Synthesis, polymerization and wool coating studies of 3-iso-butylpyrrole and 3-iso-pentylpyrrole

In a four-step method starting from pyrrole, the synthesis of 3-iso-butylpyrrole and 3-iso-pentylpyrrole, was achieved in 45 and 44% yields, respectively. Polymerization studies of these branched alkyl pyrroles are described and the results compared with those obtained for the unbranched structural isomers n-butyl and n-pentylpyrrole. A series of conductive textiles were produced by the chemical polymerization of the iso-alkylpyrroles using both solution and vapour polymerization techniques. Fabrics coated with poly-iso-alkylpyrrole formed using the solution polymerization method had a lower surface resistance than those formed using the vapour polymerization method. These conductivity results were in direct contrast to those previously obtained for 3-n-alkylpyrroles on fabrics. A remarkable crystal-like growth on the surface of the textile fabric was observed when solution polymerization of 3-iso-pentylpyrrole was employed—reinforcing the notion that subtle changes in monomer structure can drastically affect bulk polymer properties.

Conducting nylon, cotton and wool yarns by continuous vapor polymerization of pyrrole

Conductive textile yarns were prepared by a continuous vapor polymerization method; the application of polypyrrole by the continuous vapor polymerization method used is designed for the easy adaptation into industrial procedures. The resultant conductive yarns were examined by longitudinal and cross-sectional views, clearly showing the varying levels of penetration of the polymer into the yarn structure. It was found that for wool the optimum specific resistance was achieved by using the 400 TPM yarn with a FeCl3 solution concentration of 80 g/L FeCl3 to produce 1.69 Ω g/cm2. For cotton yarn, the optimum specific resistance of 1.53 Ω g/cm2 was obtained with 80 g/L of a FeCl3 solution.

The role of unbound oligomers in the nucleation and growth of electrodeposited polypyrrole and method for preparing high strength, high conductivity films

Polypyrrole is a material with immensely useful properties suitable for a wide range of electrochemical applications, but its development has been hindered by cumbersome manufacturing processes. Here we show that a simple modification to the standard electrochemical polymerization method produces polypyrrole films of equivalently high conductivity and superior mechanical properties in one-tenth of the polymerization time. Preparing the film as a series of electrodeposited layers with thorough solvent washing between layering was found to produce excellent quality films even when layer deposition was accelerated by high current. The washing step between the sequentially polymerized layers altered the deposition mechanism, eliminating the typical dendritic growth and generating nonporous deposits. Solvent washing was shown to reduce the concentration of oligomeric species in the near-electrode region and hinder the three-dimensional growth mechanism that occurs by deposition of secondary particles from solution. As artificial muscles, the high density sequentially polymerized films produced the highest mechanical work output yet reported for polypyrrole actuators.

Conductive poly(α,ω-bis(3-pyrrolyl)alkanes)-coated wool fabrics

Cross-linked poly(α,ω-bis(3-pyrrolyl)alkanes) were directly applied to woven wool substrates by either chemical, vapour or mist polymerization methods. Choice of dopant could greatly improve the surface resistance. The optimum coating on textiles with the lowest surface resistance, highest colour-fastness and stability was achieved using a mist polymerization method with 1,8-bis(pyrrolyl)octane, iron(III) chloride (FeCl₃) as the oxidant and p-toluene sulfonic acid sodium salt (pTSA) as the dopant.

Proton transport properties in zwitterion blends with brønsted acids

We describe zwitterion, 3-(1-butyl-1H-imidazol-3-ium-3-yl)propane-1-sulfonate (Bimps), mixtures with 1,1,1-trifluoro-N-(trifluoromethylsulfonyl)methanesulfoneamide (HN(Tf)₂) as new proton transport electrolytes. We report proton transport mechanisms in the mixtures based on results from several methods including thermal analyses, the complex-impedance method, and the pulsed field gradient spin echo NMR (pfg-NMR) method. The glass transition temperature (Tg) of the mixtures decreased with increasing HN(Tf)₂ concentration up to 50 mol %. The Tg remained constant at −55 °C with further acid doping. The ionic conductivity of HN(Tf)₂ mixtures increased with the HN(Tf)₂ content up to 50 mol %. Beyond that ratio, the mixtures showed no increase in ionic conductivity (10⁻⁴ S cm⁻¹ at room temperature). This tendency agrees well with that of Tg. However, the self-diffusion coefficients obtained from the pfg-NMR method increased with HN(Tf)₂ content even above 50 mol % for all component ions. At HN(Tf)₂ 50 mol %, the proton diffusion of HN(Tf)₂ was the fastest in the mixture. These results suggest that Bimps cannot dissociate excess HN(Tf)₂, that is, the excess HN(Tf)₂ exists as molecular HN(Tf)₂ in the mixtures. The zwitterion, Bimps, forms a 1:1 complex with HN(Tf)₂ and the proton transport property in this mixture is superior to those of other mixing ratios. Furthermore, CH₃SO₃H and CF₃SO₃H were mixed with Bimps for comparison. Both systems showed a similar tendency, which differed from that of the HN(Tf)₂ system. The Tg decreased linearly with increasing acid content for every mixing ratio, while the ionic conductivity increased linearly. Proton transport properties in zwitterion/acid mixtures were strongly affected by the acid species added.

Dielectric percolative composites with high dielectric constant and low dielectric loss based on sulfonated poly(aryl ether ketone) and a-MWCNTs coated with polyaniline

Acidified multi-walled carbon nanotubes (a-MWCNTs) coated with polyaniline (PANI) (a-MWCNTs@PANI) nanofiller were prepared by in situ polymerization. Novel dielectric percolative composites, sulfonated poly(aryl ether ketone) (SPAEK)/a-MWCNTs@PANI, with high dielectric constant and low dielectric loss were fabricated using simple solution blending technique. A SPAEK/a-MWCNTs@PANI composite prepared in this fashion exhibited a high dielectric constant above 800, a dielectric loss tangent less than 1.1 at 10 kHz and room temperature. The morphological study of composites by SEM suggested that the in situ polymerization method of preparing a-MWCNTs@PANI nanofillers was useful to achieve good dispersion of fillers in SPAEK matrix.

On exploratory factor analysis: a review of recent evidence, an assessment of current practice, and recommendations for future use

Exploratory factor analysis (hereafter, factor analysis) is a complex statistical method that is integral to many fields of research. Using factor analysis requires researchers to make several decisions, each of which affects the solutions generated. In this paper, we focus on five major decisions that are made in conducting factor analysis: (i) establishing how large the sample needs to be, (ii) choosing between factor analysis and principal components analysis, (iii) determining the number of factors to retain, (iv) selecting a method of data extraction, and (v) deciding upon the methods of factor rotation. The purpose of this paper is threefold: (i) to review the literature with respect to these five decisions, (ii) to assess current practices in nursing research, and (iii) to offer recommendations for future use. The literature reviews illustrate that factor analysis remains a dynamic field of study, with recent research having practical implications for those who use this statistical method. The assessment was conducted on 54 factor analysis (and principal components analysis) solutions presented in the results sections of 28 papers published in the 2012 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. The main findings from the assessment were that researchers commonly used (a) participants-to-items ratios for determining sample sizes (used for 43% of solutions), (b) principal components analysis (61%) rather than factor analysis (39%), (c) the eigenvalues greater than one rule and screen tests to decide upon the numbers of factors/components to retain (61% and 46%, respectively), (d) principal components analysis and unweighted least squares as methods of data extraction (61% and 19%, respectively), and (e) the Varimax method of rotation (44%). In general, well-established, but out-dated, heuristics and practices informed decision making with respect to the performance of factor analysis in nursing studies. Based on the findings from factor analysis research, it seems likely that the use of such methods may have had a material, adverse effect on the solutions generated. We offer recommendations for future practice with respect to each of the five decisions discussed in this paper.

Graphene/tri-block copolymer composites prepared via RAFT polymerizations for dual controlled drug delivery via pH stimulation and biodegradation

A novel tri-block copolymer poly(oxopentanoate ethyl methacrylate)-block-poly(pyridyl disulfide ethyl acrylate)-block-poly(ethylene glycol acrylate) [poly(OEMA-b-PDEA-b-PEGA)], retaining active keto groups and pyridyl disulfide (PDS) side functionalities, was synthesized as a drug delivery vehicle using reversible addition-fragmentation chain transfer (RAFT) polymerization method. One mimic drug pyridine-2-thione (PT) was introduced into the monomer, PDEA for copolymerization. The other mimic drug O-benzylhydroxylamine (BHA) was conjugated with tri-block copolymer via efficient oxime coupling chemistry, followed by the attachment onto graphene via π-π stacking interaction to obtain a graphene/tri-block copolymer composite. 1H NMR, UV-vis absorption spectroscopy, fluorescence spectroscopy, gel permeation chromatography (GPC), atomic force microscope (AFM) and transmission electron microscope (TEM) were used to verify the successful step-wise preparation of the tri-block copolymer and drug loaded composite. In vitro release behaviors of BHA and PT from graphene/tri-block copolymer composite via dual drug release mechanisms were investigated. BHA can be released under acid environment, while PT will be released in the presence of reducing agents, such as dithiothreitol (DTT) or glutathione (GSH). It can be envisioned that this novel composite could be exploited as a novel intracellular drug delivery system via dual release mechanisms.

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

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.

pH-detachable polymer brushes formed using titanium−diol coordination chemistry and living radical polymerization (RAFT)

pH-detachable poly(styrene) brushes formed on indium−tin oxide (ITO) glass substrates using metal complex chemistry and reversible addition−fragmentation chain transfer (RAFT) polymerization was described. These pH-detachable polymeric brushes were generated using both “graft-from” and “graft-to” methodologies. The methodologies involved either the surface self-assembly of catechol-functional RAFT agents (graft-from) or catechol-terminal polymer chains (graft-to) onto the ITO substrate via titanium−diol coordination. The stepwise functionalization of the ITO glass surfaces was characterized successfully using X-ray photoelectron spectroscopy (XPS) and contact angle measurement. Poly(styrene) brushes generated using the “graft-from” method were denser than those generated using the “graft-to” method, as exemplified by atom force microscopy (AFM) and quantified using cyclic voltammetry. Poly(styrene) brushes assembled using both methods could be detached easily by manipulating the pH of the brush environment. Cyclic voltammetry was utilized to calculate precisely the surface coverage of the RAFT functionality and polymeric brush density.

A method for tracking rats in a complex and completely dark environment using computerized video analysis

The automated tracking of rodents in open field environments has become a standard laboratory technique for the investigation of the effects of drugs, novel therapeutic interventions and genetic mutations on behavior. Here, we develop an extension of this technique that permits tracking in full darkness through a complex (‘enriched’) environment comprising naturalistic structures such as tunnels and hides. To eliminate unwanted light reflections and tape noise, we developed a unique video filter that combines the advantages of differential and non-differential filtering. This filter enabled the tracking of albino rats against a relatively dark background to an accuracy of approximately 97% compared to hand tracking of the same animal, irrespective of whether the rat was inside a hide box or tunnel or out in the open field. The system as a whole can be easily deployed using standard PCs and inexpensive infrared cameras and lights.

K-Complex Detection Using a Hybrid-Synergic Machine Learning Method

Sleep stage identification is the first step in modern sleep disorder diagnostics process. K-complex is an indicator for the sleep stage 2. However, due to the ambiguity of the translation of the medical standards into a computer-based procedure, reliability of automated K-complex detection from the EEG wave is still far from expectation. More specifically, there are some significant barriers to the research of automatic K-complex detection. First, there is no adequate description of K-complex that makes it difficult to develop automatic detection algorithm. Second, human experts only provided the label for whether a whole EEG segment contains K-complex or not, rather than individual labels for each subsegment. These barriers render most pattern recognition algorithms inapplicable in detecting K-complex. In this paper, we attempt to address these two challenges, by designing a new feature extraction method that can transform visual features of the EEG wave with any length into mathematical representation and proposing a hybrid-synergic machine learning method to build a K-complex classifier. The tenfold cross-validation results indicate that both the accuracy and the precision of this proposed model are at least as good as a human expert in K-complex detection.

Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors

Precise and reliable modelling of polymerization reactor is challenging due to its complex reaction mechanism and non-linear nature. Researchers often make several assumptions when deriving theories and developing models for polymerization reactor. Therefore, traditional available models suffer from high prediction error. In contrast, data-driven modelling techniques provide a powerful framework to describe the dynamic behaviour of polymerization reactor. However, the traditional NN prediction performance is significantly dropped in the presence of polymerization process disturbances. Besides, uncertainty effects caused by disturbances present in reactor operation can be properly quantified through construction of prediction intervals (PIs) for model outputs. In this study, we propose and apply a PI-based neural network (PI-NN) model for the free radical polymerization system. This strategy avoids assumptions made in traditional modelling techniques for polymerization reactor system. Lower upper bound estimation (LUBE) method is used to develop PI-NN model for uncertainty quantification. To further improve the quality of model, a new method is proposed for aggregation of upper and lower bounds of PIs obtained from individual PI-NN models. Simulation results reveal that combined PI-NN performance is superior to those individual PI-NN models in terms of PI quality. Besides, constructed PIs are able to properly quantify effects of uncertainties in reactor operation, where these can be later used as part of the control process. © 2014 Taiwan Institute of Chemical Engineers.