical laboratory investigations of carbon fiber precursor synthesis. The experimental data generated can be uploaded to the platform for fine-tuning pre-trained models, thereby progressively enhancing the AI’s predictive accuracy. Ultimately, by integrating with relevant virtual simulation experiments, this approach establishes a comprehensive modular experimental system encompassing the entire “synthesis-structure-property-application” workflow of carbon fiber precursors, providing students with a systematic, exploratory, and innovative digital integrated experiment that significantly improves talent development quality.
- Carbon fiber precursor,
- Polyacrylonitrile copolymer,
- Free radical polymerization,
- Artificial intelligence,
- Digital comprehensive experiment
1. [1]
2. [2]
  Brown, K. R.; Harrell, T. M.; Skrzypczak, L.; Scherschel, A.; Felix Wu, H.; Li, X. Carbon 2022, 196, 422.
3. [3]
  Hiremath, N.; Young, S.; Ghossein, H.; Penumadu, D.; Vaidya, U.; Theodore, M. Compos. Part B: Eng. 2020, 198, 108156.
4. [4]
  ]. https://www.gov.cn/xinwen/2021-03/13/content_5592681.htm
5. [5]
6. [6]
7. [7]
  Nguyen, N. L. T.; Creighton, C.; Nunna, S.; Maghe, M.; Groetsch, T.; Varley, R. J. Polym. Degrad. Stab. 2024, 227, 110835.
8. [8]
9. [9]
  Goswami, L.; Deka, M. K.; Roy, M. Adv. Eng. Mater. 2023, 25 (13), 2300104.
10. [10]
11. [11]
  Yang, J.; Lee, J.; Kwon, H.; Sohn, E.; Chang, H.; Jang, S. Macromol. Rapid Commun. 2024, 45 (15), 2400161.
12. [12]
  Queen, O.; McCarver, G.; Thatigotla, S.; Abolins, B.; Brown, C.; Maroulas, V.; Vogiatzis, K. npj Comput. Mater. 2023, 9 (1).
13. [13]
  Souza, M.; Göçmen, T; Cutululis, N. J. Phys.: Conf. Ser. 2024, 2767 (8), 082018.
14. [14]
  Lee, J.; Choi, J.; Lee, D. J.; Lee, S.; Chae, H. G. Carbon 2022, 191, 515.
15. [15]
  Chun, W. Y. J. Comput. Chem. 2010, 32 (7), 1466–1474.
16. [16]
  Zeng, J.; Zhao, G.; Liu, J.; Xiang, Y.; Guo, S. Diam. Relat. Mater. 2024, 141, 110588.
17. [17]
18. [18]
  Yao, S.; Li, C.; Jackson, M.; Strachan, A. Polymer 2023, 277, 125969.
19. [19]
  Nataraj, S. K.; Yang, K. S.; Aminabhavi, T. M. Prog. Polym. Sci. 2012, 37 (3), 487.
1. [1]
  Zijian Zhao , Yanxin Shi , Shicheng Li , Wenhong Ruan , Fang Zhu , Jijun Jiang . A New Exploration of the Preparation of Polyacrylic Acid by Free Radical Polymerization Based on the Concept of Green Chemistry. University Chemistry, 2024, 39(5): 315-324. doi: 10.3866/PKU.DXHX202311094
2. [2]
  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
3. [3]
  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
4. [4]
  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
5. [5]
  Yuejiao Wang , Quanxing Mao , Junshuo Cui , Xiaogeng Feng , Xiaohong Chang , Zhenning Lou , Ying Xiong . 颜色识别式人工智能融入混合碱滴定实验的应用. University Chemistry, 2026, 41(1): 107-113. doi: 10.12461/PKU.DXHX202506020
6. [6]
  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
7. [7]
  Ningyuan Chen , Qingyi Zeng , Zhiqiang Kuang , Peicong Tian , Dazhi Yan , Weiliang Jin , Deming Kong . Digital-Assisted Experimental Study on Dye Adsorption by Porous Materials. University Chemistry, 2026, 41(1): 321-331. doi: 10.12461/PKU.DXHX202504072
8. [8]
  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
9. [9]
  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
10. [10]
  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
11. [11]
  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
12. [12]
  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
13. [13]
  Huixin Dong , Zhenlei Zhou , Wenxin Zou , Juan Jin , Xiguang Liu , Yuzhong Niu , Lili Zhu , Hua Jiang . Exploration and Practice of Ideological and Political Education in Inorganic Chemistry Courses with the Assistance of Artificial Intelligence. University Chemistry, 2026, 41(3): 254-261. doi: 10.12461/PKU.DXHX202505003
14. [14]
  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
15. [15]
  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
16. [16]
  Tangsheng Guan , Ruizhi Yang , Pingyue Geng , Yuhong Lin , Shui Hu , Xiaojuan Chen , Houjin Li , Yong Shen . Innovative Digital Experiments for Glutathione-Targeted Anticancer Drugs in Redox Systems. University Chemistry, 2026, 41(1): 144-158. doi: 10.12461/PKU.DXHX202505028
17. [17]
  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
18. [18]
  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
19. [19]
  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
20. [20]
  Jizhu Zhou , Xiao Wei , Kaixue Wang . Deep Integration of Artificial Intelligence and Chemical Research: Applying the Fourth Paradigm and Evolving the Role of Chemists. University Chemistry, 2025, 40(12): 119-125. doi: 10.12461/PKU.DXHX202509003

Get Citation

PDF

XML

Metrics

PDF Downloads(1)
Abstract views(484)
HTML views(90)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142