Robofurnace: A semi-automated laboratory chemical vapor deposition system for high-throughput nanomaterial synthesis and process discovery

Abstract

Laboratory research and development on new materials, such as nanostructured thin films, often utilizes manual equipment such as tube furnaces due to its relatively low cost and ease of setup. However, these systems can be prone to inconsistent outcomes due to variations in standard operating procedures and limitations in performance such as heating and cooling rates restrict the parameter space that can be explored. Perhaps more importantly, maximization of research throughput and the successful and efficient translation of materials processing knowledge to production-scale systems, relies on the attainment of consistent outcomes. In response to this need, we present a semi-automated lab-scale chemical vapor deposition (CVD) furnace system, called "Robofurnace." Robofurnace is an automated CVD system built around a standard tube furnace, which automates sample insertion and removal and uses motion of the furnace to achieve rapid heating and cooling. The system has a 10-sample magazine and motorized transfer arm, which isolates the samples from the lab atmosphere and enables highly repeatable placement of the sample within the tube. The system is designed to enable continuous operation of the CVD reactor, with asynchronous loading/unloading of samples. To demonstrate its performance, Robofurnace is used to develop a rapid CVD recipe for carbon nanotube (CNT) forest growth, achieving a 10-fold improvement in CNT forest mass density compared to a benchmark recipe using a manual tube furnace. In the long run, multiple systems like Robofurnace may be linked to share data among laboratories by methods such as Twitter. Our hope is Robofurnace and like automation will enable machine learning to optimize and discover relationships in complex material synthesis processes.

Anisotropic Carbon Nanotube Structures with High Aspect Ratio Nanopores for Li-Ion Battery Anodes

“… 42 , 63 , 64 As shown in Figure 4 g, the capacity retention over 10 cycles (after the two formation cycles shown in Figure 4 e) remains stable at ∼650 mA/hg. These experiments provide a first indication that the proposed porous CNT structures might be a good scaffold for LIB electrodes, and we anticipate that because of the tuneability of the pore size and surface chemistry, they may also be attractive for a number of other applications such as filtration 45 , 65 , 66 and catalysts. 6 , 7 The implementation of this system into real-life batteries, however, will require much more investigation and optimization.…”

Autonomy in materials research: a case study in carbon nanotube growth

“…We found that the intrinsic variability in the system, which we termed the noise floor, over 20-30 experiments ranged from 20 to 30%, which is similar to the value found by Oliver et al for their automated CNT growth furnace. 14 By the end of the experimental campaign the variability in the experimental values matched the noise floor. Thus we conclude that ARES was able to target growth rates to the degree of variability intrinsic to our system.…”

“…To address this, automation has been applied to research processes via high-throughput and combinatorial techniques in the fields of life sciences [3][4][5][6][7] and materials research. [8][9][10][11][12][13][14][15][16] In addition to automation, computation is being exploited to speed research. The Materials Genome Initiative 17 and related Integrated Computational Materials Science and Engineering efforts 18 have highlighted the need for computational approaches to more effectively make use of the explosion in data generation.…”

Leveraging in situ resources for lunar base construction

“…Foundries must be automated and regulated. For example, a CVD (chemical vapour deposition) tube furnace system -Robofurnace -has demonstrated semi-autonomous thin film manufacture (Oliver et al 2013). In general, the key to automation is the sensor suite to provide observability with closed loop controlthermochemical processing will require a distributed set of temperature control, pressure control and gas detection systems.…”