Cycloadditions of Trans-Cyclooctenes and Nitrones as Tools for Bioorthogonal Labelling

Trans-cyclooctenes (TCOs) represent interesting and highly reactive dipolarophiles for organic transformations including bioorthogonal chemistry. Herein we show that TCOs react rapidly with nitrones and that these reactions are bioorthogonal. Kinetic analysis of acyclic and cyclic nitrones with strained-trans-cyclooctene (s-TCO) shows fast reactivity and demonstrates the utility of this cycloaddition reaction for bioorthogonal labelling. Labelling of the bacterial peptidoglycan layer with unnatural d -amino acids tagged with nitrones and s-TCO-Alexa488 is demonstrated. These new findings expand the bioorthogonal toolbox, and allow TCO reagents to be used in bioorthogonal applications beyond tetrazine ligations for the first time and open up new avenues for bioorthogonal ligations with diverse nitrone reactants.     

Immobilization of polyvinyl alcohol-siloxane on the oxygen plasma-modified polyurethane-carbon nanotube composite matrix

Polyurethane-based scaffolds have been considered as a promising strategy for tissue regeneration. Herein, the polyurethane and carbon nanotubes electrospun scaffolds were modified by polyvinyl alcohol-3-glycidoxypropyl-trimethoxysilane after oxygen plasma treatment to improve physicochemical and in vitro properties for efficient bone reconstruction. Finally, the morphology of scaffolds, chemical characterization, surface roughness, bioactivity, hydrophilicity, cell attachment, cell viability, and alkaline phosphatase activity were investigated. According to microscopy results, bead free and smooth fibers were obtained using electrospinning while the degree of uniformity was reduced after the surface modification process. However, the modification process induced higher hydrophilicity and bioactivity to prepared scaffolds. In addition, the attachment and viability of the cells were improved as a function of surface modification. The expression of alkaline phosphatase especially in modified fibers confirmed the initial potential of scaffolds for bone tissue engineering applications and further studies. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48477.     

Facile construction of C3N4-TE@TiO2/UiO-66 with double Z-scheme structure as high performance photocatalyst for degradation of tetracycline

In the current research, a double Z-scheme photocatalyst C₃N₄-TE@TiO₂/UiO-66 (CNTU) is fabricated via a two-steps facile solvothermal method from Z-scheme C₃N₄-TE@TiO₂ (CNT). This double Z-scheme photocatalyst reveals greater performance for the removal of tetracycline (TC) than pristine C₃N₄-TE, TiO₂, UiO-66 (U66), and their binary compounds. The optimized composite 35C₃N₄-TE@TiO₂/35UiO-66 (35CNTU), exhibitions photocatalytic performance for antibiotic removal (TC) more than 5,4 and 2 times higher than that pure TiO₂, UiO-66, and C₃N₄-TE, respectively. The physical and chemical features of synthesized samples were described via FTIR, XRD, SEM-EDX, TEM, BET, UV–Vis DRS, and PL. The key parameters on photocatalytic performances of 35CNTU such as pH, the amount of catalyst, and the primary concentration of TC were clari?ed. The advancement of the photocatalytic process for 35CNTU is due to the increase in the surface area and structure of double Z-scheme in this compound, which growths the active sites of the reaction as well as better separation of the photo-induced electron and hole pairs. Furthermore, 35CNTU can be recycled with superior stability for 5 cycles. The photocatalytic removal proficiency of TC over 35CNTU under visible light achieves 96% in 40 min. The findings of this study could inspire various novel plans for fabricating practical double Z-scheme photocatalyst for great performance and extensive useful applications.     

Machine–part cell formation using a hybrid particle swarm optimization

Cell formation (CF) is a key step in group technology (GT). This combinatorial optimization problem is NP-complete. So, meta-heuristic algorithms have been extensively adopted to efficiently solve the CF problem. Particle swarm optimization (PSO) is a modern evolutionary computation technique based on a population mechanism. Since Kennedy and Eberhart invented the PSO, the challenge has been to employ the algorithm to different problem areas other than those that the inventors originally focused on. This paper investigates the first applications of this emerging novel optimization algorithm into the CF problem, and a newly developed PSO-based optimization algorithm for it is elaborated. Forming manufacturing cells lead to process each part family within a machine group with reduction intracellular travel of parts and setup time. A maximum number of machines in a cell and the maximum number of cells are imposed. Some published results in various problem sizes have been used as benchmarks to assess the proposed algorithm. Overall, the advantages of the proposed PSO are that it is rapidly converging towards an optimum, there are fewer parameters to adjust, it is simple to compute, it is easy to implement, it is free from the complex computation, and it is very efficient to use in CF with a wide variety of machine/part matrices.     

Spectral compression using subspace clustering

This article describes a subspace clustering strategy for the spectral compression of multispectral images. Unlike standard principal component analysis, this approach finds clusters in several different subspaces of different dimension. Consequently, instead of representing all spectra in a single low‐dimensional subspace of a fixed dimension, spectral data are assigned to multiple subspaces having a range of dimensions from one to eight. In other words, this strategy allows us to distribute spectra into different subspaces thereby obtaining the best fit for each. As a result, more resources can be allocated to those spectra that need many dimensions for accurate representation and fewer resources to those that can be modeled using fewer dimensions. For a given compression ratio, this trade off reduces the overall reconstruction error. In the case of compressing multispectral images, this initial compression method is followed by JPEG2000 compression in order to remove the spatial redundancy in the data as well. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 7–15, 2016     

Characterization of 3D microstructure,thermal conductivity,and heat flow of cement-based foam using imaging technique

This study presents the results of the 3D microstructure, thermal conductivity, and heat flow in cement-based foams and examines their changes with a range of densities. Images were captured using X-ray micro computed tomography (micro-CT) imaging technique on cement-based foam samples prepared with densities of 400, 600, and 800 kg/m³. These images were later simulated and quantified using 3D data visualization and analysis software. Based on the analysis, the pore volume of 11000 µm³ was determined across the three densities, leading to optimal results. However, distinct pore diameters of 15 µm for 800 kg/m³, and 20 µm for 600 and 400 kg/m³ were found to be optimum. Most of the pores were spherical, with only 10% appearing elongated or fractured. In addition, a difference of 15% was observed between the 2D and 3D porosity results. Moreover, a difference of 5% was noticed between the experimentally measured thermal conductivity and the numerically predicted value and this variation was constant across the three cast densities. The 3D model showed that heat flows through the cement paste solids and with an increase in porosity this flow reduces.     

A novel intelligent particle swarm optimization algorithm for solving cell formation problem

The formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing sector.