Experimental analysis on cutting fluid dispersed with silver nano particles

Cutting fluids play a significant role in machining operations, impact shop productivity, tool life and quality of work. The reduction in the consumption rate of the cutting fluid leads to the minimization of production cost and environmental hazards. This could be achieved by the enhancement of its thermal and tribological properties with the inclusion of suitable additives in the cutting fluid. In recent years various nanoparticles were used as additives in the conventional cutting fluid to enhance its properties. In the present work, silver nanoparticles was synthesized, characterized, dispersed in cutting fluid and experimented in a turning operation. Heat carrying capacities of the cutting fluid, cutting forces during machining process and surface finish of the work piece were assessed by suitable instruments for cutting fluids with and without silver nanoparticles under different machining conditions. From the experimental results, it was observed that inclusion of silver nanoparticles in cutting fluid showed a significant reduction in tool tip temperature, cutting force and surface roughness of the work piece.     

Comparison of individual and combined ANN models for prediction of air and dew point temperature

Predicted air and dew point temperatures can be valuable in decision making in many areas including protecting crops from damage, avoiding heat stress on animals and humans, and in planning related to energy management. Current web-based artificial neural network (ANN) models on the Automated Environment Monitoring Network (AEMN) in Georgia predict hourly air and dew point temperature for twelve prediction horizons, using 24 models. The observed air temperature may approach the observed dew point temperature, but never goes below it. Current web based ANN models have prediction errors which, when the air and dew point temperatures are close, may cause air temperature to be predicted below the dew point temperature. Herein this error is referred to as a prediction anomaly. The goal of this research was to improve the prediction accuracy of existing air and dew point temperature ANN models by combining the two weather variables into a single ANN model for each prediction horizon. The objectives of this study were to reduce the mean absolute error (MAE) of prediction and to reduce the number of prediction anomalies. The combined models produced a reduction in the air temperature MAE for ten of twelve prediction horizons with an average reduction in MAE of 1.93 %. The combined models produced a reduction in the dew point temperature MAE for only six of twelve prediction horizons with essentially no average decrease in MAE. However, the combined models showed a marked reduction in prediction anomalies for all twelve prediction horizons with an average reduction of 34.1 %. The reduction in prediction anomalies ranged from 4.6 % at the one-hour horizon to 60.5 % at the eleven-hour horizon.     

Synthesis of Cu-10Sn/SiC Metal Matrix Composites and Experimental Investigation of its Adhesive Wear Behaviour

Silicon - The objective is to fabricate Cu-10Sn alloy and its composites reinforced with varying wt% of SiC (5, 10 and 15) to investigate its dry sliding wear behaviour. Microstructural analysis...     

Antifungal Activity of the Lipophilic Antioxidant Ferrostatin-1

Ferrostatin-1 (Fer-1) is a lipophilic antioxidant that effectively blocks ferroptosis, a distinct non-apoptotic form of cell death caused by lipid peroxidation. During many infections, both pathogens and host cells are subjected to oxidative stress, but the occurrence of ferroptosis had not been investigated. We examined ferroptosis in macrophages infected with the pathogenic yeast Histoplasma capsulatum. Unexpectedly, Fer-1 not only reduced the death of macrophages infected in vitro, but inhibited the growth of H. capsulatum and related species Paracoccidioides lutzii and Blastomyces dermatitidis at concentrations under 10 μm . Other antioxidant ferroptosis inhibitors, including liproxstatin-1, did not prevent fungal growth or reduce macrophage death. Structural analysis revealed a potential similarity of Fer-1 to inhibitors of fungal sterol synthesis, and ergosterol content of H. capsulatum decreased more than twofold after incubation with Fer-1. Strikingly, additional Fer-1 analogues with slight differences from Fer-1 had limited impact on fungal growth. In conclusion, the ferroptosis inhibitor Fer-1 has unexpected antifungal potency distinct from its antiferroptotic activity.     

One-pot dual product synthesis of hierarchical Co3O4@N-rGO for supercapacitors,N-GDs for label-free detection of metal ion and bio-imaging applications

Cobalt oxide nanoparticles@nitrogen-doped reduced graphene oxide (Co₃O₄@N-rGO) composite and nitrogen-doped graphene dots (N-GDs) were synthesized by a one-pot simple hydrothermal method. The average sizes of the synthesized bare cobalt oxide nanoparticles (Co₃O₄ NPs) and Co₃O₄ NPs in the Co₃O₄@N-rGO composite were around 22 and 24 nm, respectively with an interlayer distance of 0.21 nm, as calculated using the XRD patterns. The Co₃O₄@N-rGO electrode exhibits superior capacitive performance with a high capability of about 450 F g^?1 at a current density of 1 A g^?1 and has excellent cyclic stability, even after 1000 cycles of GCD at a current density of 4 A g^?1. The obtained N-GDs exhibited high sensitivity and selectivity towards Fe²⁺ and Fe³⁺, the limit of detection was as low as 1.1 and 1.0 μM, respectively, representing high sensitivity to Fe²⁺ and Fe³⁺. Besides, the N-GDs was applied for bio-imaging. We found that N-GDs were suitable candidates for differential staining applications in yeast cells with good cell permeability and localization with negligible cytotoxicity. Hence, N-GDs may find dual utility as probes for the detection of cellular pools of metal ions (Fe³⁺/Fe²⁺) and also for early detection of opportunistic yeast infections in biological samples.     

A Review of Tunnel Field-Effect Transistors for Improved ON-State Behaviour

Silicon - Tunnel Field-effect transistor (TFET) is regarded as the most promising candidate which can possibly replace the traditional MOSFET from current IC technology. It has gained much...     

A systematic review on selection characterization and implementation of probiotics in human health

Food Science and Biotechnology - Probiotics are live bacteria found in food that assist the body's defence mechanisms against pathogens by reconciling the gut microbiota. Probiotics are...     

Mechanical Characterization of Near-Isotropic Inconel 718 Fabricated by Laser Powder-Bed Fusion

Metallurgical and Materials Transactions A - The characterization of Ni-based superalloy Inconel® 718 fabricated by powder-based laser fusion process was performed to study microstructural...     

Improved Load-Balanced Clustering for Energy-Aware Routing (ILBC-EAR) in WSNs

Sensors are considered as important elements of electronic devices. In many applications and service, Wireless Sensor Networks (WSNs) are involved in significant data sharing that are delivered to the sink node in energy efficient manner using multi-hop communications. But, the major challenge in WSN is the nodes are having limited battery resources, it is important to monitor the consumption rate of energy is very much needed. However, reducing energy consumption can increase the network lifetime in effective manner. For that, clustering methods are widely used for optimizing the rate of energy consumption among the sensor nodes. In that concern, this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing (ILBC-EAR), which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member nodes. For providing equal rate of energy consumption among nodes, the dimensions of framed clusters are measured. Moreover, the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base station. The evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head node. Additionally, the model also provides higher throughput and minimal delay in delivering data packets.