Optimization of mechanical properties of in situ polymerized poly(methyl methacrylate)/alumina nanoparticles nanocomposites using Taguchi approach

Polymer Bulletin - Alumina nanoparticles are among important metal oxides with specific properties but chemically incompatible with an organic matrix such as poly(methyl methacrylate) (PMMA). In...     

Real-time separation of aerosolized Staphylococcus epidermidis and polystyrene latex particles with similar size distributions

For rapid and effective detection of airborne microorganisms, it is preferable to remove dust particles during the air sampling process because they can reduce the detection accuracy of measurements. In this study, a methodology of real-time separation ofaerosolized Staphylococcus epidermidis (S. epidermidis) andpolystyrene latex (PSL) particles of similar size was investigated. These two species represent biological and non-biological particles, respectively. Due to their different relative permittivities, they grasp different numbers of air ions under corona discharge. After these charged particles enter a mobility analyzer with airflow, in which an electric field is applied perpendicular to the airflow, the S. epidermidis and PSL particles separate, due to the difference in their electric mobilities, and exit through two different outlets. Purities and recoveries for S. epidermidis and PSLat their respective outlets were determined with measurements of aerosol number concentrations and ATP bioluminescence intensities at the inlet and two outlets. The results were that purities for PSL and S. epidermidis were 70% and 80%, respectively. This methodology provides a rapid and simple way to increase the detection accuracy of bacterial agents in air.

Copyright © 2017 American Association for Aerosol Research     

Effect of chemical solvents on the technological characteristics of hemp fibre/polypropylene composites

The effects of different chemical solvents on the technological properties of hemp fibre-reinforced polypropylene composites were evaluated in this experimental work. Composite profiles consisting of hemp fibre and polypropylene at 50% weight ratios, with 2% of coupling agent were fabricated using melt compounding followed by injection moulding. The composite specimens were then immersed in CH₃COOH, HNO₃, H₂SO₄, NaOH, NH₃, and C₆H₅NH₂ for different time intervals. Then, the weight loss and mechanical strength of samples were measured. Results indicated that the chemical reagents had significant effect on the weight loss of the composites. The weight loss ratio of the control samples was lower than that of those samples exposure to the chemical solvents. The tensile strength and modulus, and impact strength of composite specimens decreased after exposure to the chemical solvents. The highest mechanical reduction was observed in the case of NaOH. SEM micrographs showed that the extent of degradation increased in composites when they are exposed to chemical solvents.     

Adsorptive separation of meta-xylene from C8 aromatics

Industrial adsorptive separation process for liquids is most successful when the involved species have very close boiling points, making distillation expensive or are thermally sensitive at convenient distillation temperatures. The adsorption process was studied for separating meta-xylene from a feed mixture containing all C₈ aromatics on binder-free X and Y zeolites in the liquid phase. Zeolitic adsorbents with different SiO₂/Al₂O₃ were synthesized by the hydrothermal method and ion-exchanged with alkaline metal cations like lithium, sodium and potassium. The adsorption process was carried out in a breakthrough system at temperature of 110–160 °C and pressure of 6–8 atm. The influence of adsorbent moisture content on the separation process was studied. The optimization of adsorption process was also investigated by the changing operation conditions. The isotherms for each isomer of C₈ aromatics and the desorbent possess the adsorption characteristics of Langmuir type. The selectivity factor of meta-xylene and the saturation adsorption capacities of adsorbates were determined. It was observed that the selectivity of meta-xylene increased by sodium ion-exchanging of cationic sites in Y zeolite and the selectivity factor of meta-xylene/para-xylene, meta-xylene/ortho-xylene and meta-xylene/ethylbenzene in the optimum conditions was determined to be 2.62, 2.83 and 5.93, respectively.     

Nucleation and growth of Pd nanoparticles during electrocrystallization on pencil graphite

The aim of present study was to investigate the mechanism of palladium electrodeposition on a pencil graphite surface from an aqueous solution containing 1 mM PdCl₂ and 500 mM H₂SO₄. Cyclic voltammetry revealed that the electrodeposition process proceeds via an initial underpotential deposition (UPD) and subsequently, it follows by the overpotential deposition (OPD) under diffusion control. Current transients showed a complex deposition trend, i.e. the electrodeposition of Pd includes at least three stages: adsorption on the surface, and two types of nucleation courses; two-dimensional (2D) and three-dimensional (3D) which limited by the lattice incorporation of Pd adatoms and the diffusion of Pd ions, respectively. Therefore the electrocrystallization of Pd on pencil graphite conforms to the Stranski–Krastanov growth mechanism. Furthermore, theoretical models were applied to evaluate each of above mentioned stages for different electrode potentials and to estimate the electrochemical parameters of Pd electrodeposition. The micrographs of electrodeposited Pd at relatively high frequency of the nucleation rate, illustrated a well dispersed Pd nanoparticles with a relatively narrow size distribution.     

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.     

Sn-adopted fullerene $$(\hbox {C}_{60})$$ nanocage as acceptable catalyst for silicon monoxide oxidation

In recent years, the discovery of metal catalysts for the oxidation of silicon monoxide (SiO) has become extremely important. In first step, the Sn adoption of fullerene (\(\hbox {C}_{60})\) was investigated and then activation of surface of \(\hbox {Sn-C}_{60}\) via \(\hbox {O}_{2}\) molecule was examined. In second step, the SiO oxidation on surface of \(\hbox {Sn-C}_{60}\) via Langmuir Hinshelwood (LH) and Eley Rideal (ER) mechanisms was investigated. Results show that \(\hbox {O}_{2}\hbox {-Sn-C}_{60}\) can oxidize the SiO molecule via \(\hbox {Sn-C}_{60}\hbox {-O-O}^{*} + \hbox {SiO}\rightarrow \hbox {Sn-C}_{60}\hbox {-O-O}^{*}\hbox {-SiO} \rightarrow \hbox {Sn-C}_{60}\hbox {-O}^{*} + \hbox {SiO}_{2}\) and \(\hbox {Sn-C}_{60}\hbox {-O}^{*} + \hbox {SiO}\rightarrow \hbox {Sn-C}_{60} + \hbox {SiO}_{2}\) reactions. Results show that SiO oxidation via the LH mechanism has lower energy barrier than ER mechanism. Finally, \(\hbox {Sn-C}_{60}\) is an acceptable catalyst with high performance for SiO oxidation in normal temperature.     

Application of adaptive neuro-fuzzy inference system in prediction of hydrate formation temperature

A look at the number of publications in the last decades on the prediction of hydrate forming conditions for various gas mixtures obviously indicates the importance of this field from scientific and industrial viewpoints. Yet, the correlations presented in the literature are not accurate enough and also some of these correlations are presented mainly in graphical form, thus making it difficult to use them within general computer packages for simulation and design. In this study adaptive neuro-fuzzy inference systems were used to produce a nonlinear model to predict the hydrate formation temperature. The model was trained using 303 input–output patterns collected from reliable sources. The adaptive neuro-fuzzy inference system model enables the user to accurately predict hydrate formation conditions under varying system conditions (i.e., temperature, pressure, and gas composition), without having to do costly experimental measurements. Also, statistical error analysis is used to evaluate the performance and the accuracy of the adaptive neuro-fuzzy inference system for estimating natural gas hydrate formation to guide designers and operators in selecting the best system conditions for their particular applications. It is shown that the results of predictions are in acceptable agreement with experimental data indicating the capability of the adaptive neuro-fuzzy inference system for predicting hydrate formation conditions of natural gases.