Size and morphology controlled synthesis of gold nanoparticles in green solvent: Effect of reducing agents

The effect of reducing agents on the synthesis of Au(0) metallic nanoparticles (Au NPs) prepared in green solvent medium of β-d-glucose-water dispersions has been reported first. The different equivalent amounts of NaBH₄ and pH values adjusted by NaOH were tested for the reduction of Au salt (HAuCl₄·3H₂O (hydrogen tetrachloroaurate (III) trihydrate) to obtain Au NPs. The type and the amount of reducing agent and the pH of the solution affected the size and morphology of the NPs. Addition of 4 equivalents of NaBH₄ produced homogeneously dispersed 5.3 nm (σ = 0.7) diameter particles. Excess addition of NaBH₄ caused the NPs to settle down as the precipitate forming mesh or wire structure. When salt was reduced by the addition of NaOH (pH = 8.0) the particles were larger (14.2 nm) and less homogeneous (σ = 2.8). At pH = 12.2 the NPs settled at the bottom of the vial when preparation was left overnight. The wire and mesh like structures were obtained at higher pH = 12.2.     

In-vitro antioxidant and antibacterial activities of pumpkin,quince, muskmelon and bottle gourd seeds

Ethanol and chloroform extracts of pumpkin, quince, muskmelon, and bottle gourd seeds were studied for in-vitro antioxidant and antibacterial activities. Crude protein and fat contents of all the seeds compared favorably with high protein legumes and high oil containing oilseeds. Ethanol and chloroform extracts of bottle gourd seeds had highest phenolic content. A direct positive relationship between antioxidant activities and extract concentration was observed with the relationship being more pronounced in chloroform extract of quince seeds (R²?=?0.9685 and 0.9829) followed by ethanol extract of pumpkin seeds (R²?=?0.9666 and 0.9685) as per 1,1-diphenyl-2-picrylhydrazyl and hydrogen peroxide (H₂O₂) method, respectively. Seed extracts were also studied for in-vitro antibacterial activity against Salmonella typhii, Enterococcus sp., Escherichia coli, Bacillus subtilis and Staphylococcus aureus. Ethanol extract of pumpkin seeds inhibited the growth of all bacteria; however, both the extracts of quince seeds did not show any activity against S. typhii, E. coli and S. aureus.     

Design of backtracking search heuristics for parameter estimation of power signals

Neural Computing and Applications - This study presents a novel implementation of evolutionary heuristics through backtracking search optimization algorithm (BSA) for accurate, efficient and robust...     

On increasing the sensitivity of moving average control chart using auxiliary variable

In the statistical process control, the most useful tool to monitor the manufacturing processes in the industries is the control chart. Quality practitioners always desire the charting structure that identifies sustainable changes in the monitoring processes. The sensitivity of the control chart is improved when additional correlated auxiliary information about the study variable is introduced. The regression estimate in the form of auxiliary and supporting variables presents an unbiased and efficient statistic of the mean of the process variable. In this study, auxiliary information-based moving average (AB-MA) control chart is designed for efficient monitoring of shifts in the process location parameter. The performance of the AB-MA control chart is evaluated and compared with existing charts using average run length and other run length characteristics. The comparison reveals that the AB-MA control chart outperforms the competitors in detecting the small and medium changes in the process location parameter. The application of the proposal is also provided to implement it in real situation.     

An enhanced approach for the progressive mean control charts

To maintain and improve the quality of the processes, control charts play an important role for reduction of variation. To detect large shifts in the process parameters, Shewhart control charts are commonly applied but for small shifts, exponentially weighted moving averages (EWMA), cumulative sum (CUSUM), double exponentially weighted moving average (DEWMA), double CUSUM, moving average (MA), double moving average (DMA), and progressive mean (PM) control charts, are used. This study proposes double progressive mean (DPM) and optimal DPM control charts to enhance the performance of the PM chart. As the proposed DPM control charts use information sequentially, hence their performance is compared with natural competitors EWMA, CUSUM, DEWMA, double CUSUM, MA, DMA, and PM control charts. Run length and its different properties are evaluated to compare the performance of the proposed charts and counterparts. Results reveal that proposed optimal DPM outperforms the other charts. An example related to voltage on fixed capacitance level is also provided to illustrate the proposed charts.     

Multi-mode hydrogen storage in nanocontainers

Hydrogen can be stored in containers or in materials (in molecular or atomic forms). The atomic form can further exist as multiple phases. Molecular hydrogen can be adsorbed on the surface or can be present inside the material. By invoking multiple modes of hydrogen storage, we establish a paradigm shift in the philosophy of hydrogen storage. Using a novel strategy of storage of molecular hydrogen in metal (Pd) nanocontainers, we observe that 18% hydrogen is in molecular form. Interestingly, this is achieved at 25 °C and 1 atm pressure; which is in contrast to storage in MOFs and carbonaceous materials like nanotubes. Enhancement in storage capacity as compared to Pd nanocrystals of the same mass is observed (36% increase at 1 atm & 25 °C), along with fast kinetics (0.5 wt% hydrogen absorption in 5 s). A new mechanism for hydrogen storage involving the dual catalytic role of Pd is established.     

Energy-efficient cluster-based artificial intelligence routing for wireless sensor networks

Artificial intelligence (AI)-based wireless sensor network technology is the future of advancement for real-time applications. With AI wireless sensor network technology, it is possible to collect data from any environment, analyze in real time, and use it to optimize processes and operations. AI wireless sensor network technology provides an unprecedented level of accuracy as well as the ability to detect even the slightest changes in a given environment. The AI-based approach uses clustering-based techniques with self-organizing map (SOM) for energy conservation in resource-constrained networks. By clustering the network, it becomes more energy efficient, as data can be shared among members of a cluster without needing to be transmitted across multiple nodes. The proposed AI cluster-based routing approach outperforms in terms of energy consumption and computational challenges of the network. The results obtained demonstrate the proposed approach to achieve lower energy consumption than the existing algorithms while providing the same level of performance in terms of throughput and latency, as well as a comparison with traditional justification techniques.     

Characterization and properties of ferromagnetic shape memory alloys

Ni–Mn–Ga shape memory alloys are employed for applications in actuators and sensing devices. These alloys exhibit ferromagnetic shape memory effect with large reproducible strains in moderate magnetic fields. This work presents a study of the effect of composition and annealing treatment on the microstructure and magnetic properties in Mn-rich off-stoichiometric Ni–Mn–Ga alloys. Modulated martensitic structure (c/a < 1) with hierarchical twins was found at room temperature in alloys with Mn ≥ 28 at.% whereas the alloy containing higher Ga (> 22 at.%) revealed austenitic structure at room temperature. Ferromagnetic nature of the alloys was confirmed by the magnetization curves. It is demonstrated that a maximum of 400 parts per million strain was measured in the alloy with 7 M martensitic structure at room temperature.