The Cation Radical Chain Cycloaddition Polymerization of N,3‐Bis(trans‐1‐2)carbazole: The Critical Importance of Intramolecular Hole Transfer in Cation Radical Cycloaddition Polymerization

The synthesis and polymerization of N,3‐[bis(trans‐1‐2)]carbazole ( 1 ) is reported. Using either the stable cation radical salt tris(4‐2)aminium hexachloroantimonate ( 2 ^+. ) or anodic oxidation to initiate the reaction, novel cycloaddition polymers are obtained in which the intermonomer linkages are of the cyclobutane, and to some extent of the Diels−Alder, type. A novel cation radical chain mechanism is proposed for the reaction, and extensive support for this mechanism is presented. The greatly enhanced reactivity of difunctional, as opposed to monofunctional, substrates in cation radical cycloadditions is dramatically highlighted by a comparison of the cycloaddition reactivity (rapid polymerization) of 1 versus N‐propenylcarbazole (inefficient cyclodimerization) under electrochemical oxidation conditions. The sharply enhanced reactivity of 1 is attributed to the availability of intramolecular hole transfer in the bifunctional but not the monofunctional case.     

Multiple defect diagnostics of gas turbine engine using SVM and RCGA-based ANN algorithms

An artificial neural network (ANN) based on the real coded genetic algorithm (RCGA) has been used with the support vector machine (SVM) for developing the defect diagnostics of the turbo-shaft engine of an aircraft. Nonlinearity increases due to the ascending number of input data in the off-design region. If the ANN algorithm is used by itself to determine defects under this condition, the possibility of falling in the local minima becomes high because of the large amount of learning data. To solve this problem, the expanded multi-class SVM has been used to reduce nonlinearity of input data. The RCGA, which is effective to search the global minima, has been applied to the ANN algorithm to obtain the magnitude of defects. As results, the number of learning data has been decreased and convergence and accuracy have been improved.     

Quantum‐dots‐based detection of hepatitis C virus (HCV) NS3 using RNA aptamer on chip

BACKGROUND: Over 170 million people, more than 3% of the world's population, suffer from the hepatitis C virus (HCV) infection and the rate of death from liver‐related mortality to HCV has increased. In respect of this, the development of assays for biological imaging should be urgently considered as an essential factor in diagnosis. RESULTS: A novel HCV‐detecting technique using a nanoparticle‐supported aptamer probe was demonstrated. With the aid of nanoparticle quantum dots (QDs) with carboxyl group as an imaging probe, and 5′‐end‐amine‐modified RNA oligonucleotide as a capturing probe, target HCV NS3 was visually detected on chip. The QDs‐based RNA aptamer for HCV NS3 showed high selectivity and specificity against other protein such as BSA. The detection limit of HCV NS3 protein was 5 ng mL^?1 level. CONCLUSION: With a novel strategy for protein–aptamer interaction, the feasibility of applying QDs‐based fluorescent detection technique to HCV viral protein assay for the development of a protein biochip was demonstrated. This scheme of QDs‐mediated imaging with a target‐oriented specific RNA aptamer for the detection of infectious HCV diseases provides an efficient strategy and a promising new platform for monitoring applications. Copyright © 2010 Society of Chemical Industry     

Characterization of the surface energies of functionalized multi-walled carbon nanotubes and their interfacial adhesion energies with various polymers

The surface energies of pristine multi-walled carbon nanotubes (MWCNTs) and MWCNTs functionalized with carboxylic acid (MWCNT-COOH), acyl chloride and ethyl amine were characterized, and the effects of the changes in MWCNT surface energies on the interfacial adhesion and reinforcement of the composites were explored. When the surface energy of pristine MWCNTs was compared to that of functionalized MWCNTs, a decrease in the dispersive surface energy and an increase in the polar surface energy were observed. Interfacial adhesion energies between MWCNTs and various polymers were estimated from surface energy values of MWCNTs and various polymers. Among the MWCNTs, polyethylene, polystyrene and bisphenol-A polycarbonate (PC) had the highest interfacial energy with pristine MWCNTs, while nylon 6,6 and polyacrylamine exhibited the highest interfacial energy with MWCNT-COOH. When tensile properties and adhesion at the interface of PC and nylon 6,6 composites containing MWCNTs were examined, composites having high interfacial adhesion energy exhibited greater adhesion at the interface and reinforcement.     

Hybrid fuzzy set-based polynomial neural networks and their development with the aid of genetic optimization and information granulation

We introduce a new architecture of feed-forward neural networks called hybrid fuzzy set-based polynomial neural networks (HFSPNNs) that are composed of heterogeneous feed-forward neural networks such as polynomial neural networks (PNNs) and fuzzy set-based polynomial neural networks (FSPNNs). We develop their comprehensive design methodology by embracing mechanisms of genetic optimization and information granulation. The construction of information granulation-driven HFSPNN exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the resulting information granulation-driven genetically optimized HFSPNN results from a synergistic usage of the hybrid system generated by combining original fuzzy set-based polynomial neurons (FSPNs)-based FSPNN with polynomial neurons (PNs)-based PNN. The design of the conventional genetically optimized HFPNN exploits the extended Group Method of Data Handling (GMDH) whose some essential parameters of the network being tuned with the use of genetic algorithms throughout the overall development process. Two general optimization mechanisms are explored. First, the structural optimization is realized via GAs while the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through extensive experimentation where we considered a number of modeling benchmarks (synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling).     

Beyond Plastics-to-Plastics to Waste-to-Products: Opportunities for Closing the Carbon Cycle via Chemical Recycling

The plastic crisis is a key driver for chemical recycling (CR), with focus placed on plastics circularity via Plastics-to-Plastics. This neglects its potential in enabling circularity for a wide range of carbon-containing waste and hinders a critical discussion of its broader contributions to decarbonizing the chemical sector. To address this gap, four CR routes and their integration in the conventional waste treatment and chemical production value chains are briefly reviewed, and reasons proposed for a focus expansion to Waste-to-Products to realize opportunities for closing the carbon cycle via chemical recycling.     

Towards a Structured Framework for Techno-Economic Analyses of Chemical Recycling Technologies

The role of chemical recycling (CR) as a valuable complementary strategy to mechanical recycling in closing the carbon cycle for carbon-containing waste is currently being discussed in political, economic, and social spheres. However, CR deployment is hindered by uncertainties regarding its environmental impacts and costs compared to conventional waste treatment and chemical production routes. While methods for assessing CR's environmental impacts are the focus of socio-political debates and investigations, techno-economic analyses (TEA) to evaluate costs of CR remain scarce. To contribute to a standardized framework for assessing the economic viability of CR technologies, this article draws on life cycle assessment and TEA literature to develop a six-stage TEA process for CR. A checklist is also presented to support transparent and comprehensive analyses.