Use of Phosphorylated Chitosan/Alumina Nanoadditives for Polymer Performance Improvement.

In this research, a new generation of ternary nanocomposites based on poly(ethylene terephthalate) (PET), phosphorylated chitosan and surface modified alumina nanoparticles were fabricated in four steps. The phosphorylation process was targeted for the insertion of elemental phosphorus as a flame retardant agent in the final PET nanocomposite. Likewise, environmentally friendly nano-alumina was used for PET matrix to improve the flame retardant properties of PET in collaboration with elemental phosphorus. Alternatively, the presence of alumina nanoparticles in combination with phosphorylated chitosan improved the antibacterial activity of the PET matrix. Furthermore, the effects of the phosphorylated chitosan and alumina nanoparticles on the morphology and thermal properties of nanocomposites were inspected by different approaches. The structure and distribution of the nanoscale particles in PET were analyzed by scanning electron microscopy. In addition, differential scanning calorimetry and thermogravimetric analyses were used for the in-depth evaluation of the thermal properties of prepared nanocomposites.

Characterisation, Performance and Optimisation of Nanocellulose Metalworking Fluid (MWF) for Green Machining Process

The present research attempts to develop a hybrid coolant by mixing alumina nanoparticles with cellulose nanocrystal (CNC) into ethylene glycol-water (60:40) and investigate the viability of formulated hybrid nanocoolant (CNC-Al2O3-EG-Water) towards enhancing the machining behavior. The two-step method has been adapted to develop the hybrid nanocoolant at various volume concentrations (0.1, 0.5, and 0.9%). Results indicated a significant enhancement in thermal properties and tribological behaviour of the developed hybrid coolant. The thermal conductivity improved by 20-25% compared to the metal working fluid (MWF) with thermal conductivity of 0.55 W/m℃. Besides, a reduction in wear and friction coefficient was observed with the escalation in the nanoparticle concentration. The machining performance of the developed hybrid coolant was evaluated using Minimum Quantity Lubrication (MQL) in the turning of mild steel. A regression model was developed to assess the deviations in the tool flank wear and surface roughness in terms of feed, cutting speed, depth of the cut, and nanoparticle concentration using Response Surface Methodology (RSM). The mathematical modeling shows that cutting speed has the most significant impact on surface roughness and tool wear, followed by feed rate. The depth of cut does not affect surface roughness or tool wear. Surface roughness achieved 24% reduction, 39% enhancement in tool length of cut, and 33.33% improvement in tool life span. From this, the surface roughness was primarily affected by spindle cutting speed, feed rate, and then cutting depth while utilising either conventional water or composite nanofluid as a coolant. The developed hybrid coolant manifestly improved the machining behaviour.

Denture base adaptation, retention, and mechanical properties of BioHPP versus nano-alumina-modified polyamide resins

Background. Continuous development of denture base materials has led to the introduction of innovative alternatives to polymethyl methacrylate. The present study aimed to evaluate the mechanical properties, adaptation, and retention of alumina nanoparticles (Al2 O3 NPs) modified polyamide resin versus BioHPP (high-performance polymer) denture base materials. Methods. Four groups of specimens, one control (group I) (unmodified polyamide) and two groups (groups II and III) (2.5 and 5 wt% Al2 O3 NP-modified polyamide, respectively) versus BioHPP specimen group (group IV), were tested for surface microhardness and flexural strength. Complete dentures fabricated from 5 wt% Al2 O3 NP-modified polyamide resin and BioHPP were used to evaluate denture base adaptation and retention. Results. The higher concentration in the alumina NP-modified polyamide group (5 wt%) demonstrated significantly higher flexural strength values and insignificantly higher hardness values than the lower concentration (2.5 wt%). There was a significant increase in the BioHPP group in both flexural strength and surface hardness compared to all polyamide groups. A statistically insignificant difference was observed between the two denture base materials regarding mean misfit values of the calculated total tissue surface area and four of the total seven evaluated areas. Satisfactory and comparable retention values were observed for both denture base materials. Conclusion. BioHPP and Al2 O3 NP-modified polyamide resin could be used as a promising alternative denture base material with good adaptation, retention, and mechanical properties.

An Optimized Multilayer Perceptrons Model Using Grey Wolf Optimizer to Predict Mechanical and Microstructural Properties of Friction Stir Processed Aluminum Alloy Reinforced by Nanoparticles

In the current investigation, AA2024 aluminum alloy is reinforced by alumina nanoparticles using a friction stir process (FSP) with multiple passes. The mechanical properties and microstructure observation are conducted experimentally using tensile, microhardness, and microscopy analysis methods. The impacts of the process parameters on the output responses, such as mechanical properties and microstructure grain refinement, were investigated. The effect of multiple FSP passes on the grain refinement, and various mechanical properties are evaluated, then the results are conducted to train a hybrid artificial intelligence predictive model. The model consists of a multilayer perceptrons optimized by a grey wolf optimizer to predict mechanical and microstructural properties of friction stir processed aluminum alloy reinforced by alumina nanoparticles. The inputs of the model were rotational speed, linear processing speed, and number of passes; while the outputs were grain size, aspect ratio, microhardness, and ultimate tensile strength. The prediction accuracy of the developed hybrid model was compared with that of standalone multilayer perceptrons model using different error measures. The developed hybrid model shows a higher accuracy compared with the standalone model.

Fresh, Mechanical, and Durability Properties of Self-Compacting Mortar Incorporating Alumina Nanoparticles and Rice Husk Ash

This paper presents a comprehensive evaluation on self-compacting (SC) mortars incorporating 0, 1, 3, and 5% alumina nanoparticles (NA) as well as 0% and 30% rice husk ash (RHA) used as Portland cement replacement. To evaluate the workability, mechanical, and durability performance of SC mortars incorporating NA and RHA, the fresh properties (slump flow diameter and V-funnel flow time), hardened properties (compressive strength, flexural strength, and ultrasonic pulse velocity), and durability properties (water absorption, rapid chloride permeability, and electrical resistivity) were determined. The results indicated that the addition of NA and RHA has negligible effect on the workability and water absorption rate of the SC mortars. However, significant compressive and flexural strength development was observed in the SC mortars treated with NA or the combination of NA and RHA. The introduction of RHA and NA also reduced the rapid chloride permeability and enhanced the electrical resistivity of the SC mortars significantly. It is concluded that the coexistence of 30% RHA and 3% NA as cement replacement in SC mortars can provide the best mechanical and durability performance.