Starch–methylcellulose–whey protein film properties

Four % (wt/wt) aqueous solutions were prepared at corn starch:methylcellulose:whey protein isolate (CS:MC:WPI) ratios of 2:2:2, 1:2:3, 2:1:3, 2:2:0, 1:2:0 and 2:1:0. Glycerol (gly) was used as a plasticiser at CS–MC–WPI:gly ratios of 2:1, 2.5:1 and 3:1. CS–MC–WPI blend films were stronger than CS–WPI films and had lower oxygen permeability (OP) than MC–WPI films. The highest tensile strength (TS) of blend films was 8.01 ± 3.41 MPa, at CS:MC:WPI ratio of 2:2:0 and CS–MC–WPI:gly ratio of 3:1. Lowest OP value was 45.05 ± 7.24 cm³ μm m^?2 per day kPa^?1, at CS:MC:WPI ratio of 2:2:2 and CS–MC–WPI:gly ratio of 3:1. OP values were predictable based on relative amounts of components. However, TS and elastic modulus properties of the CS–MC–WPI blend films did not reflect the relative amounts of the components. All of CS–MC–WPI films were translucent indicating some degree of immiscibility among the CS, MC and WPI. These results indicate the influence of complex molecular interactions among the components.     

Pre- and post-peak toughening behaviours of fibre-reinforced hot-mix asphalt mixtures

Recycled plastic fibre-reinforced hot-mix asphalt (HMA) mixtures have better fatigue resistance than plain HMA. The toughening effects of recycled plastic fibre-reinforced HMA were characterised using direct tensile loading tests. Adding a small quantity of recycled plastic fibres to HMA was found to significantly increase the mixture's fracture energy and toughness, which were calculated using the pre- and post-peak stages of tensile force–displacement curves. A theoretical model representing the pre-peak behaviour of fibre-reinforced HMA with direct tension-softening curves for various fibre contents is presented here. The enhanced toughness through post-peak analysis was also observed using toughness indices associated with fibre-bridging effect after the pre-peak composite stress. The pre-peak fracture energy model and post-peak toughness indices appeared to be governed by the direct tensile toughening of fibre-reinforced HMA's enhanced fibre-bridging effects. The pre-peak fracture energy model demonstrates the effect of fibre content on the strain energy density during the pull-out process within the pre-peak composite stress region. The maximum pre-peak fracture energy of a coarse-graded HMA mixed with recycled plastic fibres is achieved at a fibre content of 0.4% of the total weight of the HMA. The increases in the toughness indices within the post-peak composite stress region indicate that the fatigue resistance of fibre-reinforced HMA is at least 30% greater than that of control HMA.     

Development of an Automated Method for Folin‐Ciocalteu Total Phenolic Assay in Artichoke Extracts

Abstract: We developed a system to run the Folin‐Ciocalteu (F‐C) total phenolic assay, in artichoke extract samples, which is fully automatic, consistent, and fast. The system uses 2 high performance liquid chromatography (HPLC) pumps, an autosampler, a column heater, a UV/Vis detector, and a data collection system. To test the system, a pump delivered 10‐fold diluted F‐C reagent solution at a rate of 0.7 mL/min, and 0.4 g/mL sodium carbonate at a rate of 2.1 mL/min. The autosampler injected 10 μL per 1.2 min, which was mixed with the F‐C reagent and heated to 65 °C while it passed through the column heater. The heated reactant was mixed with sodium carbonate and color intensity was measured by the detector at 600 nm. The data collection system recorded the color intensity, and peak area of each sample was calculated as the concentration of the total phenolic content, expressed in μg/mL as either chlorogenic acid or gallic acid. This new method had superb repeatability (0.7% CV) and a high correlation with both the manual method (r²= 0.93) and the HPLC method (r²= 0.78). Ascorbic acid and quercetin showed variable antioxidant activity, but sugars did not. This method can be efficiently applied to research that needs to test many numbers of antioxidant capacity samples with speed and accuracy.     

High‐Precision Temperature‐Controllable Metal‐Coated Polymeric Molds for Programmable,Hierarchical Patterning

Nanofabrication is an indispensable process in nanoscience and nanotechnology. Unconventional lithographic techniques are often used for fabrication as alternatives to photolithography because they are faster, more cost‐effective, and simpler to use. However, these techniques are limited in scalability and utility because of the collapse of preprinted structures during step‐and‐repeat processes. This study proposes a new class of temperature‐controllable polymeric molds that are coated with a metal such that any site‐specific patterning can be accomplished in a programmable manner using selective contact‐dewetting lithography. The lithography allows sub‐100 nm patterning, step‐and‐repeat processing, and hierarchical structure fabrication. The programmable feature of the lithography can be utilized for the structural coloring and shaping of objects. Large‐area programmable patterning, semiconductor device manufacturing, and the fabrication of iridescent security devices would benefit from the unique features of the proposed strategy.     

Interpretation and diagnosis of fouling progress in membrane bioreactor plants using a periodic pattern recognition method

Fouling is a principal constraint of membrane bioreactors (MBRs). It blocks the wide use of MBRs and aggravates the ability of MBRs. Trans-membrane pressure (TMP) is measured simply from MBRs and is a useful factor for evaluating fouling phenomena such as fouling mechanisms. Fouling mechanism diagnosis based on a measured TMP was used to evaluate MBRs operation conditions. However, diagnosis of MBR conditions is difficult due to the dynamic conditions of MBRs. Therefore, we used differential calculus, exponential weighted moving average (EWMA) and fast Fourier transform (FFT) to determine a periodic pattern for diagnosing fouling mechanisms in the dynamic operating conditions of MBRs. The periodic pattern was reflected in the operating conditions of MBRs, based on the fouling mechanism using TMP. We used two data sets obtained from pilot-scale MBR to suggest a periodic pattern and validated the proposed method using a lab-scale MBR experiment. Consequently, the suggested periodic pattern can diagnose fouling mechanisms using the proposed method, because the methods can be adjusted under the dynamic conditions of MBRs.     

Steam reforming of liquefied natural gas (LNG) for hydrogen production over nickel–boron–alumina xerogel catalyst

A series of mesoporous nickel–boron–alumina xerogel (x-NBA) catalysts with different boron/nickel molar ratio (x = 0–1) were prepared by an epoxide-driven sol–gel method. The effect of boron/nickel molar ratio on the catalytic activities and physicochemical properties of nickel–boron–alumina xerogel catalysts was investigated in the steam reforming of liquefied natural gas (LNG). All the mesoporous x-NBA catalysts showed similar surface area. Introduction of boron increased interaction between nickel and support. In addition, introduction of boron into x-NBA catalysts reduced methane activation energy and increased nickel surface area. Promotion of boron had a positive effect on the catalytic activity due to the increase of adsorbed methane and nickel surface area. The amount of adsorbed methane and nickel surface area exhibited volcano-shaped trends with respect to boron/nickel molar ratio. LNG conversion and hydrogen yield increased with increasing the amount of adsorbed methane and with increasing nickel surface area. Among the catalysts, 0.3-NBA, which retained the largest amount of adsorbed methane and the highest nickel surface area, showed the best catalytic performance. It was also revealed that x-NBA catalysts showed strong coke resistance during the steam reforming reaction.     

Rapid and efficient deoxygenation of sulfoxides to sulfides with tantalum(V) chloride/sodium iodide system

TaCl₅/NaI system converts a wide range of sulfoxides to the corresponding sulfides in high yields with short reaction times, under mild conditions. It is worth mentioning that this protocol is chemoselective and tolerates various functional groups (such as –Br, –Cl, –OCH₃, –CHO, and –NO₂) and double bond.     

Commercial process for mass production of synthetic natural gas through the adiabatic reactors: operational characteristics of a 50‐kW pilot‐plant,influence of steam,and CO2

In synthetic natural gas (SNG) reaction process, the water gas shift (WGS) reaction and methanation reaction take place simultaneously, and an insufficient supply of steam might deactivate the catalyst. In this study, the characteristics of the methanation reaction with a commercial catalyst and using a low [H₂]/[CO] mole ratio in SNG synthesis are evaluated. The reaction characteristics at various possible process parameters are evaluated varying different process parameters such as the [H₂O]/[CO] mole ratio, [H₂]/[CO] mole ratio, flow of different % CO₂, and reaction temperature. Temperature profiles on catalyst bed are monitored as a function of the [H₂O]/[CO] mole ratio, [H₂]/[CO] mole ratio, and flow of different % CO₂. Through a lab‐scale optimization process, suitable optimum conditions are selected and in the same condition a 50‐kW pilot‐scale SNG production process through adiabatic reactors is carried out. The pilot scale SNG reaction is stable through overnight and the CO conversion efficiency and CH₄ selectivity are 100% and 97.3%, respectively, while the maximum CH₄ productivity is 0.654 m³/kg_cat · h. Copyright © 2016 John Wiley & Sons, Ltd.     

Deep Q-network-based auto scaling for service in a multi-access edge computing environment

In 5G networks, it is necessary to provide services while meeting various service requirements, such as high data rates and low latency, in response to dynamic network conditions. Multi-access edge computing (MEC) is a promising concept to meet these requirements. The MEC environment enables service providers to deploy their low latency services that are composed of multiple components. However, operating a service manually and attempting to satisfy the quality of service (QoS) requirements is difficult because many factors need to be considered in an MEC scenario. In this paper, we propose an auto-scaling method using deep Q-networks (DQN), which is a reinforcement learning algorithm, to resize the number of instances assigned to service. In our evaluation, compared to other baseline methods, the proposed approach maintains the appropriate number of instances effectively in response to dynamic traffic change while satisfying QoS and minimizing the cost of operating the service in the MEC environment. The proposed method was implemented as a module running in OpenStack and published as open-source software.