Sep 1, 2015 · In this work we present the results from the use of ionic liquids (ILs) for the sample preparation of carbon nanotubes prior to their characterization by scanning electron microscopy (SEM). Their use implies some advantages such as no damage of the surface of CNTs, the aggregation of the nanoparticles decreases while the number of CNTs ...

Jan 1, 2018 · A methodology is presented to determine the length of well individualized single-walled carbon nanotubes (SWCNTs) by means of scanning electron microscopy (SEM). Accurate measurements on wide areas of the sample can be achieved in …

May 26, 2021 · In this study, the capacity for Raman spectroscopy, XPS, and SEM to characterize MWCNTs in nanocomposite samples at extremely low MWCNT concentration regime is evaluated.

Jan 30, 2018 · Here we show that a plasma-hydrophilized carbon nanotube (CNT)-coated polyethylene terephthalate (PET) tape effectively resolves these issues and produces SEM images of comparable quality...

The CNT-embedded PS/PAN nanofibrous mat, as an efficient and sustainable oil sorbent, will significantly contribute to tackling oil spills in the ocean owing to its increased oil sorption...

Apr 23, 2018 · Carbon nanotubes (CNTs) are anticipated to be the successor of silicon in next-generation integrated circuits. However, one great challenge to the practical application of this concept is the...

Oct 31, 2022 · Through the experiments based on it, we found that the accelerating voltage of 7.5kV enables us to get the sharpest image of the microstructure of CNT surface. Then we compared theoretical results and experimental results.

In their latest paper, Matt Maschmann, Filiz Bunyak and co-authors have outlined a deep learning technique to segment carbon nanotube forests in scanning electron microscopy (SEM) images. This technique allows them to detect individual CNTs within a forest which can help eventually characterize their properties.

Revealing a "true" picture of the carbon nanotube (CNT) growth front at the catalyst surface is critical to understanding the mechanism of CNT growth.

In this paper, we pro-pose CNT-NeRF, a generative adversarial network (GAN) for simultaneous depth layer decomposition and segmenta-tion 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.