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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.