数智赋能碳纤维前体共聚聚丙烯腈合成及应用实验的探究

引用本文: 杨晴羽, 于渊海, 吴艳柳, 杨婷, 钟乐, 阮文红, 李洁. 数智赋能碳纤维前体共聚聚丙烯腈合成及应用实验的探究[J]. 大学化学, 2026, 41(1): 41-56. doi: 10.12461/PKU.DXHX202506010 shu

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

数智赋能碳纤维前体共聚聚丙烯腈合成及应用实验的探究

中山大学化学学院, 广州 510006

通讯作者: 阮文红，E-mail:cesrwh@mail.sysu.edu.cn; 李洁，E-mail:lijie72@mail.sysu.edu.cn

基金项目:
广东省高等教育教学改革项目；广东省研究生教育创新计划项目；中山大学本科教学质量工程项目

摘要: 碳纤维作为国防、航空航天、轨道交通等领域的关键材料，已被纳入国家战略发展规划。其前体(如共聚聚丙烯腈)的结构组成是影响碳纤维结构与性能的核心因素。然而，我国在碳纤维共聚前体的合成及应用方面仍面临技术瓶颈，亟需通过本科相关教学实验培养具备专业知识与创新能力的复合型人才。目前，本科高分子化学实验课程多以均聚物自由基聚合实验为主，并采用单变量控制的非探究性模式进行。由于实验时长有限、单体种类与比例选择复杂以及仪器条件不足等因素，具有重要应用的共聚物的合成实验难以纳入传统的实验教学中。随着人工智能等数字化技术的快速发展，这一困境有望被突破。本研究设计了一套基于共聚物合成及应用的数字化实验教学方案，通过利用开源数据库训练神经网络，借助人工智能程序预测不同合成策略的结果，学生可在虚拟实验平台上优化相关参数，模拟聚丙烯腈基碳纤维的全流程合成及性能测试，进而指导开展线下探究性碳纤维共聚前体的合成实验，产生的实验数据可上传至平台，用于微调预训练模型，从而逐步提高人工智能模型的预测精度。最终，通过与相关虚拟仿真实验的链接，构建了碳纤维前体“合成–结构–性能–应用”的全流程模块化实验体系，为学生提供了一个系统性、探究性及创新性的数字化综合实验，有效提升了人才培养的质量。

关键词:

English

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: 03 June 2025
Revised Date: 09 September 2025
Available Online: 09 October 2025

Abstract: 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.

Key words:

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数智赋能碳纤维前体共聚聚丙烯腈合成及应用实验的探究

通讯作者: 阮文红，E-mail:cesrwh@mail.sysu.edu.cn; 李洁，E-mail:lijie72@mail.sysu.edu.cn

English

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

Corresponding author: Wenhong Ruan, ; Jie Li,

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

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通讯作者: 陈斌, bchen63@163.com

中国化学会秘书处

数智赋能碳纤维前体共聚聚丙烯腈合成及应用实验的探究

通讯作者: 阮文红，E-mail:cesrwh@mail.sysu.edu.cn; 李洁，E-mail:lijie72@mail.sysu.edu.cn

English

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

Corresponding author: Wenhong Ruan, ; Jie Li,

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

CCS Chemistry：颜徐州、鲍哲南： 你的皮肤可能是一片SPMs

CCS Chemistry：工作高效不疲劳，只须让催化剂动起来

CCS Chemistry：静电组装导向聚合- 聚电解质纳米凝胶量化制备新策略

CCS Chemistry：金黄色葡萄球菌醛基脱氢酶是如何识别特异性底物的？

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通讯作者: 陈斌, bchen63@163.com

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