The effect of colloidal solution of molybdenum nanoparticles on the microbial composition in rhizosphere of Cicer arietinum L.

The use of colloidal solutions of metals as micronutrients enhances plant resistance to unfavorable environmental conditions and ensures high yields of food crops due to the active penetration of nanoelements into the plant cells.Microbiological examination of rhizosphere soil have revealed that combined use of colloidal solution of nanoparticles of molybdenum (CSNM, 8 mg/l), and microbial preparation for pre-sowing inoculation of chickpea seeds stimulates the development of ‘agronomically valuable’ microflora. It was shown that combined seed treatment with colloidal solution of Mo nanoparticles with microbial preparation have stimulated nodule formation per plant by four times compared to controls. Single treatment with CSNM increased the number of nodules by two times, while the treatment of microbial preparation have not significantly affected the number of nodules per plant.

PACS

Colloids, 82.70.Dd; Ecology, 87.23.-n     

Mathematical Modeling and Microbiological Verification of Ohmic Heating of a Multicomponent Mixture of Particles in a Continuous Flow Ohmic Heater System with Electric Field Parallel to Flow

To accomplish continuous flow ohmic heating of a low‐acid food product, sufficient heat treatment needs to be delivered to the slowest‐heating particle at the outlet of the holding section. This research was aimed at developing mathematical models for sterilization of a multicomponent food in a pilot‐scale ohmic heater with electric‐field‐oriented parallel to the flow and validating microbial inactivation by inoculated particle methods. The model involved 2 sets of simulations, one for determination of fluid temperatures, and a second for evaluating the worst‐case scenario. A residence time distribution study was conducted using radio frequency identification methodology to determine the residence time of the fastest‐moving particle from a sample of at least 300 particles. Thermal verification of the mathematical model showed good agreement between calculated and experimental fluid temperatures (P > 0.05) at heater and holding tube exits, with a maximum error of 0.6 °C. To achieve a specified target lethal effect at the cold spot of the slowest‐heating particle, the length of holding tube required was predicted to be 22 m for a 139.6 °C process temperature with volumetric flow rate of 1.0 × 10^?4 m³/s and 0.05 m in diameter. To verify the model, a microbiological validation test was conducted using at least 299 chicken‐alginate particles inoculated with Clostridium sporogenes spores per run. The inoculated pack study indicated the absence of viable microorganisms at the target treatment and its presence for a subtarget treatment, thereby verifying model predictions.     

Mechanical behavior and failure mechanisms of boron carbide based three-layered laminates with weak interfaces

The effect of weak interfaces on failure mechanisms of a three-layered composite was studied. Three-layered B₄C/B₄C–C_nanofibers laminates have been produced using a hot pressing technique. The laminates were designed with thick (∼2.6 mm) outer layers of B₄C and a thin (∼90 μm) center layer of B₄C–70 wt.% C_nanofibers. Based on the difference in coefficients of thermal expansion and Young's moduli of the pure B₄C and B₄C–70 wt.% C_nanofibers layers, it was estimated that low tensile thermal residual stress with a magnitude of 11.3 ± 2.5 MPa was developed in the thick B₄C outer layers, and compressive residual stress with a magnitude of 455.7 ± 5 MPa was developed in the thin central B₄C–70 wt.% C_nanofibers layer. The apparent fracture toughness of laminates was measured and based on the estimated fracture toughness values, a threshold stress was calculated.     

Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?

Sales prediction is an essential part of stock planning for the wholesales and retail business. It is a complex task because of the large number of factors affecting the demand. Designing an intelligent predictor that would beat a simple moving average baseline across a number of products appears to be a non-trivial task. We present an intelligent two level sales prediction approach that switches the predictors depending on the properties of the historical sales. First, we learn how to categorize the sales time series into ‘predictable’ and ‘random’ based on structural, shape and relational features related to the products and the environment using meta learning approach. We introduce a set of novel meta features to capture behavior, shape and relational properties of the sales time series. Next, for the products identified as ‘predictable’ we apply an intelligent base predictor, while for ‘random’ we use a moving average. Using the real data from a food wholesales company we show how the prediction accuracy can be improved using this strategy, as compared to the baseline predictor as well as an ensemble of predictors. In our study we also show that by applying an intelligent predictor for the most ‘predictable’ products we can control the risk of performing worse than the baseline.     

Simulation and optimization of the ohmic processing of highly viscous food product in chambers with sidewise parallel electrodes

Three dimensional numerical simulations of ohmic heating (OH) of highly viscous chicken chow mein sauce in typical and modified pilot scale ohmic heating chambers with sidewise parallel electrodes arrangement were undertaken. The fully coupled momentum, electrostatic and energy transfer partial differential equations were solved using commercial finite element modeling (FEM) software. The residence time (RT) of sauce in the OH treatment chambers was studied by solving the convection and diffusion equation for a tracer species injected in the system. The key parameters affecting the process, including electrical conductivity, σ, thermal conductivity, k, specific heat capacitance, C_p, density, ρ and viscosity, η, were measured experimentally. For the model validation, the predicted current delivered by each generator and temperatures in selected locations were compared with those obtained from trials performed under the same conditions. A good agreement was observed between experimental and simulated data with the coefficients of determination R² = 0.987.     

Microcracking in electron beam deposited scandia-stabilised zirconia electrolyte

It is the aim of the present work to address some of the aspects of microcracking in electron beam deposited scandia-stabilised zirconia electrolyte applied for solid oxide fuel cells (SOFC) where a thin electrolyte layer is deposited on a relatively thick anode substrate. A model of microcracking for the electrolyte material is proposed which takes into account the statistical distribution of grain sizes, the stress redistribution due to failure of individual structural elements as well as the local criterion of grain fracture. The combination of electron microscopy research with model calculations permits both the specific energy of new surface creation in the electrolyte and critical parameters of the microcracking process to be determined. The annealing-induced electrolyte microcracking discussed in this work corresponds to localised microcracking, where each next structural element fails mainly at an existing microcrack tip. The features of localised microcracking in electron beam deposited scandia-stabilised zirconia electrolyte are analysed.     

Mössbauer spectroscopic investigation of iron species in coal

A series of Herrin No. 6 coal and three coal-derived samples have been examined by Mössbauer spectroscopy. It is established that Mössbauer spectroscopy can be used to identify multiple iron species in a whole coal or an autoclaved char sample without the need to concentrate the minerals to enhance resolution. Our results indicate that there may be an association between the pyrite in raw coal and the coal matrix. This association appears to be broken down when the coal is heated to temperatures as low as 175 °C. It is also apparent that the iron sulphide present in the whole coal is converted to pyrite at these low temperatures. For our samples, the total quantity of iron species in different coal lithotypes is about the same, but they differ in their distributions. The fusain has the least amount of Fe²⁺ species when compared to the vitrain or whole-coal sample used. At least two types of nonstoichiometric pyrrhotite are produced in the heat-treated samples. One of these pyrrhotites is unstable and contains dissolved sulphur which is apparently liberated as the temperature is increased.