Reversible Conversion of Dominant Polarity in Ambipolar Polymer/Graphene Oxide Hybrids

The possibility to selectively modulate the charge carrier transport in semiconducting materials is extremely challenging for the development of high performance and low-power consuming logic circuits. Systematical control over the polarity (electrons and holes) in transistor based on solution processed layer by layer polymer/graphene oxide hybrid system has been demonstrated. The conversion degree of the polarity is well controlled and reversible by trapping the opposite carriers. Basically, an electron device is switched to be a hole only device or vice versa. Finally, a hybrid layer ambipolar inverter is demonstrated in which almost no leakage of opposite carrier is found. This hybrid material has wide range of applications in planar p-n junctions and logic circuits for high-throughput manufacturing of printed electronic circuits.

Ultra-flexible nonvolatile memory based on donor-acceptor diketopyrrolopyrrole polymer blends

Flexible memory cell array based on high mobility donor-acceptor diketopyrrolopyrrole polymer has been demonstrated. The memory cell exhibits low read voltage, high cell-to-cell uniformity and good mechanical flexibility, and has reliable retention and endurance memory performance. The electrical properties of the memory devices are systematically investigated and modeled. Our results suggest that the polymer blends provide an important step towards high-density flexible nonvolatile memory devices.

Omission of author name in the article by Davidson et al (Arthritis Rheum, July 2013)

This article corrects: Brief Report: High-Throughput Sequencing of IL23R Reveals a Low-Frequency, Nonsynonymous Single-Nucleotide Polymorphism That Is Associated With Ankylosing Spondylitis in a Han Chinese Population Vol. 65, Issue 7, 1747–1752, Article first published online: 2 JUL 2013

Genome-wide association study for radiographic vertebral fractures: A potential role for the 16q24 BMD locus

Vertebral fracture risk is a heritable complex trait. The aim of this study was to identify genetic susceptibility factors for osteoporotic vertebral fractures applying a genome-wide association study (GWAS) approach. The GWAS discovery was based on the Rotterdam Study, a population-based study of elderly Dutch individuals aged >55years; and comprising 329 cases and 2666 controls with radiographic scoring (McCloskey-Kanis) and genetic data. Replication of one top-associated SNP was pursued by de-novo genotyping of 15 independent studies across Europe, the United States, and Australia and one Asian study. Radiographic vertebral fracture assessment was performed using McCloskey-Kanis or Genant semi-quantitative definitions. SNPs were analyzed in relation to vertebral fracture using logistic regression models corrected for age and sex. Fixed effects inverse variance and Han-Eskin alternative random effects meta-analyses were applied. Genome-wide significance was set at p<5×10-8. In the discovery, a SNP (rs11645938) on chromosome 16q24 was associated with the risk for vertebral fractures at p=4.6×10-8. However, the association was not significant across 5720 cases and 21,791 controls from 14 studies. Fixed-effects meta-analysis summary estimate was 1.06 (95% CI: 0.98-1.14; p=0.17), displaying high degree of heterogeneity (I2=57%; Qhet p=0.0006). Under Han-Eskin alternative random effects model the summary effect was significant (p=0.0005). The SNP maps to a region previously found associated with lumbar spine bone mineral density (LS-BMD) in two large meta-analyses from the GEFOS consortium. A false positive association in the GWAS discovery cannot be excluded, yet, the low-powered setting of the discovery and replication settings (appropriate to identify risk effect size >1.25) may still be consistent with an effect size <1.10, more of the type expected in complex traits. Larger effort in studies with standardized phenotype definitions is needed to confirm or reject the involvement of this locus on the risk for vertebral fractures.

Mapping bound plasmon propagation on a nanoscale stripe waveguide using quantum dots: Influence of spacer layer thickness

In this paper we image the highly confined long range plasmons of a nanoscale metal stripe waveguide using quantum emitters. Plasmons were excited using a highly focused 633 nm laser beam and a specially designed grating structure to provide stronger incoupling to the desired mode. A homogeneous thin layer of quantum dots was used to image the near field intensity of the propagating plasmons on the waveguide. We observed that the photoluminescence is quenched when the QD to metal surface distance is less than 10 nm. The optimised spacer layer thickness for the stripe waveguides was found to be around 20 nm. Authors believe that the findings of this paper prove beneficial for the development of plasmonic devices utilising stripe waveguides.

MnOx/carbon nanotube/reduced graphene oxide nanohybrids as high-performance supercapacitor electrodes

Nanohybrids consisting of both carbon and pseudocapacitive metal oxides are promising as high-performance electrodes to meet the key energy and power requirements of supercapacitors. However, the development of high-performance nanohybrids with controllable size, density, composition and morphology remains a formidable challenge. Here, we present a simple and robust approach to integrating manganese oxide (MnOx) nanoparticles onto flexible graphite paper using an ultrathin carbon nanotube/reduced graphene oxide (CNT/RGO) supporting layer. Supercapacitor electrodes employing the MnOx/CNT/RGO nanohybrids without any conductive additives or binders yield a specific capacitance of 1070 F g−1 at 10 mV s−1, which is among the highest values reported for a range of hybrid structures and is close to the theoretical capacity of MnOx. Moreover, atmospheric-pressure plasmas are used to functionalize the CNT/RGO supporting layer to improve the adhesion of MnOx nanoparticles, which results in theimproved cycling stability of the nanohybrid electrodes. These results provide information for the utilization of nanohybrids and plasma-related effects to synergistically enhance the performance of supercapacitors and may create new opportunities in areas such as catalysts, photosynthesis and electrochemical sensors

Biological application of carbon nanotubes and graphene

Carbon nanotubes (CNTs) and graphene are two representative nanomaterials comprised of purely element carbon [1,2]. Graphene is the two-dimensional, hexagonal sp2-carbon ring networks with one atomic layer thickness, while CNTs can be envisaged as one or several graphene sheets concentrically rolled up into a one-dimensional cylindrical structure, so-called singlewalled (SW) or multi-walled (MW) CNTs, respectively. Figure 12.1 shows the schematic diagram of structures of graphene, SWCNT and MWCNT. Owing to their exceptional mechanical, electrical, optical and thermal properties, CNTs and graphene have been widely considered as a new type of materials with great potentials to revolutionalize many of the biological and medical fields [3–5].

A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC

Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php

Single-walled carbon nanotube-based polymer monoliths for the enantioselective nano-liquid chromatographic separation of racemic pharmaceuticals

Many protocols have been used for extraction of DNA from Thraustochytrids. These generally involve the use of CTAB, phenol/chloroform and ethanol. They also feature mechanical grinding, sonication, N2 freezing or bead beating. However, the resulting chemical and physical damage to extracted DNA reduces its quality. The methods are also unsuitable for large numbers of samples. Commercially-available DNA extraction kits give better quality and yields but are expensive. Therefore, an optimized DNA extraction protocol was developed which is suitable for Thraustochytrids to both minimise expensive and time-consuming steps prior to DNA extraction and also to improve the yield. The most effective method is a combination of single bead in TissueLyser (Qiagen) and Proteinase K. Results were conclusive: both the quality and the yield of extracted DNA were higher than with any other method giving an average yield of 8.5 µg/100 mg biomass.

Pre-lithiation of onion-like carbon/MoS2 nano-urchin anodes for high-performance rechargeable lithium ion batteries

Hybrid urchin-like nanostructures composed of a spherical onion-like carbon (OLC) core and MoS2 nanoleaves were synthesized by a simple solvothermal method followed by thermal annealing treatment. Compared to commercial MoS2 powder, MoS2/OLC nanocomposites exhibit enhanced electrochemical performance as anode materials of lithium-ion batteries (LIBs) with a specific capacity of 853 mA h g−1 at a current density of 50 mA g−1 after 60 cycles, and a moderate initial coulombic efficiency of 71.1%. Furthermore, a simple pre-lithiation method based on direct contact of lithium foil with MoS2/OLC nano-urchins was used to achieve a very high coulombic efficiency of 97.6% in the first discharge/charge cycle, which is at least 26% higher compared to that of pristine MoS2/OLC nano-urchins. This pre-lithiation method can be generalized to develop other carbon-metal sulfide nanohybrids for LIB anode materials. These results may open up a new avenue for the development of the next-generation high-performance LIBs.

Carbon nanotubes on nanoporous alumina: From surface mats to conformal pore filling

Control over nucleation and growth of multi-walled carbon nanotubes in the nanochannels of porous alumina membranes by several combinations of posttreatments, namely exposing the membrane top surface to atmospheric plasma jet and application of standard S1813 photoresist as an additional carbon precursor, is demonstrated. The nanotubes grown after plasma treatment nucleated inside the channels and did not form fibrous mats on the surface. Thus, the nanotube growth mode can be controlled by surface treatment and application of additional precursor, and complex nanotube-based structures can be produced for various applications. A plausible mechanism of nanotube nucleation and growth in the channels is proposed, based on the estimated depth of ion flux penetration into the channels.

Plasma-enabled carbon nanostructures for early diagnosis of neurodegenerative diseases

Carbon nanostructures (CNs) are amongst the most promising biorecognition nanomaterials due to their unprecedented optical, electrical and structural properties. As such, CNs may be harnessed to tackle the detrimental public health and socio-economic adversities associated with neurodegenerative diseases (NDs). In particular, CNs may be tailored for a specific determination of biomarkers indicative of NDs. However, the realization of such a biosensor represents a significant technological challenge in the uniform fabrication of CNs with outstanding qualities in order to facilitate a highly-sensitive detection of biomarkers suspended in complex biological environments. Notably, the versatility of plasma-based techniques for the synthesis and surface modification of CNs may be embraced to optimize the biorecognition performance and capabilities. This review surveys the recent advances in CN-based biosensors, and highlights the benefits of plasma-processing techniques to enable, enhance, and tailor the performance and optimize the fabrication of CNs, towards the construction of biosensors with unparalleled performance for the early diagnosis of NDs, via a plethora of energy-efficient, environmentally-benign, and inexpensive approaches.

Catalyst engineering for lithium ion batteries: The catalytic role of Ge in enhancing the electrochemical performance of SnO2(GeO2)0.13/G anodes

The catalytic role of germanium (Ge) was investigated to improve the electrochemical performance of tin dioxide grown on graphene (SnO(2)/G) nanocomposites as an anode material of lithium ion batteries (LIBs). Germanium dioxide (GeO(20) and SnO(2) nanoparticles (<10 nm) were uniformly anchored on the graphene sheets via a simple single-step hydrothermal method. The synthesized SnO(2)(GeO(2))0.13/G nanocomposites can deliver a capacity of 1200 mA h g(-1) at a current density of 100 mA g(-1), which is much higher than the traditional theoretical specific capacity of such nanocomposites (∼ 702 mA h g(-1)). More importantly, the SnO(2)(GeO(2))0.13/G nanocomposites exhibited an improved rate, large current capability (885 mA h g(-1) at a discharge current of 2000 mA g(-1)) and excellent long cycling stability (almost 100% retention after 600 cycles). The enhanced electrochemical performance was attributed to the catalytic effect of Ge, which enabled the reversible reaction of metals (Sn and Ge) to metals oxide (SnO(2) and GeO(2)) during the charge/discharge processes. Our demonstrated approach towards nanocomposite catalyst engineering opens new avenues for next-generation high-performance rechargeable Li-ion batteries anode materials.

Synergistic fusion of vertical graphene nanosheets and carbon nanotubes for high-performance supercapacitor electrodes

Graphene and carbon nanotubes (CNTs) are attractive electrode materials for supercapacitors. However, challenges such as the substrate-limited growth of CNTs, nanotube bundling in liquid electrolytes, under-utilized basal planes, and stacking of graphene sheets have so far impeded their widespread application. Here we present a hybrid structure formed by the direct growth of CNTs onto vertical graphene nanosheets (VGNS). VGNS are fabricated by a green plasma-assisted method to break down and reconstruct a natural precursor into an ordered graphitic structure. The synergistic combination of CNTs and VGNS overcomes the challenges intrinsic to both materials. The resulting VGNS/CNTs hybrids show a high specific capacitance with good cycling stability. The charge storage is based mainly on the non-Faradaic mechanism. In addition, a series of optimization experiments were conducted to reveal the critical factors that are required to achieve the demonstrated high supercapacitor performance.

Secondary structure element alignment kernel method for prediction of protein structural classes

In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α + β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://math.xtu.edu.cn/myphp/math/research/source/SSEAK_source_code.rar.