Optimizing the growth of vertically aligned carbon nanotubes by literature mining and high-throughput experiments
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摘要: 具有良好力学性能和高导热性的碳纳米管垂直阵列（VACNT）可用作热管理中的有效热界面材料。为了利用沿碳纳米管轴向的高导热性，需要优化碳纳米管垂直阵列的结晶度和高度。然而，碳纳米管垂直阵列的生长参数空间（如退火时间、催化剂种类、生长温度、载气、碳源等）复杂，结构特征之间相互影响，同时提高碳纳米管垂直阵列的高度和质量仍是一个巨大的挑战。与此同时，缺乏对参数调控方向的指导进一步增加了实验结果的不确定性，并限制了产物结构优化的效率。本研究开发了一种文献挖掘-机器学习-高通量制备策略，有效优化了碳纳米管垂直阵列的高度和质量。为了揭示碳纳米管垂直阵列结构与关键生长参数之间的潜在关系，采用随机森林回归算法对一组已发布的样本数据（864个样本）进行建模，并利用机器学习模型解释包 SHAP（SHapley Additive exPlanations）分析获得影响垂直阵列高度和结晶度的主要生长参数。经分析确定，高通量实验旨在调节4个关键参数：生长温度、生长时间、催化剂组分和碳源浓度。结果发现，经筛选的 Fe/Gd/Al2O3 催化剂能够生长出具有毫米级高度和更高结晶度的碳纳米管垂直阵列。结果表明，文献挖掘、高通量实验和基于数据的机器学习可以有效地处理碳纳米管生长等多参数过程，提高对结构的控制。Abstract: Vertically aligned carbon nanotube (VACNT) arrays with good mechanical properties and high thermal conductivity can be used as effective thermal interface materials in thermal management. In order to take advantage of the high thermal conductivity along the axis of nanotubes, the quality and height of the arrays need to be optimized. However, the immense synthesis parameter space for VACNT arrays and the interdependence of structural features make it challenging to improve both their height and quality. We have developed a literature mining approach combined with machine learning and high-throughput design to efficiently optimize the height and quality of the arrays. To reveal the underlying relationship between VACNT structures and their key growth parameters, we used random forest regression (RFR) and SHapley Additive exPlanation (SHAP) methods to model a set of published sample data (864 samples). High-throughput experiments were designed to change 4 key parameters: growth temperature, growth time, catalyst composition, and concentration of the carbon source. It was found that a screened Fe/Gd/Al2O3 catalyst was able to grow VACNT arrays with millimeter-scale height and improved quality. Our results demonstrate that this approach can effectively deal with multi-parameter processes such as nanotube growth and improve control over their structures.
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Figure 1. Workflow for optimizing the height and quality of VACNT arrays by 4 main steps
Figure 2. SHAP summary plots of the effects of growth parameters in the RFR models for (a) height, and (b) IG/ID values, in descending order. All of the variables in the database constitute each point in the image, the color of the point indicates the value of the input variable, and the horizontal location shows whether the effect of that value is associated with a higher or a lower prediction. t(G.) and t(A.) denote the growth time and annealing time, respectively
Figure 3. Fe-Cr-Gd ternary diagram associated (a) height and (b) IG/ID values of the VACNT arrays. Plots of (c) height and (d) IG/ID values of the VACNT arrays as a function of Gd and Cr content (weight %). The arrows are just to guide the eyes
Figure 4. Height of VACNT arrays grown from Fe and Fe-Gd catalyst as a function of the IG/ID values
Figure 5. Characterizations of VACNT arrays before and after optimization. (a) Comparison of Raman spectra taken from the top of the VACNT arrays using a wavelength of 532 nm laser; (b) Cross-sectional SEM images of the VACNT arrays; TEM images and the inserted diameter distribution of VACNT arrays grown from (c) 1.8 nm Fe catalyst and (d) 1.5 nm Fe-0.2 nm Gd catalyst
Table 1. Regression model validation and performance evaluation
Output
parametersRegression
modelPerformance evaluation metrics RMSE MAE Coefficient of
determination
(R2)Height LR 0.57 0.33 0.12 RFR 0.15 0.09 0.87 SVR 0.44 0.19 0.52 IG/ID LR 2.79 2.44 0.55 RFR 2.22 1.64 0.73 SVR 2.58 1.97 0.68 
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