Kim Cheol Hwan, Kim Jae Hyuk, Jeong Sung-Yeob, Shin Bo Sung
Department of Cogno-Mechatronics, Pusan National University, Busan, 46241, Republic of Korea.
Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
Sci Rep. 2025 Aug 27;15(1):31627. doi: 10.1038/s41598-025-15945-2.
Wood-based substrates-known for their renewability, abundance, and surface functionalization potential-have recently gained attention as polymers for laser-induced graphene (LIG) synthesis because of their environmentally friendly attributes. These environment-friendly properties also make them pollution-free and easy to dispose of after use. However, the formation of LIG on wood substrates lacks robust theoretical support, and molecular dynamics (MD) simulations, which are a potential theoretical framework, are time-consuming and computationally intensive. Herein, we employed temperature-dependent MD simulations to explore LIG formation on wood-based materials, validating our findings through a comparative analysis with atomic-scale characterization results. To address the high computational requirements of MD simulations, machine learning (ML) models, including long short-term memory (LSTM) networks, support vector regression (SVR), and multilayer perceptrons (MLP), were implemented to extrapolate predictions beyond direct simulation conditions. Each model exhibited high data explanatory power (R values ≥ 0.9), and the computational time was significantly reduced compared to the MD simulations. ML-based predictions revealed a substantial correlation between the temperature and LIG formation extent, establishing an efficient framework for optimizing LIG synthesis from wood-based materials under various laser processing conditions. This framework has considerable potential for applications in energy storage devices, high-sensitivity sensors, and advanced catalytic materials.
木质基材因其可再生性、丰富性和表面功能化潜力而闻名，由于其环保特性，最近作为激光诱导石墨烯（LIG）合成的聚合物受到关注。这些环保特性还使其无污染且使用后易于处理。然而，在木质基材上形成LIG缺乏有力的理论支持，而分子动力学（MD）模拟作为一种潜在的理论框架，既耗时又计算量大。在此，我们采用温度相关的MD模拟来探索木质材料上LIG的形成，并通过与原子尺度表征结果的对比分析来验证我们的发现。为了满足MD模拟的高计算要求，实施了包括长短期记忆（LSTM）网络、支持向量回归（SVR）和多层感知器（MLP）在内的机器学习（ML）模型，以在直接模拟条件之外进行预测推断。每个模型都具有较高的数据解释力（R值≥0.9），并且与MD模拟相比，计算时间显著减少。基于ML的预测揭示了温度与LIG形成程度之间的显著相关性，建立了一个在各种激光加工条件下优化木质材料合成LIG的有效框架。该框架在储能装置、高灵敏度传感器和先进催化材料方面具有相当大的应用潜力。
