A novel application of micro-EDM process for the generation of nickel nanoparticles with different shapes

This article explains production of nickel nanoparticles through a micro-electrical discharge machining (EDM) process with a combination of different process parameters. The production of nickel nanoparticles was carried out in a dielectric medium (deionized water) with developed micro-EDM while polyvinyl alcohol worked as the stabilizing agent. The characterization of nickel nanoparticle was done by scanning electron microscope (SEM), Energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), UV–Vis spectroscopy, and Fourier transform infrared (FTIR) analysis. From this investigation, the mean crystal size of the nickel nanoparticles was found to be in the range of 15–20?mm for a pulse-on time variation of 2–0.3?µs and the crystal size was found to decrease with the decrease of pulse-on time. It was also observed that with this decrease, the shape and size of nickel nanoparticles change from spherical to needle-like. The dispersion stability of nickel nanofluid was determined by viscosity measurements and the dynamic viscosity was noted to decrease by decreasing the pulse duration. From the FTIR spectrum results, it was confirmed that the synthesized nickel nanoparticles in deionized water were pure and monolithic. UV–Vis–NIR spectroscopy depicted that the band gap energy increases with a reduction in the pulse-on time and obtains a higher band gap (5.31?eV) for 0.3?µs pulse-on time.     

Extranasal Staphylococcus aureus colonization predisposes to bloodstream infections in patients on hemodialysis with noncuffed internal jugular vein catheters

Introduction: Staphylococcal infection of endogenous origin is an important cause of morbidity and mortality in patients who receive hemodialysis (HD). The risk of such infections in nasal carriers of the organism is well defined. Extranasal carriage of the organism at extranasal sites may pose similar risks. Methods: A total of 70 patients about to undergo internal jugular vein catheterization for HD were enrolled in this prospective observational study. Swab cultures were obtained from anterior nares, posterior pharynx, axillae, toe web spaces, and vascular access sites at baseline and 1 week later. A patient was defined as a persistent carrier when the same organism was grown in both samples. Staphylococcus aureus bloodstream infections were assessed by blood and catheter tip cultures over a 90‐day period. Findings: The mean age of the patients was 43.71 ± 16.2 years. Persistent S. aureus carriage at anterior nares, throat, axilla, toe web spaces, vascular access site, and all sites was documented in 27.9%, 11.4%, 40%, 32.9%, 4.3%, and 64.2% of patients, respectively. Fifteen patients developed S. aureus infections. Catheter related S. aureus infections (CRI) were more likely in persistent carriers than nonpersistent carriers with odds ratios (95% CI) of 10.2 (2.8–37.1), 8.6 (1.7–42.2), 17.3 (3.4–86.0), 3.0 (0.9–9.8), and 1.9 (0.2–22.4) for anterior nares, throat, axilla, toe web spaces, and vascular access site carriers, respectively. The probability of developing CRI in persistent S. aureus carriers was 55% compared to none in noncarriers at 90 days (P = 0.04). Discussion: Extranasal S. aureus carriage is as significant a risk factor as nasal carriage for staphylococcal infections in patients on HD through catheters. The study is limited by lack of molecular phenotyping.     

Can twice weekly hemodialysis expand patient access under resource constraints?

The convention of prescribing hemodialysis on a thrice weekly schedule began empirically when it seemed that this frequency was convenient and likely to treat symptoms for a majority of patients. Later, when urea was identified as the main target and marker of clearance, studies supported the prevailing notion that thrice weekly dialysis provided appropriate clearance of urea. Today, national guidelines on hemodialysis from most countries recommend patients receive at least thrice weekly therapy. However, resource constraints in low‐ and middle‐income countries (LMIC) have resulted in a substantial proportion of patients using less frequent hemodialysis in these settings. Observational studies of patients on twice weekly dialysis show that twice weekly therapy has noninferior survival rates compared with thrice weekly therapy. In fact, models of urea clearance also show that twice weekly therapy can meet urea clearance “targets” if patients have significant residual function or if they follow a protein‐restricted diet, as may be common in LMIC. Greater reliance on twice weekly therapy, at least at the start of hemodialysis, therefore has potential to reduce health care costs and increase access to renal replacement therapy in low‐resource settings; however, randomized control trials are needed to better understand long‐term outcomes of twice versus thrice weekly therapy.     

Optical detection of CA 15.3 breast cancer antigen using CdS quantum dot

The present study focus on optical sensing of breast cancer antigen 15.3 (CA 15.3) using cadmium sulphide quantum dot (CdS‐QD) in saline and serum samples spiked with antigen. The surface of CdS‐QD was modified by cysteamine capping followed by tagging of CA 15.3 antibody. The samples were characterised using UV‐visible absorption spectroscopy (UV‐VIS Spectroscopy), Fourier transform infrared spectroscopy (FTIR), high‐resolution transmission electron microscopy (HRTEM) attached with energy‐dispersive X‐ray spectroscopy, phase contrast inverted epi‐fluorescence microscopy and photoluminescence (PL) spectrophotometry (EDS). The CdS‐QD showed a mean diameter of 3.02 ± 0.6 nm. The complex formed after antigen‐antibody interaction resulted in distinguishable optical and fluorescence intensity with respect to varying concentration of antigen. The PL study revealed that CA 15.3 antibody labelled CdS QD can detect CA 15.3 tumour marker even at very low concentration of 0.002 KU/L with a constant response time of 15 min. This study clearly indicates that detection of CA 15.3 at low concentration is possible using surface modified CdS QD in serum samples and can find immense applications in biosensor development for detection of breast cancer marker similar to various automated detection kits available in market.Inspec keywords: semiconductor quantum dots, cadmium compounds, II‐VI semiconductors, wide band gap semiconductors, cancer, tumours, optical sensors, biosensors, biomedical equipment, visible spectra, ultraviolet spectra, Fourier transform infrared spectra, transmission electron microscopy, X‐ray chemical analysis, fluorescence, optical microscopy, photoluminescence, proteins, molecular biophysics, nanosensors, nanomedicine, nanoparticlesOther keywords: optical detection, CA 15.3 breast cancer antigen, optical sensing, cadmium sulphide quantum dot, saline samples, serum samples, cysteamine capping, CA 15.3 antibody, UV‐visible absorption spectroscopy, Fourier transform infrared spectroscopy, high‐resolution transmission electron microscopy, energy dispersive X‐ray spectroscopy, phase contrast inverted epifluorescence microscopy, photoluminescence spectrophotometry, antigen‐antibody interaction, fluorescence intensity, optical intensity, CA 15.3 tumour marker, surface modified CdS QD, biosensor development, time 15 min, CdS     

Effect of friction stir processing and hybrid reinforcements on copper

In this research, a copper based surface composite was fabricated through dispersing hybrid composite particles onto its surface through friction stir processing (FSP) technique. Optical micrographs and scanning electron microscopy images indicates finer refinement of grains and particles dispersion into matrix along with its bonding and particle separation. As per the outcomes of microhardness analysis, hardness of the developed surface composite shows increment with increase in dispersion of volume fraction of hybrid particles. Strength of the developed copper surface composite exhibited a positive trend with introduction of hybrid reinforcement particle onto the surface of the composite but yet again ductility reduced. Wear resistance of the composite increased with reinforcement addition and the same was supported through worn out surface morphology. Fluctuations in friction coefficient value reduced with increase in particles, as for the presence in BN particles while the average frictional coefficient value was observed increasing. A reduction in corrosion rate was observed with increase in reinforcement particle dispersion onto copper matrix through FSP.     

Cutting force and hole quality analysis in micro-drilling of CFRP

Micro-drilling in carbon fiber reinforced plastic (CFRP) composite material is challenging because this material machining is difficult due to anisotropic, abrasive and non-homogeneous properties and also downscaling of cutting process parameters affect the cutting forces and micro-drilled hole quality extensively. In this work, experimental results based statistical analysis is applied to investigate feed and cutting speed effect on cutting force components and hole quality. Analysis of variance based regression equation is used to predict cutting forces and hole quality and their trend are described by response surface methodology. Results show that roundness error and delamination factor have similar trends to those of radial forces and thrust force, respectively. Non-linear trends of cutting forces and hole quality errors are observed during downscaling of the micro-drill feed value. Optimization results show that cutting forces and hole quality errors are minimum at a feed value which is almost equal to the tool edge radius rather than at the lowest feed value. Therefore, the presented results clearly show the influences of size effects on cutting forces and hole quality parameters in micro-drilling of CFRP composite material.     

Life cycle energy and carbon footprint analysis of photovoltaic battery microgrid system in India

Electricity supply in India is from a centralized grid. Many parts of the country experience grid interruptions. Life cycle energy and environmental analysis has been done for a 27 kWp photovoltaic system which acts as grid backup for 3 h outage in an Indian urban residential scenario. This paper discusses energy requirements and carbon emission for a PV storage system for five different battery technologies in Indian context. This can be used as a metric for comparative analysis for new batteries, with an undeveloped market. The energy requirements for the components are quantified and are compared in terms of energy payback time (EPBT) and Net Energy Ratio (NER). All the calculations are done for Indian context. EPBT is found to be in the range of 2–4.5 years for all the systems, while NER is in the range of 6.6–2.52. NaS has the highest emission factor of 0.67 kgCO₂/kWh and the least for NiCd (0.091 kgCO₂/kWh). These factors can be used to select a PV battery option and to target selection of materials and systems based on the reported values.     

Partitioning large networks without breaking communities

The identification of cohesive communities is a key process in social network analysis. However, the algorithms that are effective for finding communities do not scale well to very large problems, as their time complexity is worse than linear in the number of edges in the graph. This is an important issue for those interested in applying social network analysis techniques to very large networks, such as networks of mobile phone subscribers. In this respect, the contributions of this paper are twofold. First, we demonstrate these scaling issues using a prominent community-finding algorithm as a case study. Then, we show that a two-stage process, whereby the network is first decomposed into manageable subnetworks using a multilevel graph partitioning procedure, is effective in finding communities in networks with more than 10⁶ nodes.     

Detection and quantification of valve stiction by the method of unknown input estimation

Valve stiction is one of the most common causes of oscillations in industrial process control loops. Such oscillations can degrade the overall performance of the loop and eventually the final product quality. The detection and quantification of valve stiction in industrial process control loops is thus important. From previous studies in the literature, a sticky valve has been shown to have a distinct signature of the stiction phenomena in its valve positioner data. However, the position of the modulating control valves is seldom available. We consider the problem of estimating the valve position as an unknown input estimation problem. In this work, we propose a novel application of the unknown input estimator in order to estimate the valve position given the process model and the data of the process variable and controller output. Using the estimated valve position, we can detect and also quantify the amount of stiction. We demonstrate the efficacy of the method through simulation examples where a sticky valve is deliberately introduced in the closed loop using a two-parameter stiction model available in the literature. Application of the proposed methodology to a laboratory scale flow control loop is presented. An industrial case study is also presented in which the algorithm accurately detects and quantifies stiction in the level control loop of a power plant.     

Stacking graphic elements to avoid over-plotting

An ongoing challenge for information visualization is how to deal with over-plotting forced by ties or the relatively limited visual field of display devices. A popular solution is to represent local data density with area (bubble plots, treemaps), color (heatmaps), or aggregation (histograms, kernel densities, pixel displays). All of these methods have at least one of three deficiencies:1) magnitude judgments are biased because area and color have convex downward perceptual functions, 2) area, hue, and brightness have relatively restricted ranges of perceptual intensity compared to length representations, and/or 3) it is difficult to brush or link to individual cases when viewing aggregations. In this paper, we introduce a new technique for visualizing and interacting with datasets that preserves density information by stacking overlapping cases. The overlapping data can be points or lines or other geometric elements, depending on the type of plot. We show real-dataset applications of this stacking paradigm and compare them to other techniques that deal with over-plotting in high-dimensional displays.