Synthesis of free-standing graphene in atmospheric pressure microwave plasma for the oil-water separation application

The synthesis of free-standing graphene in a microwave plasma at ambient conditions is currently of great interest. The past works have relied on the usage of higher microwave powers to synthesize free-standing graphene which is not only costly but also an obstacle to the industrialization of the process. The aim of this work was to bring down the cost of the process by synthesizing graphene at a significantly lower microwave power, i.e. 250 W. The formation of graphene was confirmed through Raman spectrum and scanning electron microscopy, where the Raman spectrum showed the signature 2D peak of graphene, and the vertical orientation of the graphene was observed in the microscopic images. The application of graphene in oil-water separation is demonstrated based on its hydrophobic and oleophilic properties. The as-synthesized pristine graphene coated on a melamine sponge showed a mass absorption capacity (57 g/g) comparable to that of functionalized or composite graphene

Assessment of salinity tolerance in rice using seedling based morpho-physiological indices

Background: Salinity is among the most damaging abiotic stresses for rice production which limits its growing area. The present research was conducted to evaluate five rice varieties for salinity tolerance at seedling stage.Methods: Experiment was conducted in triplicate and in two sets. One set was grown as a control (non-stress) and other as salt stressed. Salt stress of 15 dS/m was applied to one set of rice seedlings under controlled conditions. Data for different growth related morpho-physiological traits, i.e. germination percentage, root and shoot length, seedling fresh and dry weight, Na+ and K+ uptake were recorded after 15 days of seedling emergence under control as well as salinity condition.Results: Significant differences were observed among the genotypes under both the treatments and interaction of the evaluated traits suggested a significant variability among the rice genotypes under salt stress. NIAB-IRRI-9, Basmati-198 and KSK-133 were proved to be relatively salt tolerant varieties as they showed good performance for the recorded parameters. However, Basmati-385 was observed a salt sensitive variety due to highest reduction in seedling fresh and dry weight along with the maximum Na+ uptake.Conclusion: Based on obtained results, it was concluded that the evaluated morpho-physiological traits were useful to screen rice cultivars for salinity stress. In addition, NIAB-IRRI-9, Basmati-198 and KSK-133 can be used in breeding programs as tolerant check and Basmati-385 can be used as sensitive check

Electrochemical sensing of oxalic acid using silver nanoparticles loaded nitrogen-doped graphene oxide

The adverse effects of oxalic acid (OA) on human health linked with its excessive consumption necessitates an improved sensor. Here, we demonstrate an electrochemical sensor for oxalic acid detection based on silver nanoparticles (Ag-Nps) and nitrogen-doped graphene oxide (N-GO) nanocomposite. N-GO, which was synthesized using atmospheric pressure microwave plasma has been first time employed for electrochemical application. The nanocomposite formation was confirmed through scanning electron microscopy and EDS elemental analysis. The nanocomposite-based sensor showed a higher current response, good selectivity and stability which can be attributed to the synergistic-effect of Ag-Nps and N-GO. Amperometric responses were proportional to the concentration of OA between 10 and 300 μM, and the detection limit was 2 μM

Application of Gene Expression Programming (GEP) for the prediction of compressive strength of geopolymer concrete

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (), the percentage of plasticizer (), the initial curing temperature (), the age of the specimen (), the curing duration (), the fine aggregate to total aggregate ratio (), the percentage of total aggregate by volume (), the percent SiO2 solids to water ratio () in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (), the activator or alkali to FA ratio (), the sodium oxide (Na2O) to water ratio () for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (). A GEP empirical equation is proposed to estimate the of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations

Bactericidal vertically aligned graphene networks derived from renewable precursor

Graphene nanostructures exhibit a wide range of remarkable properties suitable for many applications, including those in the field of biomedical engineering. In this work, plasma-enhanced chemical vapor deposition was utilized at different applied RF power for the fabrication of vertical graphene nanowalls on silicon and quartz substrates from an inherently volatile carbon precursor without the use of any catalyst. AFM confirmed the presence of very sharp exposed graphene edges, with associated high surface roughness. The hydrophobicity of the material increased with the power of deposition, reaching the water contact angle of 123 ˚ for 500 W. Confocal scanning laser microscopy demonstrated that the viability of gram-negative Escherichia coli and gram-positive Staphylococcus aureus cells were 33% and 37% when incubated on graphene samples, respectively, compared to controls (quartz) that showed the viability of 82% and 84%, respectively. SEM verified significant morphological damage to bacterial cell walls by the sharp edges of graphene walls, with cells appearing abnormal and deformed. The presented data clearly contributed to the current understanding of the mechanical-bactericidal mechanism of vertically oriented graphene nanowalls upon direct contact with microorganisms

Organization Performance Influenced by the BSD and Consultative Management Style

The purpose of this study is to investigate the relationship among Balanced Scorecard, consultative management style, participative management style and organization size with organizational performance. All the variables are highly correlated with each other. This is an empirical study and data is collected from 100 respondents of service sector from Azad Kashmir. We used five likert scales to collect data from the respondents and data analyzed through SPSS. The result showed that there is a significant positive correlation among variables. This study also indicates that effective management style is necessary for improving the performance of organization. The result indicates that Balanced Scorecard has significant impact on performance of organization. Organization use BSC to handle the complexity and challenges that management faces during their operations. The collection of data is from small area due to time constraint. In the future comparative study can be done with other organizations for better analysis of results

Knowledge, Attitude and Practices about Blood Donation among Undergraduate Medical Students in Karachi

Background: Blood donation is a major concern to the society as donated blood is lifesaving for individuals who need it. In Pakistan, the concept of voluntary blood donors is almost non-existent due to the absence of blood donor motivation and retention strategies. The healthy, active and receptive huge student population can be potential blood donor to meet the safe blood requirements. The objective of this study was to determine the knowledge, attitude and practices about blood donation among undergraduate medical students in Karachi. Methods: A cross sectional study was conducted in two public and one private medical college in Karachi from January to March 2012. A total of 600 medical students were interviewed with the help of structured questionnaire. Data analysis was performed in SPSS version 19. Multiple regressions were used to examine the effect of different independent variables on a single dependent variable to test the statistical significance at 95% confidence level. Results: The majority of participants (92%) had appropriate knowledge regarding the various aspects of blood donation. Attitude domain showed that around 42% of students were positive about blood donation. Around 50% of students showed willingness to donate blood. After adjusting for potential confounders, male gender was associated with appropriate knowledge, positive attitude and willingness for blood donation when compared against female gender. Conclusion: Good knowledge about blood donation practices is not transforming in donating blood. Interactive awareness sessions on blood donation should be organized during undergraduate years and opportunities for blood donation should be created for the students, which can greatly enhance the movement for voluntary non-remunerated blood donation-, to ensure good quality of blood and safe modern medical care.sch_pod2pub3848pub

Job Interactivity Using a Steering Service in an Interactive Grid Analysis Environment

Grid computing has been dominated by the execution of batch jobs. Interactive data analysis is a new domain in the area of grid job execution. The Grid-Enabled Analysis Environment (GAE) attempts to address this in HEP grids by the use of a Steering Service. This service will provide physicists with the continuous feedback of their jobs and will provide them with the ability to control and steer the execution of their submitted jobs. It will enable them to move their jobs to different grid nodes when desired. The Steering Service will also act autonomously to make steering decisions on behalf of the user, attempting to optimize the execution of the job. This service will also ensure the optimal consumption of the Grid user's resource quota. The Steering Service will provide a web service interface defined by standard WSDL. In this paper we have discussed how the Steering Service will facilitate interactive remote analysis of data generated in Interactive Grid Analysis Environment

Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions

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Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically.publishedVersio