Air Force Research Laboratory, Materials and Manufacturing Directorate, RXAS , Wright-Patterson AFB, Ohio 45433, United States.
ACS Nano. 2014 Oct 28;8(10):10214-22. doi: 10.1021/nn503347a. Epub 2014 Oct 16.
Applications of carbon nanotubes continue to advance, with substantial progress in nanotube electronics, conductive wires, and transparent conductors to name a few. However, wider application remains impeded by a lack of control over production of nanotubes with the desired purity, perfection, chirality, and number of walls. This is partly due to the fact that growth experiments are time-consuming, taking about 1 day per run, thus making it challenging to adequately explore the many parameters involved in growth. We endeavored to speed up the research process by automating CVD growth experimentation. The adaptive rapid experimentation and in situ spectroscopy CVD system described in this contribution conducts over 100 experiments in a single day, with automated control and in situ Raman characterization. Linear regression modeling was used to map regions of selectivity toward single-wall and multiwall carbon nanotube growth in the complex parameter space of the water-assisted CVD synthesis. This development of the automated rapid serial experimentation is a significant progress toward an autonomous closed-loop learning system: a Robot Scientist.
碳纳米管的应用不断推进，在纳米管电子学、导电丝和透明导体等方面取得了重大进展。然而，由于缺乏对具有所需纯度、完美性、手性和层数的纳米管生产的控制，其更广泛的应用仍然受到阻碍。这在一定程度上是由于生长实验耗时较长，每个运行大约需要 1 天，因此难以充分探索生长过程中涉及的许多参数。我们努力通过自动化 CVD 生长实验来加速研究过程。本文介绍的自适应快速实验和原位光谱 CVD 系统在一天内进行了 100 多次实验，实现了自动化控制和原位拉曼特性分析。线性回归建模用于在水辅助 CVD 合成的复杂参数空间中映射单壁和多壁碳纳米管生长的选择性区域。这种自动化快速连续实验的发展是朝着自主闭环学习系统——机器人科学家迈出的重要一步。
