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Abstract 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. 

Electron microscopy images of carbon nanotube (CNT) forests are difficult to segment due to the long and thin nature of the CNTs; density of the CNT forests resulting in CNTs touching, crossing, and occluding each other; and low signal-to-noise ratio electron microscopy imagery. In addition, due to image complexity, it is not feasible to prepare training segmentation masks. In this paper, we propose CNTSegNet, a dual loss, orientation-guided, self-supervised, deep learning network for CNT forest segmentation in scanning electron microscopy (SEM) images. Our training labels consist of weak segmentation labels produced by intensity thresholding of the raw SEM images and self labels produced by estimating orientation distribution of CNTs in these raw images. The proposed network extends a U-net-like encoder-decoder architecture with a novel two-component loss function. The first component is dice loss computed between the predicted segmentation maps and the weak segmentation labels. The second component is mean squared error (MSE) loss measuring the difference between the orientation histogram of the predicted segmentation map and the original raw image. Weighted sum of these two loss functions is used to train the proposed CNTSegNet network. The dice loss forces the network to perform background-foreground segmentation using local intensity features. The MSE loss guides the network with global orientation features and leads to refined segmentation results. The proposed system needs only a few-shot dataset for training. Thanks to it’s self-supervised nature, it can easily be adapted to new datasets. 

Carbon nanotube (CNT) forests are imaged using scanning electron microscopes (SEMs) that project their multilayered 3D structure into a single 2D image. Image analytics, particularly instance segmentation is needed to quantify structural characteristics and to predict correlations between structural morphology and physical properties. The inherent complexity of individual CNT structures is further increased in CNT forests due to density of CNTs, interactions between CNTs, occlusions, and lack of 3D information to resolve correspondences when multiple CNTs from different depths appear to cross in 2D. In this paper, we propose CNT-NeRF, a generative adversarial network (GAN) for simultaneous depth layer decomposition and segmentation of CNT forests in SEM images. The proposed network is trained using a multi-layer, photo-realistic synthetic dataset obtained by transferring the style of real CNT images to physics-based simulation data. Experiments show promising depth layer decomposition and accurate CNT segmentation results not only for the front layer but also for the partially occluded middle and back layers. This achievement is a significant step towards automated, image-based CNT forest structure characterization and physical property prediction. 

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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. 

The parameter space of CNT forest synthesis is vast and multidimensional, making experimental and/or numerical exploration of the synthesis prohibitive. We propose a more practical approach to explore the synthesis-process relationships of CNT forests using machine learning (ML) algorithms to infer the underlying complex physical processes. Currently, no such ML model linking CNT forest morphology to synthesis parameters has been demonstrated. In the current work, we use a physics-based numerical model to generate CNT forest morphology images with known synthesis parameters to train such a ML algorithm. The CNT forest synthesis variables of CNT diameter and CNT number densities are varied to generate a total of 12 distinct CNT forest classes. Images of the resultant CNT forests at different time steps during the growth and self-assembly process are then used as the training dataset. Based on the CNT forest structural morphology, multiple single and combined histogram-based texture descriptors are used as features to build a random forest (RF) classifier to predict class labels based on correlation of CNT forest physical attributes with the growth parameters. The machine learning model achieved an accuracy of up to 83.5% on predicting the synthesis conditions of CNT number density and diameter. These results are the first step towards rapidly characterizing CNT forest attributes using machine learning. Identifying the relevant process-structure interactions for the CNT forests using physics-based simulations and machine learning could rapidly advance the design, development, and adoption of CNT forest applications with varied morphologies and properties