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Background: Use of air purifier masks is one of the ways to reduce exposure to harmful respiratory pollutants in the workplace. The present research was conducted to evaluate the efficiency of respiratory masks used by refractory workers in controlling refractory ceramic fibers (RCF) and particles in a steel industry. Methods: In this study, three types of masks, namely FFP3 (APOLO), FFP2 (3MAX), and 3M elastomeric half-face mask with P100 filter against particle pollutants and FFP3 (APOLO) masks against fibrous contaminants were evaluated. To investigate penetration percentage and efficiency of the mask, the concentrations of particles were measured outside (Cout) and inside (Cin) the masks. The number of samples for each mask was 20. Results: The result showed that mean penetration of total particles in FFP3, FFP2, and 3M elastomeric half-face mask with P100 filter is 10.92, 9.4, and 4.37%, respectively. On the other hand, the mean penetration of respirable particles in FFP3, FFP2, and 3M elastomeric half-face mask with P100 filter was 40.76, 14.26, and 2.07%, respectively. The evaluation of the FFP3 (APOLO) mask against fibrous contaminants showed that this mask does not have a good efficiency against fibrous contaminants and provides a penetration of 15.1%. Conclusions: The faceseal leakage due to inappropriate fitting of the mask on the face and the high surface resistance the mask against airflow can be a major cause for poor efficiency of the masks.

In the present study, investigations are focused on microstructural evolution and the resulting hardness during continuous cooling transformation (CCT) in a commercial vanadium microalloyed steel (30MSV6). Furthermore, the effects of cooling rate and austenite grain size (AGS) on CCT behavior of the steel have been studied by employing high-resolution dilatometry. Quantitative metallography accompanied with scanning electron microscopy (SEM) has efficiently confirmed the dilatometric measurements of transformation kinetics and austenite decomposition products. A semi-empirical model has been proposed for prediction of microstructural development during austenite decomposition of the steel and the resultant hardness. The model consists of 8 sub-models including ferrite transformation start temperature, ferrite growth, pearlite start temperature, pearlite growth, bainite start temperature, bainite growth, martensite start temperature and hardness. The transformed fractions of ferrite, pearlite and bainite have been described using semi-empirical Johnson–Mehl–Avrami–Kolmogorov (JMAK) approach in combination with Scheil's equation of additivity. The JMAK rate parameter for bainite has been formulated using a diffusion-controlled model. Predictions of the proposed model were found to be in close agreement with the experimental measurements. Keywords: Microalloyed steel, Austenite decomposition, Modeling, CCT, Hardness

Intermetallics of Fe and Ni, which are known as permalloy, are under attention due to their excellent magnetic performance. Besides, mechanical properties of the materials can be improved by decreasing crystallite size of FeNi intermetallics or by reinforcing them with hard secondary phases such as Al2O3. In this study, FeNi–Al2O3 nanocomposite powders with three different compositions were successfully synthesized through mechanical alloying of Fe2O3, Ni, and Al powders mixture. Characterization of the samples was accomplished by scanning electron microscopy, transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy and X-ray diffraction. Effects of various parameters such as chemical composition of received materials, milling time, and annealing on the phase evolution, morphology, and microhardness of samples were investigated. It was found that by the addition of Fe as diluent, the required milling time for formation of FeNi intermetallic increased. By increasing milling time, mean crystallite size of FeNi decreased and reach to about 28 nm for FeNi-30 wt% Al2O3 nanocomposite powder sample. TEM observations also showed that in situ-formed Al2O3 particles, with particle size of about 65 nm, were uniformly dispersed within FeNi matrix.

FeNi–Al2O3 composites are under attention for many applications because of their good magnetic properties, high strength and fracture toughness and excellent corrosion and wear resistance. In the current study, FeNi–Al2O3 nanocomposite powders were synthesized through mechanical alloying of Fe2O3, Al and Ni powder mixture. Bulk samples were prepared under subsequent cold pressing and sintering processes. Study of the phase evolution using SEM, EDS and XRD showed that FeNi matrix nanocomposites reinforced with 10, 15 and 30 wt.% of Al2O3 were fabricated in 300, 240 and 180 min of milling, respectively. Mean crystallite size of FeNi phase at these samples after 720 min milling was calculated about 28, 35 and 22 nm, respectively. Effect of various parameters such as Al2O3 weight percentage, uniaxially cold pressure, sintering temperature and sintering time on the fabrication (densification and consolidation) and mechanical properties (hardness, compression strength and specific wear rate) of these nanocomposites was investigated. Because of numerous experiments needed, design of experiment based on Taguchi method was applied. Considering optimized condition offered by analysis of variance (ANOVA), maximum density (5.314 g/cm3), maximum hardness (786 HV), maximum compression strength (97.76 MPa) and minimum specific wear rate (0.0251 mm3/Nm) of FeNi–Al2O3 nanocomposites were obtained.

Predictive models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were successfully developed to predict yield strength and ultimate tensile strength of warm compacted 0.85 wt.% molybdenum prealloy samples. To construct these models, 48 different experimental data were gathered from the literature. A portion of the data set was randomly chosen to train both ANN with back propagation (BP) learning algorithm and ANFIS model with Gaussian membership function and the rest was implemented to verify the performance of the trained network against the unseen data. The generalization capability of the networks was also evaluated by applying new input data within the domain covered by the training pattern. To compare the obtained results, coefficient of determination (R2), root mean squared error (RMSE) and average absolute error (AAE) indexes were chosen and calculated for both of the models. The results showed that artificial neural network and adaptive neuro-fuzzy system were both potentially strong for prediction of the mechanical properties of warm compacted 0.85 wt.% molybdenum prealloy; however, the proposed ANFIS showed better performance than the ANN model. Also, the ANFIS model was subjected to a sensitivity analysis to find the significant inputs affecting mechanical properties of the samples.

In this Study, FeNi-Al2O3 nanocomposites with three different compositions were successfully synthesized through mechanical alloying of Fe2O3, Ni and Al powders mixture. Characterization of the products was accomplished by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX) and X-ray diffraction (XRD). The effect of various parameters such as chemical composition of starting materials, milling time and annealing on the phase evolution, morphology and microhardness of samples was investigated. It was found that FeNi matrix nanocomposites reinforced with 10, 15 and 30wt.% of Al2O3 were fabricated in 300, 240 and 180 min of milling, respectively. The crystallite size of the intermetallic FeNi phase and particle size of Al2O3 in the 720 min milled FeNi-30wt.%Al2O3 nanocomposite sample were calculated 28nm and 5.15 µm, respectively. Microhardness results also showed the same sample had the maximum hardness value of 790 HV.

Aflatoxins are toxic fungal metabolites enable to contaminate a wide range of natural substrates. This contamination can be host-specific for different plant species. In this study, the ability of a toxigenic Aspergillus parasiticus to produce various aflatoxins on major Iranian cereals was evaluated with special focus on plant susceptibility to toxin production at cultivar level.Aspergillus parasiticus cultured on major Iranian cereal cultivars and some selected spices was incubated in shaking condition at 28 masculineC for 6 days. The concentration of aflatoxins B1 and total (B1, B2, G1 and G2) was measured by thin layer chromatography.The amounts of aflatoxin B1 produced on maize, wheat and rice cultivars were in the ranges of 1.0-33.9, 41.9-193.7, and 39.1-82.3 microg/g fungal weight, respectively. Interestingly, genetically modified Bacillus thuringiensis rice (GM rice) of Tarom Molaii cultivar examined for the first time in this study showed less susceptibility to aflatoxin production in comparison with its normal counterpart (P less than 0.05). The mean of aflatoxin production on maize cultivars was less than both wheat and rice cultivars that indicates considerable resistance of maize to aflatoxin compared with two other cereals. Unlike to Cuminum cyminum, both Helianthus annuus and Carum carvi seeds were highly resistant to aflatoxin production.These results indicate that inter- and intra-species differences exist in susceptibility of the major Iranian cereals as well as spices tested to A. parasiticus growth and aflatoxin production. Further studies are recommended to determine resistance markers of selected cultivars of Iranian cereals.