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Controlling the self-assembly of carbon nanotube (CNT) forests is important for tailoring their ensemble properties for specific applications. In this study, real-time electrical resistance measurements of in-situ CNT forest syntheses was established as a method to interrogate the evolution of CNT forest morphology. The method employs in-situ scanning electron microscopy (SEM) synthesis techniques to correlate observed morphological changes to electrical resistance. A finite-element simulation was used to simulate CNT forest synthesis and the evolution of electrical resistance in a configuration like that used experimentally. The simulation considers the contribution of CNT electrical resistance, CNT-CNT junction resistance, and CNT-electrode contact resistance. Simulation results indicate that an increased number of CNT-CNT junctions with time have a diminishing effect on increasing electrical conductance. Experimentally observed increases in electrical resistance are attributed to increasing CNT delamination from the electrical contacts.

 

The piezoresistance of carbon nanotube (CNT)-coated microfibers is examined using diametric compression. Diverse CNT forest morphologies were studied by changing the CNT length, diameter, and areal density via synthesis time and fiber surface treatment prior to CNT synthesis. Large-diameter (30–60 nm) and relatively low-density CNTs were synthesized on as-received glass fibers. Small-diameter (5–30 nm) and-high density CNTs were synthesized on glass fibers coated with 10 nm of alumina. The CNT length was controlled by adjusting synthesis time. Electromechanical compression was performed by measuring the electrical resistance in the axial direction during diametric compression. Gauge factors exceeding three were measured for small-diameter (<25 μm) coated fibers, corresponding to as much as 35% resistance change per micrometer of compression. The gauge factor for high-density, small-diameter CNT forests was generally greater than those for low-density, large-diameter forests. A finite element simulation shows that the piezoresistive response originates from both the contact resistance and intrinsic resistance of the forest itself. The change in contact and intrinsic resistance are balanced for relatively short CNT forests, while the response is dominated by CNT electrode contact resistance for taller CNT forests. These results are expected to guide the design of piezoresistive flow and tactile sensors.

 

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Area‐selective atomic layer deposition (AS‐ALD) techniques are an emerging class of bottom‐up nanofabrication techniques that selectively deposit patterned ALD films without the need for conventional top‐down lithography. To achieve this patterning, most reported AS‐ALD techniques use a chemical inhibitor layer to proactively block ALD surface reactions in selected areas. Herein, an AS‐ALD process is demonstrated that uses a focused electron beam (e‐beam) to dissociate ambient water vapor and “write” highly resolved hydroxylated patterns on the surface of highly oriented pyrolytic graphite (HOPG). The patterned hydroxylated regions then support subsequent ALD deposition. The e‐beam functionalization technique facilitates precise pattern placement through control of beam position, dwell time, and current. Spatial resolution of the technique exceeded 42 nm, with a surface selectivity of between 69.9% and 99.7%, depending on selection of background nucleation regions. This work provides a fabrication route for AS‐ALD on graphitic substrates suitable for fabrication of graphene‐based nanoelectronics. 

While the physical properties of carbon nanotubes (CNTs) are often superior to conventional engineering materials, their widespread adoption into many applications is limited by scaling the properties of individual CNTs to macroscale CNT assemblies known as CNT forests. The self-assembly mechanics of CNT forests that determine their morphology and ensemble properties remain poorly understood. Few experimental techniques exist to characterize and observe the growth and self-assembly processes in situ. Here we introduce the use of in-situ scanning electron microscope (SEM) synthesis based on chemical vapor deposition (CVD) processing. In this preliminary report, we share best practices for in-situ SEM CVD processing and initial CNT forest synthesis results. Image analysis techniques are developed to identify and track the movement of catalyst nanoparticles during synthesis conditions. Finally, a perspective is provided in which in-situ SEM observations represent one component of a larger system in which numerical simulation, machine learning, and digital control of experiments reduces the role of humans and human error in the exploration of CNT forest process-structure-property relationships. 

Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.