Showing papers by "Joselito M. Razal published in 2014"

High-Performance Multifunctional Graphene Yarns: Toward Wearable All-Carbon Energy Storage Textiles

Abstract: The successful commercialization of smart wearable garments is hindered by the lack of fully integrated carbon-based energy storage devices into smart wearables. Since electrodes are the active components that determine the performance of energy storage systems, it is important to rationally design and engineer hierarchical architectures atboth the nano- and macroscale that can enjoy all of the necessary requirements for a perfect electrode. Here we demonstrate a large-scale flexible fabrication of highly porous high-performance multifunctional graphene oxide (GO) and rGO fibers and yarns by taking advantage of the intrinsic soft self-assembly behavior of ultralarge graphene oxide liquid crystalline dispersions. The produced yarns, which are the only practical form of these architectures for real-life device applications, were found to be mechanically robust (Young’s modulus in excess of 29 GPa) and exhibited high native electrical conductivity (2508 ± 632 S m–1) and exceptionally high specific surface ar...

Strain-Responsive Polyurethane/PEDOT:PSS Elastomeric Composite Fibers with High Electrical Conductivity

Abstract: It is a challenge to retain the high stretchability of an elastomer when used in polymer composites. Likewise, the high conductivity of organic conductors is typically compromised when used as filler in composite systems. Here, it is possible to achieve elastomeric fiber composites with high electrical conductivity at relatively low loading of the conductor and, more importantly, to attain mechanical properties that are useful in strain-sensing applications. The preparation of homogenous composite formulations from polyurethane (PU) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) that are also processable by fiber wet-spinning techniques are systematically evaluated. With increasing PEDOT:PSS loading in the fiber composites, the Young's modulus increases exponentially and the yield stress increases linearly. A model describing the effects of the reversible and irreversible deformations as a result of the re-arrangement of PEDOT:PSS filler networks within PU and how this relates to the electromechanical properties of the fibers during the tensile and cyclic stretching is presented. Conducting elastomeric fibers based on a composite of polyurethane (PU) and PEDOT:PSS, produced by a wet-spinning method, have high electrical conductivity and stretchability. These fibers can sense large strains by changes in resistance. The PU/PEDOT:PSS fiber is optimized to achieve the best strain sensing. PU/PEDOT:PSS fibers can be produced on a large scale and integrated into conventional textiles by weaving or knitting. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Formation and processability of liquid crystalline dispersions of graphene oxide

Abstract: Rational control over the formation and processability, and consequently final properties of graphene oxide liquid crystalline dispersions has been a long-standing goal in the development of bottom-up device fabrication processes. Here we report, the principal conditions through which such levels of control can be exercised to fine-tune dispersion properties for further processing.

Fabrication of graphene foam supported carbon nanotube/polyaniline hybrids for high-performance supercapacitor applications

Abstract: A large-scale, high-powered energy storage system is crucial for addressing the energy problem. The development of high-performance materials is a key issue in realizing the grid-scale applications of energy-storage devices. In this work, we describe a simple and scalable method for fabricating hybrids (graphenepyrrole/ carbon nanotube-polyaniline (GPCP)) using graphene foam as the supporting template. Graphene-pyrrole (G-Py) aerogels are prepared via a green hydrothermal route from two-dimensional materials such as graphene sheets, while a carbon nanotube/polyaniline (CNT/PANI) composite dispersion is obtained via the in situ polymerization method. The functional nanohybrid materials of GPCP can be assembled by simply dipping the prepared G-py aerogels into the CNT/PANI dispersion. The morphology of the obtained GPCP is investigated by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), which revealed that the CNT/PANI was uniformly deposited onto the surfaces of the graphene. The as-synthesized GPCP maintains its original three-dimensional hierarchical porous architecture, which favors the diffusion of the electrolyte ions into the inner region of the active materials. Such hybrid materials exhibit significant specific capacitance of up to 350 F g-1, making them promising in large-scale energy-storage device applications.

Performance enhancement of single-walled nanotube–microwave exfoliated graphene oxide composite electrodes using a stacked electrode configuration

Abstract: We report the development of a stacked electrode supercapacitor cell using stainless steel meshes as the current collectors and optimised single walled nanotubes (SWNT)–microwave exfoliated graphene oxide (mw rGO) composites as the electrode material. The introduction of mw rGO into a SWNT matrix creates an intertwined porous structure that enhances the electroactive surface area and capacitive performance due to the 3-D hierarchical structure that is formed. The composite structure was optimised by varying the weight ratio of the SWNTs and mw rGO. The best performing ratio was the 90% SWNT–10% mw rGO electrode which achieved a specific capacitance of 306 F g−1 (3 electrode measurement calculated at 20 mV s−1). The 90% SWNT–10% mw rGO was then fabricated into a stacked electrode configuration (SEC) which significantly enhanced the electrode performance per volume (1.43 mW h cm−3, & 6.25 W cm−3). Device testing showed excellent switching capability up to 10 A g−1, and very good stability over 10 000 cycles at 1.0 A g−1 with 93% capacity retention.

Automated quantification of neurite outgrowth orientation distributions on patterned surfaces

Abstract: Objective. We have developed an image analysis methodology for quantifying the anisotropy of neuronal projections on patterned substrates. Approach. Our method is based on the fitting of smoothing splines to the digital traces produced using a non-maximum suppression technique. This enables precise estimates of the local tangents uniformly along the neurite length, and leads to unbiased orientation distributions suitable for objectively assessing the anisotropy induced by tailored surfaces. Main results. In our application, we demonstrate that carbon nanotubes arrayed in parallel bundles over gold surfaces induce a considerable neurite anisotropy; a result which is relevant for regenerative medicine. Significance. Our pipeline is generally applicable to the study of fibrous materials on 2D surfaces and should also find applications in the study of DNA, microtubules, and other polymeric materials.