Experimental Temperature Research Articles

Vacuum thermal decomposition behavior of Ag2Te

Silver and tellurium are crucial valuable metallic elements in anode slimes. During pyrometallurgical processing, tellurium exists in the alloy as the stable compound Ag2Te. The current process of separating tellurium from silver-tellurium compounds in crude silver by oxidizing tellurium into slag leads to undesirable loss of silver, low recovery rates of tellurium, and considerable slag. An environmentally friendly and efficient vacuum thermal decomposition process of Ag2Te was proposed, and the effects of temperature, pressure, and holding time on the decomposition of Ag2Te were investigated. The Experimental temperature was thermodynamically calculated to be in the range of 1473–1673 K and pressure in the range of 1–100 Pa. As the temperature increases, especially above 1573 K, the silver volatiles increase significantly, and silver and tellurium vapors react strongly to form stable silver-tellurium compounds. Investigating the volatilization and condensation mechanisms of silver and tellurium highlights the importance of inhibiting silver volatilization to achieve efficient separation of silver and tellurium. Volatiles containing 98.85% tellurium mainly as tellurium monomer, and a remained melt consisting of 95.56% silver primarily as silver monomer were produced at 1523 K, 50 Pa, and an 8-h holding time.

Adaptive Force Field Parameter Optimization for Expanding Reaction Simulations within Wide-Ranged Temperature.

Large-scale and long-term simulation of chemical reactions are key research topics in computational chemistry. However, there are still difficulties in simulating high-temperature reactions, such as polymer thermal decomposition. Herein, we introduce an adaptive potential parameter optimization framework designed to automatically fine-tune parameters, and the application of it to optimize ReaxFF parameters enhances the accuracy of chemical reaction simulations conducted at experimental temperatures. To achieve this, we leverage the power of Random Forests and interpretable machine learning techniques that enable the identification and selection of parameters that exert a substantial influence on the target attribute. By training deep neural network (NN) models, we established optimized parameter associations with reference properties. We train deep neural network (NN) models to establish the relationship between the optimized parameters and reference properties. We employ a Genetic Algorithm (GA) to utilize the surrogate NN model and the quantum mechanical targets to speed up the search for the optimal parameters. Our simulation results of resin pyrolysis show that the adaptive optimized ReaxFF can predict the peak temperature more accurately and obtain reasonable product composition under conditions that more closely resemble experimental scenarios. This work facilitates advances in force field parameter optimization for more accurate and universal reaction simulations.

Fitness difference between two synonymous mutations of Phytophthora infestansATP6 gene

BackgroundSequence variation produced by mutation provides the ultimate source of natural selection for species adaptation. Unlike nonsynonymous mutation, synonymous mutations are generally considered to be selectively neutral but accumulating evidence suggests they also contribute to species adaptation by regulating the flow of genetic information and the development of functional traits. In this study, we analysed sequence characteristics of ATP6, a housekeeping gene from 139 Phytophthora infestans isolates, and compared the fitness components including metabolic rate, temperature sensitivity, aggressiveness, and fungicide tolerance among synonymous mutations.ResultsWe found that the housekeeping gene exhibited low genetic variation and was represented by two major synonymous mutants at similar frequency (0.496 and 0.468, respectively). The two synonymous mutants were generated by a single nucleotide substitution but differed significantly in fitness as well as temperature-mediated spatial distribution and expression. The synonymous mutant ending in AT was more common in cold regions and was more expressed at lower experimental temperature than the synonymous mutant ending in GC and vice versa.ConclusionOur results are consistent with the argument that synonymous mutations can modulate the adaptive evolution of species including pathogens and have important implications for sustainable disease management, especially under climate change.

Computational analysis of thermal cycling phenomena during multilayer laser powder bed fusion process for Ti6Al4V

During laser powder bed fusion (LPBF) process, single tracks overlap to form layers and layers merge to form the complete 3D object. Because of this, a recurring temperature rise and fall happens, which is called thermal cycling phenomenon. It affects the microstructure and mechanical properties of the manufactured component via multiple reheating (and remelting) cycles, resulting in an intrinsic aging type heat treatment process that invokes an additional anisotropy along the build direction. To date, this phenomenon has been rarely studied due to the computational resource constraints and difficulties in experimental temperature measurement. Hence, in this study, a computationally efficient thermal model has been developed and applied to study the multilayer LPBF process for Ti6Al4V. The model was validated against the single track single layer experimental data and then used to simulate multilayer LPBF till a height of 3 layers to study the temperature field evolution and thermal cycling phenomenon. The average temperature of the computational domain is plotted against time to show the origin of intrinsic heat treatment. It is observed that while with each next layer the temperature rises, the maximum temperature value remains constant due to material evaporation taking place. The results suggest that thermal buildup also happens in the process as the average temperature of the computational domain as well as the melt pool volume increases with each layer.

Qualitative ab initio theory of magnetic and atomic ordering in FeNi

Magnetic and atomic ordering in equiatomic FeNi alloy is studied by different techniques and methods based on density functional theory in order to clarify the main driving forces and their interplay behind these transitions and possibility of their accurate description within standard density functional theory calculations. The Curie temperature is obtained in Monte Carlo simulations using magnetic exchange interactions obtained by applying the magnetic force theorem within multiple scattering theory for different magnetic and atomic configurational states, including account for the thermal atomic displacements and exchange-correlation potential. The calculations show a very strong sensitivity of the results upon exchange-correlation potential, atomic order, and thermal atomic displacements. The calculated Curie temperature of a completely random alloy with the account of thermal lattice displacement is at least about 200 K below the known experimental data (780–800 K) depending on the above mentioned factors. The atomic order-disorder transition temperature is determined from effective chemical interactions, which apart from the chemical contribution (on the ideal fcc lattice) include contributions from lattice thermal vibrations and local lattice relaxations. The effective chemical interactions are strongly affected by the magnetic state, so the order-disorder transition temperature changes between 1000 and 140 K in the fully ordered ferromagnetic and paramagnetic states, respectively. For the reduced magnetization 0.7 (close to the experimental order-disorder transition temperature at 600 K), the order-disorder transition temperature varies between 550 and 700 K depending mostly on the exchange-correlation potential. The latter effect is the uncertainty in the choice of the exchange-correlation approximation in density functional theory calculations. Published by the American Physical Society 2024

Polyethylene waste co-processing in fluid catalytic cracking plants

Plastics pollution is an overwhelming environmental problem that must be solved as soon as possible. Refining processes such as the Fluidized Catalytic Cracking (FCC) process with a global capacity of 14 million barrels per day, may help to solve it in the short term, as many scientists have already pointed out. Just by co-processing 5 wt % polyethylene waste in those units, 37 million tons per year of polyethylene could be eliminated from landfills and transformed into valuable fuels. However, refiners must be completely sure that processing polyethylene in their FCC plants will not cause any deleterious effects. That is the purpose of this paper.Low density polyethylene waste was transformed into valuable hydrocarbons by co-processing in proportions of 5 and 10 wt % with heavy gasoil in an FCC pilot plant which operates as industrial FCC plants do. Polyethylene was completely converted mainly into naphtha and liquified petroleum gas; at 510 °C polyethylene was converted into naphtha (46 %), LPG (20 %), light cyclic oil (9 %), heavy cyclic oil (15 %), coke (6 %) and dry gas (4 %); at 530 °C, the order and proportions changed significantly, naphtha (43 %), LPG (35 %), heavy cyclic oil (0 %), light cyclic oil (2 %), coke (8 %) and dry gas (12 %); LPG olefinicity and naphtha research octane number increased slightly. No catalyst circulation problems nor clogging or plugging were observed. However, at the highest experimental reaction temperature (530 °C), dry gas yield increased to more than 4 wt %, this could be a problem for most of industrial plants since it may overload the wet gas compressor.

Phenotypic plasticity and carryover effects in an ecologically important bivalve in response to changing environments

Phenotypic plasticity can improve an organism’s fitness when exposed to novel environmental conditions or stress associated with climate change. Our study analyzed spatiotemporal differences in phenotypic plasticity and offspring performance in Olympia oysters Ostrea lurida. This species is an ecosystem engineer and is of great interest for commercial and restoration aquaculture. We used a multidisciplinary approach to examine acute and long-term physiological differences in O. lurida in response to in situ oceanographic conditions in a dynamic inland sea. We outplanted oysters to different areas in Puget Sound, Washington, affixing cages to anchor lines of oceanographic monitoring buoys. This allowed us to couple high-resolution oceanographic data with organism’s phenotypic response. To assess spatiotemporal differences in oyster physiological performance, we collected oysters after six-months and one year of acclimatization at four field sites. During each collection period we evaluated changes in shell properties, diet, metabolism, and reproduction. Adult growth, δ13C and δ15N isotopic signatures, and gametogenesis were affected by both seasonal and environmental conditions. In the winter, oysters from all sites had higher respiration rates when exposed to acute thermal stress, and lower respiration response to acute pH stress. Lipid content, sex ratio and shell strength were unchanged across locations. Offspring growth rates between sites at experimental temperature 20°C closely reflected parental growth rate patterns. Offspring survival was not correlated with growth rates suggesting different energetic trade-offs in oyster offspring. The metabolic response (respiration) of larvae reached its highest point at 20°C but sharply decreased at 25°C. This indicates that larvae are more sensitive to temperature stress, as adults did not exhibit a reduction in metabolic response at 25°C. By deploying genetically similar oysters into distinct environments and employing a wide range of physiological methodologies to examine performance and fitness, our results indicate that Olympia oysters exhibit a high degree of phenotypic plasticity and show evidence of parental carryover.

Development of a lead-free colossal dielectric material barium bismuth ferrous oxide for electronic devices

Ceramic materials are promising for the ever-increasing demand in next-generation electronic devices. In this regard, we report the development and characterizations (structural, microstructural surface, dielectric, transport, impedance, resistive and current-voltage properties) of a novel complex material Ba3Bi2Fe2O9. Preliminary investigation of the X-ray diffraction (XRD) pattern shows the dual phase nature (cubic and tetragonal phase). The analysis of the Williamson-Hall plot illustrates the average crystallite size and micro-lattice strain are 91.37 nm and 0.002, respectively. The scanning electron microscopic spectrum examines the microstructural surface interface analysis, fractal study and topographic roughness (using the standard ISO25178). The analysis of Maxwell-Wagner dielectric dispersion, relaxation, and transport mechanisms are investigated utilizing dielectric, impedance, and conductivity spectrum accumulated within the experimental frequency (1 kHz–1 MHz) and temperature (298 K–373 K) range. The energy band of intrinsic and ionization regions with different frequencies (1 kHz and 10 kHz) indicates the semiconducting behavior of the material. The presence of non-Ohmic and space charge limited conduction (SCLC) mechanisms is investigated by the varistor constant β1 and β2. The thermistor constant (β) is determined to be 1683.13, and the temperature coefficient of resistance (TCR) is −0.00903, which may be suitable for wearable NTC thermistors and other related device applications.

Rapid millisecond heating via ferromagnetic resonance in MnFe2O4 nanoparticles

This study undertakes an exhaustive analysis of rapid heat generation in MnFe2O4 nanoparticles through ferromagnetic resonance within an ultra-fast timeframe of 1 ms. Real-time monitoring of temperature during single-field-pulse excitations provided detailed insights into the temperature rise profiles. By integrating micromagnetic simulations with analytical modeling—taking into account both convective and radiative losses—we have deepened our understanding of the heat transfer dynamics at play. Adjusting the analytical model to align with experimental temperature profiles enabled us to determine the efficiency of converting spin dissipation energy into heat, which stands at 17%. This figure reflects not only the surface area of the nanoparticles but also includes considerations for radiative and convective losses. Notably, employing a low AC-field strength of 17.6 Oe facilitated a rapid temperature increase of up to 90 K in just 0.5 s, showcasing a peak initial temperature rise rate of approximately 680 K/s. This research advances the frontiers of high-power heat generation driven by spin dynamics and provides a comprehensive exploration of heat transfer mechanisms over exceptionally short pulse durations. These findings could revolutionize precise and rapid temperature management at the nanoscale, unlocking prospects in bio applications, accelerated material processing, and inducing color and phase shifts in polymer matrices.

High-Temperature Reactive Wetting of Natural Quartz by Liquid Magnesium.

High-temperature wetting of natural, high-purity quartz (SiO2) and liquid magnesium (Mg) was investigated at temperatures between 973 and 1273 K. Sessile drop experiments using the capillary purification (CP) procedure were carried out under an Ar gas atmosphere (N6.0), eliminating the native oxide layer on the surface of Mg melt. The results showed that the wetting behavior was strongly dependent on temperature. At 973 and 1073 K, the wetting system displayed relatively large contact angles of 90° and 65°, respectively, demonstrating modest wetting. The wetting increased to some extent by increasing the temperature to 1123 K with a wetting angle of 22°. However, the SiO2/Mg system demonstrated complete wetting at temperatures of 1173 K and above. Furthermore, interface microstructure examination showed different reaction product phases/microstructures, depending on the wetting experiment temperature.

Solubility of Methane in Ionic Liquids for Gas Removal Processes Using a Single Multilayer Perceptron Model

In this work, four hundred and forty experimental solubility data points of 14 systems composed of methane and ionic liquids are considered to train a multilayer perceptron model. The main objective is to propose a simple procedure for the prediction of methane solubility in ionic liquids. Eight machine learning algorithms are tested to determine the appropriate model, and architectures composed of one input layer, two hidden layers, and one output layer are analyzed. The input variables of an artificial neural network are the experimental temperature (T) and pressure (P), the critical properties of temperature (Tc) and pressure (Pc), and the acentric (ω) and compressibility (Zc) factors. The findings show that a (4,4,4,1) architecture with the combination of T-P-Tc-Pc variables results in a simple 45-parameter model with an absolute prediction deviation of less than 12%.

An entropic theory of homogeneous ice nucleation in non-ionic aqueous solutions.

The nucleation of ice from aqueous solutions is a process essential to myriad environmental and industrial processes, but the physical factors affecting the capacity of different solutes to depress the homogeneous nucleation temperature of ice are yet poorly understood. In this work, we demonstrate that for many binary aqueous solutions of non-ionic solutes, this depression is dominated by the entropy of the liquid phase. Employing the classic Turnbull interpretation of the interfacial free energy γ∼TSliquid-Ssolid and estimating solution entropies with a Flory-style modification of the ideal entropy of mixing that accounts for solute size effects, we demonstrate that mixing entropy alone predicts experimental homogeneous nucleation temperatures across a wide variety of non-ionic solutions. We anticipate that this physical insight will not only enhance a fundamental understanding of homogeneous nucleation processes across fields but also open new avenues to the rational design of aqueous solutions for desired nucleation behaviors.

Evaluation by Liquid Crystal Thermography of Transient Surface Temperature Distribution in Radiant Floor Cooling Applications and Assessment of Analytical and Numerical Models

Abstract The purpose of our study is to evaluate the surface temperature distribution on a radiant floor, particularly focusing on space cooling operations, to assess the presence of nonuniformities. In fact, knowing the temperature difference between the average superficial temperature and the coldest spot can be a useful indication for condensation prevention. Primarily, we performed an experimental campaign in test rooms using temperature sensors and liquid crystal thermography. This allowed us to evaluate the floor temperature distribution both on a local scale, influenced by the discontinuous presence of buried water pipes, and on a macroscale, influenced by internal use, objects, and boundary conditions of the surrounding space. Then, the experimental temperature field on the radiant floor surface has been compared with analytical and numerical models in steady-state and transient phases, respectively. The results indicate limited superficial temperature variations that become more significant at larger tube spacings and under transient conditions. In particular, the numerical transient analysis showed that shortly after a step change in the pipe's temperature boundary condition, a larger variation is locally observable on the floor, which then decays to the new steady-state conditions, presenting more uniformity. However, local effects are generally overshadowed by macro-effects, especially for practical scenarios where many objects, furnishings, and different boundary conditions are present. Finally, as a conservative guideline for the cooling system control, we recommend maintaining the average superficial floor temperature at least 1 °C above the dew point, to account for the described nonuniformities.

Measuring thermal profiles in high explosives using neural networks

We present a new method for calculating the temperature profile of high explosive (HE) material using a Convolutional Neural Network (CNN). To train/test the CNN, we have developed a hybrid experiment/simulation method for collecting acoustic and temperature data. We experimentally heat cylindrical containers of HE material until detonation/deflagration, where we continuously measure the acoustic bursts through the HE using multiple acoustic transducers lined around the exterior container circumference. However, measuring the temperature profile in the HE in an experiment would require inserting a large number of thermal probes, which would disrupt the heating process. Thus, we use two thermal probes, one at the HE center and one at the wall. We then use numerical simulation of the heating process to calculate the temperature distribution and correct the simulated temperatures based on the experimental center and wall temperatures. We calculate temperature errors on the order of 15 °C, which is ∼12% of the range of temperatures in the experiment. We also investigate how the algorithm’s accuracy is affected by the number of acoustic receivers used to collect each measurement and the resolution of the temperature prediction. This work provides a means of assessing the safety status of HE material, which cannot be achieved using existing temperature measurement methods. In addition, it has implications for a range of other applications where internal temperature profile measurements would provide critical information. These applications include detecting chemical reactions, observing thermodynamic processes such as combustion, monitoring metal or plastic casting, determining the energy density in thermal storage capsules, and identifying abnormal battery operations.

Methane Production from Poly(lactic Acid) (PLA) and Polyhydroxybutyrate (PHB) Biodegradable Plastics with Anaerobic Granulated Sludge

- In this study, using Methanosarcina barkeri DSM 800 and Methanococcus vannielii DSM 1224 methanogenic bacteria under mesophilic (38 ± 1oC) and thermophilic (58 ± 1oC) conditions in anaerobic granulated sludge taken from Pakmaya Yeast Factory in Izmir, Turkey; Methane production from biodegradable plastics with poly(lactic acid) (PLA) and polyhydroxybutyrate (PHB) was investigated. Effect of different operating parameters, increasing biodegradation times (from 10 days to 500 days), different inoculumsubstrate ratios (ISRs) (16, 8, 4, 2, 1) and increasing biochemical methane potential (BMP) times (between 10 day and 500 days) for the production of methane gas from PLA and PHB biodegradable plastics in anaerobic granular sludge waste; Methanosarcina barkeri DSM 800 and Methanococcus vannielii DSM 1224 methanogenic bacteria were operated during the anaerobic digestion process under anaerobic conditions at mesophilic (38 ± 1oC) and thermophilic (58 ± 1oC) experimental temperatures. PLA biodegradable plastics were operated at optimum pH=7.6. PHB biodegradable plastics were carried out at optimum pH=8.1. Predicting the biodegradation behavior of PLA and PHB biodegradable plastics with BMP tests; It is found that the ISR parameter plays a very important role. This study showed that temperature plays a key role in the aging of microorganisms (Methanosarcina barkeri DSM 800 and Methanococcus vannielii DSM 1224 methanogenic bacteria) during anaerobic digestion, the degradation of bioplastic materials (PLA and PHB) and the degradation of their natural properties. The increase in temperature from mesophilic conditions to thermophilic conditions increased the activities of methanogenic bacteria such as Methanosarcina barkeri DSM 800 and Methanococcus vannielii DSM 1224. The maximum cumulative CH4(g) production was measured at 630 NL CH4 / kgVS for PHB biodegradable plastics in anaerobic granulated sludge with inoculum culture (the mixture of Methanosarcina barkeri DSM 800 and Methanococcus vannielii DSM 1224 methanogenic bacteria), at ISR=16 value, after 100 days, at pH=8.1 and at 58±1oC, respectively. The maximum 97% biodegradation efficiency was observed for PHB biodegradable plastics after 100 days, at pH=8.1 and at 58 ± 1oC thermophilic conditions, respectively.

Control effect and field application of four predatory Orius species on Megalurothrips usitatus (Thysanoptera: Thripidae).

Megalurothrips usitatus (Bagrall) is one of the most important pests of cowpea, Vigna unguiculata (Linn.) Walp in South China. Four Orius species, including Orius minutus (L.), Orius nagaii (Yasunaga), Orius sauteri (Poppius), and Orius strigicollis (Poppius), have been commercially produced and widely used as natural enemies of pests in China. In this study, we evaluated the control efficiency of these Orius species on M. usitatus in tropical Hainan Province, China, by recording the survival rates, developmental times, and predation effects in laboratory and semi-field conditions. Laboratory experiments showed that all these 4 Orius species preyed on M. usitatus under the experimental temperatures (25, 30, and 35 °C), and O. strigicollis exhibited the highest survival rate and predation effect. Semi-field cage experiments showed that the control effect of 4 Orius species on M. usitatus was significantly higher than that under normal chemical control, with O. strigicollis having the highest effect. Greenhouse experiments in Hainan Province, China, confirmed that O. strigicollis had a significant control effect on M. usitatus. Our study indicated that O. strigicollis has a significant potential for the control of M. usitatus in cowpea fields in southern China.

A spatio-temporal temperature prediction model for coal spontaneous combustion based on back propagation neural network

Coal spontaneous combustion (CSC) occurs with the change of temperature and gas concentration which is useful for early warning. To explore the intrinsic correlation between temperature evolution and gas distribution, a Back Propagation Neural Network (BPNN) model of coal temperature - gas composition - spatial distance was constructed, which is based on the gas characteristics and spatial distribution of coal temperature in large-scale coal spontaneous combustion experiments. The results showed that the O2 concentration reached the lowest value (8.058%) at 45 cm on Day 52 with the time. CO and CO2 also formed concentration peaks at 45 cm, and the gas distribution continuously moved towards the inlet position. The hydrocarbon gas components showed periodic increases, with values below 100 ppm. When the supply air time increased, the temperature rise rate in the initial stage of oxidation was slow but relatively uniform. Affected by ventilation conditions and coal density, two high-temperature zones were observed at 45 cm and 85 cm in the experimental furnace. With the inclusion of spatial distance parameters in coal temperature prediction, the BPNN model exhibits accurate predictive capability, with a total error of 0.94997. This can provide a method for predicting temperature changes in coal mine.

Temperature and strain rate effects on ultra‐high‐molecular‐weight‐polyethylene compression: An experimental and modeling approach

AbstractThis study aims to predict the compression behavior of ultra‐high molecular weight polyethylene (UHMWPE) at various temperatures (25, 40, and 55°C) and strain rates (~10−4/s and ~10−2/s) using a single set of three‐network (TN) viscoplastic model parameters. The TN model is made up of three parallel networks: networks A and B use nonlinear springs and dashpots to control the responses of the crystalline phase and confined amorphous phases, respectively, while network C captures the macromolecular amorphous networks using a single nonlinear spring. A single set of TN model parameters captures the yield and post‐yield hardening responses of the stress–strain curves at experimental temperatures and strain rates. When these TN model calibrated parameters are used as material property in a commercial finite element tool to simulate UHMWPE compression, the predicted and simulated results match well, showing the model's fidelity. Additionally, the model predicts experiments conducted at 40 and 70°C with loading rates of ~10−3/s and ~10−2/s, respectively. The study also correlates the deformations of UHMWPE's crystalline structure and macromolecular amorphous networks with its global stress versus strain response by extracting stresses from individual networks of the TN model at different strain rates and temperatures.Highlights UHMWPE's mechanical behavior predicted by a single set of TN model parameters TN model predicts mechanical response at various strain rates and temperatures. TN model explains microstructural crystalline and amorphous phase deformations.

Effect of Selective Enrichment Storage Temperature and Duration Time on the Detection of Salmonella in Food.

For pathogen detection in food, there are occasions where samples cannot be processed immediately after selective enrichment or need to be reexamined days or weeks later for confirmation or retest. This study aimed to investigate the effect of different prolonged period of storage of selective enrichments of food at 4 ± 2°C and room temperature (20-22°C) on the detection and isolation of Salmonella. This study included two experiments involving 34 types of foods to compare the effect of 4 ± 2°C and room temperature storage on the detection of Salmonella in 204 selective enrichments (Rappaport-Vassiliadis [RV] and Tetrathionate [TT] broths) during a 42-day storage (Experiment I); and to monitor the survival of Salmonella in 300 selective enrichments (RV and TT) with different pre-enrichment broths (Lactose broth [LB] or Buffered peptone water broth [BPW]), stored at 4°C for 60 days (Experiment II). All the samples were subjected to Salmonella analysis following the FDA BAM method. During multiple samplings, the positive detection rate for Salmonella remained consistent through Day 42 after selective enrichment, irrespective of Salmonella serotype, storage temperature, pre-enrichment broth, or selective enrichment broth in both Experiment I and II. However, on Day 60 sampling in Experiment II, seven previously positive results turned to negatives. These data indicated that storage of RV and TT enrichments at 4 ± 2°C or room temperature for up to 42 days after selective enrichment did not compromise the detection of Salmonella in the tested food categories, regardless of Salmonella serotypes and the broths used for pre-enrichment and selective enrichment. At least for the food types studied in this experiment, the recovery of Salmonella from selective enrichments could be postponed for a limited period of time (e.g., <42 days) if needed without adversely affecting the test results. However, the delayed analysis of TT and RV enrichments does pose a risk of reduced detection sensitivity, as evidenced by the seven negative results on Day 60 compared to previous positives. We do not recommend or endorse delaying the analysis of TT and RV enrichments. In the food matrixes investigated in this experiment, the plating and isolation of Salmonella from selective TT and RV enrichments stored at 4 ± 2°C or room temperature could be deferred for a period (up to 42 days) without any negative effect on the test results, if necessary.

Microstructures evolution and mechanical properties of dilute Mg-Y-Al-Ca-Mn alloy during uniaxial compression deformation at different strain levels

The isothermal compression tests of dilute as-extruded Mg-2Y–1Al (wt%) alloy, Mg-2Y–1Al-0.3Ca-0.7Mn (wt%) alloy and Mg-2Y–1Al-0.7Ca-0.3Mn (wt%) alloy were systematically carried out. The experimental temperature (T) was 200 °C, the strain rate (ε̇) was 0.001 s−1 and the strain levels ranged from 0.1 to 0.3. Tests results demonstrated that compressive strength and yield strength clearly increased after adding Ca and Mn, just as Mg-2Y–1Al-0.3Ca-0.7Mn alloy and Mg-2Y–1Al-0.7Ca-0.3Mn alloy had displayed. It was worth mentioning that peak stress of Mg-2Y–1Al-0.7Ca-0.3Mn alloy was 100 MPa higher than that of Mg-2Y–1Al alloy under the strain count of 0.3. The main reason was that adding Ca and Mn into Mg-2Y–1Al caused existence of more second phases which made microstructure finer. Furthermore, microstructures evolution and deformation mechanisms of three alloys were investigated by electron backscatter diffraction (EBSD). Based on EBSD results, what could be found was there existed plenty of twins which could be determined to be {10−12} extension twin when strain count is 0.1. And because of more second phases, Mg-2Y–1Al-0.3Ca-0.7Mn alloy and Mg-2Y–1Al-0.7Ca-0.3Mn alloy activated more extension twins than Mg-2Y–1Al alloy. When strain count was 0.3, it could be seen that three alloys all occurred dynamic recrystallization (DRX) and the main DRX mechanism was strongly believed to be discontinue dynamic recrystallization (DDRX). According to dislocation slip analysis results of three alloys, much pyramidal <c+a> slip and little prismatic <a> slip was activated during compression and dominated deformation.