In this study, an innovative route for secondary recycling (with zirconium oxide (ZrO2) reinforcement) has been proposed based on melt processing of high-density polyethylene (HDPE) in low-temperature bearing applications. Initially, secondary recycled HDPE, acrylonitrile butadiene styrene, and nylon 6 thermoplastic composites were investigated for melt flow index (MFI) according to ASTM D1238 standard. Based on the acceptable MFI, secondary recycled HDPE matrix was selected for second-stage processing on twin screw extrusion (TSE). The final process involves reinforcement of ZrO2 into HDPE matrix by TSE in 60:40 ratio (by weight %) for preparation of feedstock filament (for possible 3-D printing of bearings). The results of the study suggest that for processing of HDPE, 40% ZrO2 composite matrix, 50 r min−1 screw speed, 190°C barrel temperature, and 15 kg applied load are the best setting of TSE (for maximizing the tensile strength of feedstock filament). The results are also supported by wear properties, thermal stability, and morphological analysis (based on scanning electron microscopy and electron-dispersive X-ray analysis).
We report the fabrication of low cost highly structured silver (Ag) capped aluminium (Al) nanorods (NRs) as surface enhanced Raman spectroscopy (SERS) substrate utilising the glancing angle deposition technique. The nano-capping of silver onto the Al NRs can concentrate the local electric field within the minimal volume that can serve as hotspots. The average size of the Ag nanocaps was 50 nm. The newly proposed nanoporous Ag capped Al NRs as SERS substrate could detect the Raman signal of rhodamine 6G (R6G) up to 10 −15 molar concentration. The significant enhancement in the Raman signal of 10 7 was achieved for Ag capped Al NRs considering R6G as a probe molecule. Using the developed SERS substrate, we recorded Raman spectra for Escherichia coli bacteria with its concentration varying from 10 8 colony forming units per ml (CFU ml −1 ) up to 10 2 CFU ml −1 . All the reported Raman spectra were acquired by a portable handheld Raman spectrometer. Hence, this newly proposed low cost, effective SERS substrate can be used commercially for the onsite detection of clinical pathogens. The 3D finite difference time domain simulation model was performed for Ag capped Al nanostructure to understand the generation of hotspots. The simulated results show excellent agreement with the experimental results. We fabricated uncapped Ag nanorods of similar dimensions and performed the experimental measurements and simulations for comparison. We found a significant enhancement in Ag capped Al NRs compared to the long Ag NRs. The description of the Raman signal enhancement has been elaborated.
Rain gauge network is important for collecting rainfall information effectively and efficiently. Rain gauge networks have been studied for several decades from a range of hydrological perspectives, where rain gauges with unique or non-repeating information are considered as important. However, the problem of quantification of node importance and subsequent identification of the most important nodes in rain gauge networks have not yet been extensively addressed in the literature. In this study, we use the concept of the complex networks to evaluate the Indian Meteorological Department (IMD) monitored 692 rain gauge in the Ganga River Basin. We consider the complex network theory-based Degree Centrality (DC), Clustering Coefficient (CC) and Mutual Information (MI) as the parameters to quantify the rainfall variability associated with all the rain gauges in the network. Multiple rain gauge network scenario with varying rain gauge density (i.e. Network Size (NS) = 173, 344, 519, and 692) and Temporal Resolution (i.e. TR = 3 hours, 1 day, and 1 month) are introduced to study the effect of rain gauge density, gauge location and temporal resolution on the node importance quantification. Proxy validation of the methodology was done using a hydrological model. Our results indicate that the network density and temporal resolution strongly influence a node’s importance in rain gauge network. In addition, we concluded that the degree centrality along with clustering coefficient is the preferred parameter than the mutual information for the node importance quantification. Furthermore, we observed that the network properties (spatial distribution, DC, Collapse Correlation Threshold (CCT), CC Range distributions) associated with TR = 3 hours and 1 day are comparable whereas TR = 1 month exhibit completely different trends. We also found that the rain gauges situated at high elevated areas are extremely important irrespective of the NS and TR. The encouraging results for the quantification of nodes importance in this study seem to indicate that the approach has the potential to be used in extreme rainfall forecasting, in studying changing rainfall patterns and in filling gaps in spatial data. The technique can be further helpful in the ground-based observation network design of a wide range of meteorological parameters with spatial correlation.
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