Advanced Functional Materials·· 1 天前AI 评分38
层流边界层传质引导的机器学习实现水平碳纳米管阵列密度精准调控
Laminar Boundary Layer Mass Transport‐Guided Machine Learning for Precision Density Regulation of Horizontal Carbon Nanotube Arrays
AI 导读
一种融合边界层流体动力学的机器学习工作流将高维生长参数压缩为传质系数Km与停留时间t两个物理描述符，使水平排列碳纳米管（HACNT）阵列密度预测的R²从0.68提升至0.84。4200次虚拟筛选绘制出全局动力学相图，显示高密度HACNT生长窗口极窄，需碳前驱体供应充足且适中并匹配适当的t。验证实验确认了模型可靠性并实现120 tubes/µm阵列的定向生长。
图表、补充材料与最终表述以原文为准。阅读原文
来源：Advanced Functional Materials · doi.org
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