Transparent and flexible films of new segmented polyurethane nanocomposites incorporated by NH2-functionalized TiO2 nanoparticles

In this work, TiO₂ nanoparticles are surface modified by NH₂-terminated organic moieties arised from 4,4′-methylene diphenyl diisocyanate (MDI). These nanoparticles are incorporated into ether-based segmented polyurethane (SPU) matrix. MDI is utilized as monomer together with poly(tetramethylene oxide) (PTMO) comonomer for preparing the final polymer as well. The NH₂-functionalized TiO₂ nanoparticles are covalently linked to the NCO terminals of the resulting SPU macromolecules during film preparation stage. Therefore, in addition to butylene glycol, these surface modified nanoparticles with enhanced organophilicity could play the role of the second chain extender of NCO-capped SPU macromolecules through formation of urea linkages. Optical and thermal behaviors of the transparent and flexible film (SPU/TiO₂–MDI) is compared with those of unmodified TiO₂ (SPU/TiO₂) and TiO₂-unloaded SPU films. Though the particle loading is only 5 wt.%, incorporation of TiO₂ and TiO₂–MDI nanoparticles into the SPU polymer enhances significantly the light absorption in UV region at 300–400 nm. SEM images of the prepared films clearly show a considerable decrease in particle aggregation for TiO₂–MDI into SPU matrix compared to that of unmodified TiO₂. TG analyses indicate a one-step decomposition pattern with onset temperatures of about 360 and 380 °C for neat SPU and SPU/TiO₂–MDI, respectively. Moreover, DTA thermograms of both nanocomposites show obviously two exothermic phase transitions in the thermal range of 330–440 °C.     

Top illuminated organic photodetectors with dielectric/metal/dielectric transparent anode

The top illuminated organic photodetectors (OPDs) with a Dielectric/Metal/Dielectric (DMD) transparent anode are fabricated. The transparent electrode is composed of molybdenum trioxide (MoO₃)/silver (Ag)/MoO₃ layers and zinc oxide (ZnO)/aluminum (Al) is used for bottom cathode. The optimized DMD electrode has an optical transmittance of 85.7% at the wavelength of 546 nm and sheet resistance of ∼6 Ω/sq. The fabricated OPDs exhibit a high detectivity and wide range linearity.     

Nonlinear dynamic behaviors and control based on simulation of high-purity heat integrated air separation column

In this paper, the dynamic behaviors on the basis of simulation for high-purity heat integrated air separation column (HIASC) are studied. A nonlinear generic model control (GMC) scheme is proposed based on the nonlinear behavior analyses of a HIASC process, and an adaptive generic model control (AGMC) scheme is further presented to correct the model parameters online. Related internal model control (IMC) scheme and multi-loop PID (M-PID) scheme are also developed as the comparative base. The comparative researches are carried out among these linear and nonlinear control schemes in detail. The simulation research results show that the proposed AGMC schemes present advantages in both servo control and regulatory control for the high-purity HIASC.     

Resistive switching characteristics of ZnO–graphene quantum dots and their use as an active component of an organic memory cell with one diode-one resistor architecture

We investigated the resistive switching characteristics of a polystyrene:ZnO–graphene quantum dots system and its potential application in a one diode-one resistor architecture of an organic memory cell. The log–log I–V plot and the temperature-variable I–V measurements revealed that the switching mechanism in a low-current state is closely related to thermally activated transport. The turn-on process was induced by a space-charge-limited current mechanism resulted from the ZnO–graphene quantum dots acting as charge trap sites, and charge transfer through filamentary path. The memory device with a diode presented a ∼10³ I_ON/I_OFF ratio, stable endurance cycles (10² cycles) and retention times (10⁴ s), and uniform cell-to-cell switching. The one diode-one resistor architecture can effectively reduce cross-talk issue and realize a cross bar array as large as ∼3 kbit in the readout margin estimation. Furthermore, a specific word was encoded using the standard ASCII character code.     

Carbon nanotube filter for heavy metal ion adsorption

This study investigated carbon nanotube filtration technology using catalyst particles supported on silicalite-1–biomorphic carbon materials (BCMs). Aqueous solutions of Mn(II), Cu(II), Cr(III), Cd(II), and Pb(II) were used to test the efficiency of heavy metal ions removal. Carbon nanotubes (CNTs) were synthesized and grown on BCMs by the chemical vapor deposition method catalyzed with the catalyst (Co, Fe, and Ni). The synthesized CNTs with Co– and Fe– nanoparticles were typically multi-walled carbon nanotubes, and they showed good crystallinity (I_D/I_G = 1.05) and yield of (11.10 and 8.86) %. The removal efficiency of Mn(II), Cu(II), Cr(III), Cd(II), and Pb(II) ions using Co-catalyzed CNT filter was 97.57%, 98.01%, 97.89%, 97.42%, and 99.99%, respectively.     

Hydrocarbon-based electrode ionomer for proton exchange membrane fuel cells

The electrode ionomer is a key factor that significantly affects the catalyst layer morphology and fuel cell performance. Herein, sulfonated poly(arylene ether sulfone)-based electrode ionomers with polymers of various molecular weights and alcohol/water mixtures were prepared, and those comprising the alcohol/water mixture showed a higher performance than the ones prepared using higher boiling solvents, such as dimethylacetamide; this is owing to the formation of the uniformly dispersed ionomer catalyst layer. The relation between ionomer molecular weight for the same polymer structure and the sulfonation degree was investigated. Because the chain length of polymer varies with molecular weight and chain entanglement degree, its molecular weight affects the electrode morphology. As the ionomer covered the catalyst, the agglomerates formed were of different morphologies according to their molecular weight, which could be deduced indirectly through dynamic light scattering and scanning electron microscopy. Additionally, the fuel cell performance was confirmed in the current-voltage curve.     

The improvement of the self-healing ability of MoSi2 coatings at 900–1200 °C by introducing SiB6

The brittleness of MoSi₂ ceramic and the thermal mismatch between MoSi₂ coating and C / C composite lead to brittle cracking of the coating at 900−1200 °C. This problem has been overcome in this studyby introducing submicron-SiB₆ into the coating. The pre-fabricated cracks and a kinetics model of hot-pressed SiB₆-MoSi₂ ceramic could quantitatively predict the glass growth and crack healing. As expected, enhancing temperature and SiB₆ content increased the growth rate of the borosilicate glass and the crack healing ability of MoSi₂ ceramic, which was ascribed to the lower oxidation activation energy and larger specific surface area of submicron-SiB₆. For the plasma sprayed coating, SiB₆ with submicron structure was benefit for cracking inhibition and formation of borosilicate glass during oxidation, reducing the oxygen permeability and the consumption of inner coating. Hence, the 15 % SiB₆-MoSi₂ coatings raised the protection times to 84 and 120 h at 900 and 1200 °C respectively, presenting favorable oxidation protective performance.     

An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

Model checking temporal knowledge and commitments in multi-agent systems using reduction

Though modeling and verifying Multi-Agent Systems (MASs) have long been under study, there are still challenges when many different aspects need to be considered simultaneously. In fact, various frameworks have been carried out for modeling and verifying MASs with respect to knowledge and social commitments independently. However, considering them under the same framework still needs further investigation, particularly from the verification perspective. In this article, we present a new technique for model checking the logic of knowledge and commitments (CTLKC⁺). The proposed technique is fully-automatic and reduction-based in which we transform the problem of model checking CTLKC⁺ into the problem of model checking an existing logic of action called ARCTL. Concretely, we construct a set of transformation rules to formally reduce the CTLKC⁺ model into an ARCTL model and CTLKC⁺ formulae into ARCTL formulae to get benefit from the extended version of NuSMV symbolic model checker of ARCTL. Compared to a recent approach that reduces the problem of model checking CTLKC⁺ to another logic of action called GCTL¹, our technique has better scalability and efficiency. We also analyze the complexity of the proposed model checking technique. The results of this analysis reveal that the complexity of our reduction-based procedure is PSPACE-complete for local concurrent programs with respect to the size of these programs and the length of the formula being checked. From the time perspective, we prove that the complexity of the proposed approach is P-complete with regard to the size of the model and length of the formula, which makes it efficient. Finally, we implement our model checking approach on top of extended NuSMV and report verification results for the verification of the NetBill protocol, taken from business domain, against some desirable properties. The obtained results show the effectiveness of our model checking approach when the system scales up.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.