READ THESE TERMS AND CONDITIONS CAREFULLY BEFORE USING THIS WEBSITE.http://nparc.cisti-icist.nrc-cnrc.gc.ca/eng/copyright Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. Questions? Contact the NRC Publications Archive team atPublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. If you wish to email the authors directly, please see the first page of the publication for their contact information. NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. NRC Publications Record / Notice d'Archives des publications de CNRC:http://nparc.cisti-icist.nrc-cnrc.gc.ca/eng/view/object/?id=5c9e5f2f-e987-427a-a147-3f9bc515eded http://nparc.cisti-icist.nrc-cnrc.gc.ca/fra/voir/objet/?id=5c9e5f2f-e987-427a-a147-3f9bc515eded ABSTRACT: Fully printed thin film transistors (TFT) based on poly(9,9-di-n-dodecylfluorene) (PFDD) wrapped semiconducting single walled carbon nanotube (SWCNT) channels are fabricated by a practical route that combines roll-to-roll (R2R) gravure and ink jet printing. SWCNT network density is easily controlled via ink formulation (concentration and polymer:CNT ratio) and jetting conditions (droplet size, drop spacing, and number of printed layers). Optimum inkjet printing conditions are established on Si/SiO 2 in which an ink consisting of 6:1 PFDD:SWCNT ratio with 50 mg L −1SWCNT concentration printed at a drop spacing of 20 μm results in TFTs with mobilities of ∼25 cm 2 V −1 s −1 and on-/offcurrent ratios > 10 5 . These conditions yield excellent network uniformity and are used in a fully additive process to fabricate fully printed TFTs on PET substrates with mobility values > 5 cm 2 V −1 s −1 (R2R printed gate electrode and dielectric; inkjet printed channel and source/drain electrodes). An inkjet printed encapsulation layer completes the TFT process (fabricated in bottom gate, top contact TFT configuration) and provides mobilities > 1 cm 2 V −1 s −1 with good operational stability, based on the performance of an inverter circuit. An array of 20 TFTs shows that most have less than 10% variability in terms of threshold voltage, transconductance, on-current, and subthreshold swing.

The chemistry of carbon nanotubes has become an area of intense research as chemical derivatization is the only means for modifying the properties of these highly interesting and technologically promising materials. Specifically, numerous researchers have focused on improving the solubility of carbon nanotubes through chemical grafting. To this end, significant recent effort has been devoted to the attachment of polymers to the nanotube surface, as macromolecules can be more effective in modifying nanotube solubility properties than small molecules. In addition, the use of functional polymers has enabled the preparation of polymer-nanotube composite materials that demonstrate a variety of interesting properties, such as responsiveness to environmental stimuli (solvent, temperature, pH), the ability to complex metal ions, and photoinduced electron transport. A variety of different techniques have now been developed for the functionalization of carbon nanotubes with polymers, including "grafting to", "grafting from", and supramolecular interactions. This review will focus on recent developments in the use of living radical polymerization methods for the functionalization of carbon nanotubes with well-defined polymers.

Current processes to manufacture nanotubes at commercial scales are unfortunately imperfect and commonly generate undesirable byproducts. After manufacturing, purification is necessary and is a rate and cost determining step in advancing the development of commercial products based on nanotubes. Boron nitride nanotubes (BNNTs) produced without metal catalysts from high-temperature processes are known to contain a significant amount (e.g., 50 wt %) of various boron derivatives. Herein we report a simple yet efficient and scalable process to purify these types of BNNT materials at commercial scales, from a few grams to hundreds of grams, at purity over 85 wt % in a single step. The process relies on a vertically mounted flow tube reactor and scrubber system that can be operated under pure or diluted chlorine gas flow at temperatures up to 1100 °C. The main chemical reactions driving the purification are the conversion of boron and BN derivatives into BCl3 and HCl, which are removed as gaseous species, while pristine BNNTs are left behind. The preferential etching of impurities over pristine BNNTs shows the extreme chemical resistance of BNNTs in this harsh environment and opens up new applications for this nanomaterial. The process has been examined at various temperatures, up to 1050 °C, and the resulting materials display improved BNNT purity and quality across a range of imaging and spectroscopic assessments. The recommended temperature to optimize quality with yield is 950 °C, although higher quality material is obtained at a higher temperature.

We introduce a new method, called CNNAS (convolutional neural networks for atomistic systems), for calculating the total energy of atomic systems which rivals the computational cost of empirical potentials while maintaining the accuracy of ab initio calculations. This method uses deep convolutional neural networks (CNNs), where the input to these networks are simple representations of the atomic structure. We use this approach to predict energies obtained using density functional theory (DFT) for 2D hexagonal lattices of various types. Using a dataset consisting of graphene, hexagonal boron nitride (hBN), and graphene-hBN heterostructures, with and without defects, we trained a deep CNN that is capable of predicting DFT energies to an extremely high accuracy, with a mean absolute error (MAE) of 0.198 meV / atom (maximum absolute error of 16.1 meV / atom). To explore our new methodology, we investigate the ability of a deep neural network (DNN) in predicting a Lennard-Jones energy and separation distance for a dataset of dimer molecules in both two and three dimensions. In addition, we systematically investigate the flexibility of the deep learning models by performing interpolation and extrapolation tests.