Palladium nanoparticles supported on chemically derived graphene: An efficient and reusable catalyst for the dehydrogenation of ammonia borane

Chemically derived graphene (CDG) was prepared by hydrazine hydrate reduction of graphene oxide and used as support for palladium nanoparticles (Pd NPs) generated ex situ with controllable particle size and dispersion. The Pd NPs supported on CDG were well characterized by using a combination of advance analytical techniques and employed as catalyst in the dehydrogenation and hydrolysis of ammonia borane (AB) in organic solvents and aqueous solutions, respectively. Monodisperse Pd NPs of 4.5 nm were prepared from the reduction of palladium(II) acetylacetonate by tert-butylamine borane in the presence of oleylamine. They were readily impregnated on CDG which has BET surface area of 500 m² g⁻¹. Pd NPs retain their particle size dispersion and stability when supported on chemically derived graphene. The resulting materials are highly active and stable catalyst for the dehydrogenation and hydrolysis of AB. In addition to their high activity and stability, these Pd NPs are also reusable catalyst in both dehydrogenation and hydrolysis of AB preserving 85% and 95% of initial activity after 5th and 10th runs, respectively.     

Microbiological and physicochemical properties of farm bulk tank milk and antimicrobial resistance of its dominant bacteria

This study determined the antibiotic resistance of the dominant bacteria in the 85 farm BTMs according to the guidelines recommended by the epidemiological cutoff values in the EUCAST. In addition, some physicochemical and microbiological properties of farm BTMs were investigated. The milk samples were divided into two groups according to their SCC values. The milk samples with higher SCC than 400,000 cells mL⁻¹ were further examined bacteriologically, and the antibiotic resistance of isolates was determined. The average TAMB value was 6.34 log CFU/mL in farm BTM. It was found that high-SCC values did not affect other physicochemical properties of BTM samples, such as fat, protein and total solids, except for lactose content. Seventy-two strains were isolated from 45 bulk milk samples. The most prevalent bacteria were Enterococcus spp. (23.61%). The other isolates were Citrobacter spp. (12.5%), Staphylococcus spp. (12.51%), Serratia spp. (11.12%), Klebsiella spp. (9.72%), Bacillus spp. (9.72%), and Enterobacter spp. (8.33%). In antibiotic resistance analysis, 52.6% of Enterobacterales isolates showed cefoxitin resistance, and nine Enterobacterales isolates were determined as the presumptive ESBL producers. None of them was confirmed as ESBL producers. Moreover, MDR was detected in 83.3% of Enterobacter spp. isolates and all Bacillus spp. isolates. The over and inappropriate use of antibiotics in mastitis treatment may cause antibiotic-resistant microorganisms in milk. It was found that 52.7% of the isolated bacteria were MDR, which could pose a risk to public health and food safety, with the consumer's increasing interest in consuming raw milk.     

A novel image Denoising approach using super resolution densely connected convolutional networks

Multimedia Tools and Applications - Image distortion effects, called noise, may occur due to various reasons such as image acquisition, transfer, and duplication. Image denoising is a preliminary...     

Solving continuous optimization problems using the ımproved Jaya algorithm (IJaya)

Jaya algorithm is one of the heuristic algorithms developed in recent years. The most important difference from other heuristic algorithms is that it updates its position according to its best and worst position. In addition to its simplicity, there is no algorithm-specific parameter. Because of these advantages, it has been preferred by researchers for problem-solving in the literature. In this study, the random walk phase of the original Jaya algorithm is developed and the Improved Jaya Algorithm (IJaya) is proposed. IJaya has been tested for success in eighteen classic benchmark test functions. Although the performance of the original Jaya algorithm has been tested at low dimensions in the literature, its success in large sizes has not been tested. In this study, IJaya's success in 10, 20, 30, 100, 500, and 1000 dimensions was examined. Also, the success of IJaya was tested in different population sizes. It has been proven that IJaya's performance has increased with the tests performed. Test results show that IJaya displays good performance and can be used as an alternative method for constrained optimization. In addition, three different engineering design problems were tested in different population sizes to demonstrate the achievements of Jaya and IJaya. According to the results, IJaya can be used as an optimization algorithm in the literature for continuous optimization and large-scale optimization problems.

     

Porosity–Property Relationships of Plasma‐Sprayed Gd2Zr2O7/YSZ Thermal Barrier Coatings

During the past decade, gadolinium zirconate (Gd₂Zr₂O₇, GZO) has attracted interest as an alternative material to partially yttria‐stabilized zirconia (YSZ) for thermal barrier coatings (TBCs). Despite the well‐known benefits of GZO, such as lower thermal conductivity and superior temperature capability compared to YSZ, processing of GZO via atmospheric plasma spraying (APS) still remains a challenge. Here, we report on APS experiments which were performed to investigate the influence of processing on GZO microstructure and lifetime of GZO/YSZ double‐layer TBCs. Different microstructures of GZO were produced and characterized in terms of porosity, stoichiometry, Young′s modulus, and their effects on the lifetime of YSZ/GZO double‐layer TBCs were discussed. Particle diagnostics were utilized for the optimization of the process parameters with respect to different microstructures of GZO and stoichiometry. It was found that both cumulative porosity of GZO and pore size distribution, which alter the Young′s modulus significantly, govern the lifetime of double layers. In addition, it was shown that the deviation in GZO stoichiometry due to gadolinia evaporation in the investigated range does not display any critical effect on lifetime.     

A high-capacity reversible data hiding method: HCRHide

Reversible data hiding methods have drawn considerable attention in the last decade, which allow full recovery of the original image used for embedding secret data as well as avert the third parties who should not realise the existence of hidden data. In this paper, we propose a high-capacity reversible data hiding method called HCRHide based on the Neighbour Mean Interpolation (NMI) method and the R-weighted Coding Method (RCM). Throughout a joint imperceptibility and data hiding capacity evaluation, results have approved high performance of the proposed method over the existing reversible data hiding methods.     

Synthesis of β-myrcene-based macroporous nanocomposite foams: Altering the morphological and mechanical properties by using organo-modified nanoclay

Organo-modified nanoclay incorporated high internal phase emulsions (HIPEs) were successfully used for the preparation of macroporous nanocomposite foams. Due to the aim of obtaining mechanically improved foams, HIPEs were prepared by using a monomer mixture composed of β-myrcene and ethylene glycol dimethacrylate. Accordingly, two groups of macroporous nanocomposite foams were synthesized depending on the nanoclay type. The morphological analysis demonstrated that the pore openness of the resulting nanocomposites were significantly improved due to the decrease in the average cavity size and increase in the interconnected pore size. In terms of mechanical properties, it was found that filling 1 wt% of nanoclay which is surface modified by hydrogenated tallow lead to a 33% of increment in the compression modulus, as compared to the neat foam. However, loading 5 wt% of nanoclay having octadecylamine and aminopropyltriethoxysilane surface groups caused only 11% of increment in the compression modulus, as compared to the neat foam.