@article{ART003342000},
author={Cao Wenping and Tu Jiabin and Chen Wanxiaonan and Zhang Linsen and Lin Qianru and Sheng Jie},
title={Machine learning-optimized pre-carbonization process for sugar-based carbon fibers},
journal={Carbon Letters},
issn={1976-4251},
year={2026},
volume={36},
number={1},
pages={467-479},
doi={10.1007/s42823-025-00998-0}
TY - JOUR
AU - Cao Wenping
AU - Tu Jiabin
AU - Chen Wanxiaonan
AU - Zhang Linsen
AU - Lin Qianru
AU - Sheng Jie
TI - Machine learning-optimized pre-carbonization process for sugar-based carbon fibers
JO - Carbon Letters
PY - 2026
VL - 36
IS - 1
PB - Korean Carbon Society
SP - 467
EP - 479
SN - 1976-4251
AB - Sugar-derived carbon fibers (SBCFs) emerge as a promising next-generation sustainable material due to their biomass origin, cost-effectiveness, and superior strength-to-weight ratio. However, industrial adoption remains hindered by inefficient optimization of complex pre-carbonization processes. Here, we present a machine learning (ML)-driven framework to address this challenge, integrating experimental data to establish quantitative correlations between pre-carbonization parameters (temperature, dwell time) and mechanical performance. Gradient Boosted Decision Trees (GBDT) achieved superior predictive accuracy (R2 = 0.857 for tensile strength), enabling efficient identification of optimal conditions: 220 °C pre-carbonization temperature with 100 min dwell time. Experimental validation confirmed a 5.60% tensile strength enhancement over baseline protocols. Optimized protocols yield fibers with 874 MPa tensile strength and 76 GPa modulus, with an average diameter of 28 μm. This machine learning-driven methodology not only advances SBCF manufacturing but also establishes a generalizable paradigm for accelerating functional material development.
KW - Sugar-based carbon fibers Machine learning Pre-carbonization Optimization design
DO - 10.1007/s42823-025-00998-0
ER -
Cao Wenping, Tu Jiabin, Chen Wanxiaonan, Zhang Linsen, Lin Qianru and Sheng Jie. (2026). Machine learning-optimized pre-carbonization process for sugar-based carbon fibers. Carbon Letters, 36(1), 467-479.
Cao Wenping, Tu Jiabin, Chen Wanxiaonan, Zhang Linsen, Lin Qianru and Sheng Jie. 2026, "Machine learning-optimized pre-carbonization process for sugar-based carbon fibers", Carbon Letters, vol.36, no.1 pp.467-479. Available from: doi:10.1007/s42823-025-00998-0
Cao Wenping, Tu Jiabin, Chen Wanxiaonan, Zhang Linsen, Lin Qianru, Sheng Jie "Machine learning-optimized pre-carbonization process for sugar-based carbon fibers" Carbon Letters 36.1 pp.467-479 (2026) : 467.
Cao Wenping, Tu Jiabin, Chen Wanxiaonan, Zhang Linsen, Lin Qianru, Sheng Jie. Machine learning-optimized pre-carbonization process for sugar-based carbon fibers. 2026; 36(1), 467-479. Available from: doi:10.1007/s42823-025-00998-0
Cao Wenping, Tu Jiabin, Chen Wanxiaonan, Zhang Linsen, Lin Qianru and Sheng Jie. "Machine learning-optimized pre-carbonization process for sugar-based carbon fibers" Carbon Letters 36, no.1 (2026) : 467-479.doi: 10.1007/s42823-025-00998-0
Cao Wenping; Tu Jiabin; Chen Wanxiaonan; Zhang Linsen; Lin Qianru; Sheng Jie. Machine learning-optimized pre-carbonization process for sugar-based carbon fibers. Carbon Letters, 36(1), 467-479. doi: 10.1007/s42823-025-00998-0
Cao Wenping; Tu Jiabin; Chen Wanxiaonan; Zhang Linsen; Lin Qianru; Sheng Jie. Machine learning-optimized pre-carbonization process for sugar-based carbon fibers. Carbon Letters. 2026; 36(1) 467-479. doi: 10.1007/s42823-025-00998-0
Cao Wenping, Tu Jiabin, Chen Wanxiaonan, Zhang Linsen, Lin Qianru, Sheng Jie. Machine learning-optimized pre-carbonization process for sugar-based carbon fibers. 2026; 36(1), 467-479. Available from: doi:10.1007/s42823-025-00998-0
Cao Wenping, Tu Jiabin, Chen Wanxiaonan, Zhang Linsen, Lin Qianru and Sheng Jie. "Machine learning-optimized pre-carbonization process for sugar-based carbon fibers" Carbon Letters 36, no.1 (2026) : 467-479.doi: 10.1007/s42823-025-00998-0
