Synthesis and characterization of iron oxide nanoparticles by solvothermal method

In the present work iron oxide nanoparticles have been synthesized by alkaline solvo thermal method using anhydrous ferric chloride, sodium hydroxide, polyethylene glycol and cetyl trimethyl ammonium bromide and characterized by X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM), Energy-dispersive X-ray Spectroscopy (EDX) and Thermal Gravimetric Analysis (TGA). XRD indicated that the product is a mixture of different phases of iron oxide viz. gamma-Fe₂O₃ (maghemite, tetragonal), Fe₂O₃ (maghemite, cubic), Fe₃O₄ (magnetite, cubic) and ?-Fe₂O₃(epsilon iron oxide). FESEM studies indicated that size of the particles is observed in the range of about 19.8 nm to 48 nm. EDX spectral analysis reveals the presence of carbon, oxygen, iron in the synthesized nanoparticles. The FTIR spectra indicated absorption bands due to O-H stretching, C-O bending, N-H stretching and bending, C-H stretching and Fe-O stretching vibrations. TGA curve represented weight loss of around 3.0446 % in the sample at temperature of about 180°C due to the elimination of the water molecules absorbed by the nanoparticles from the atmosphere.     

Potential use,mechanical and thermal studies of sabai grass fibre

Journal of Materials Science Letters -     

Sintering behaviour of pyrophyllite mineral: effect of some alkali and alkaline-earth metal carbonates

Pyrophyllite mineral was heat treated with alkali and alkaline-earth metal carbonates in equimolar ratios at 1000 °C for 2 h and the various phases formed in the sintered products were investigated using X-ray powder diffraction and infrared spectroscopy. The morphology of the products was studied using scanning electron microscopy. In each case formation of aluminosilicate phase of respective cation was observed, but the presence of mullite in sintered products was not detected.     

Investigation on gradient dielectric characteristics of bamboo (Dendrocalamus strictus)

Dielectric constant (ε′) and tan δ dependence on distance from the outer most skin to the center of bamboo has been determined. Dielectric measurements have been done in the temperature range from 24 to 120°C and in the frequency range from 4 to 100 kHz. Gradient behavior in dielectric constant (ε′) and tan δ has been found in bamboo. It has also been observed that the dielectric constant (ε′) and tan δ increase with the increasing temperature and decrease with the increasing frequency. Relaxation times have been calculated for the four samples at 80, 90, and 100°C temperatures, which show that relaxation time decreases with the increase of temperature due to the increased molecular mobility. A continuous increase in the hardness from the center 48 to the outer surface 70 and density from 0.45 to 0.80 g/cc has been observed. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 3489–3494, 2006     

Role of PET in improving wear properties of PP in dry sliding condition

The sliding wear of isotactic polypropylene (PP), polyethylene terephthalate (PET) and their blends was evaluated as a function of applied pressure and composition against a stainless steel counter face in dry condition. Wear rate decreases with the addition of PET in the blend. The wear was observed in two stages, the moderate wear and high wear while increasing the applied pressure on test samples. The addition of PET in PP helps in increasing the limit of moderate wear towards the high-pressure side. Microstructure and worn surfaces of samples were observed by scanning electron microscope. The wear phenomenon has been discussed based on wear losses and worn surfaces.     

Study of Structural and Mechanical Properties of Al/Nano-Al2O3 Metal Matrix Nanocomposite Fabricated by Powder Metallurgy Method

In this work, the (1???x) Al–xAl₂O₃ (x?=?0, 1, 2, 3, and 4 wt%) of metal matrix nanocomposites (MMNCs) has been manufactured using the powder metallurgy technique. Aluminium metal powder (Al) was used as the matrix material, and alumina nanoparticles (Al₂O₃) synthesized by the sol–gel method were used as the reinforcing material to produce the MMNCs. Two phases of Al₂O₃ have been identified, i.e. the α-phase (rhombohedral structure) and the δ-phase (orthorhombic structure) by X-ray diffraction patterns (XRD) of synthesized Al₂O₃ nanoparticles with an average crystallite size of 31.33 nm. The average particle size of the Al₂O₃ nanoparticle is obtained as 39.6 nm. The XRD patterns of the Al–Al₂O₃ nanocomposites contain the Al and Al₂O₃ peaks that confirm the development of the MMNC without any solid-state reaction during the manufacturing process. FESEM micrographs show an almost uniform distribution of Al₂O₃ particles in the Al metal matrix. The reinforcement of the Al₂O₃ nanoparticles in the Al metal matrix has shown an improvement in hardness by increasing the wt% of Al₂O₃ in Al matrix, and a maximum 24.8% improvement in hardness is observed for 4 wt% Al₂O₃ sample. An increase in wear rate is observed with the increasing wt% of Al₂O₃ in the Al metal matrix in Al–Al₂O₃ nanocomposite. The addition of Al₂O₃ nanoparticles in the Al matrix has resulted in improved corrosion performance of the samples with a maximum corrosion resistance efficiency of 85.6% for 4 wt% Al₂O₃ in Al metal matrix.

     

A fuzzy reinforcement learning approach to thermal unit commitment problem

Unit commitment problem (UCP) aims at optimizing generation cost for meeting a given load demand under several operational constraints. We propose to use fuzzy reinforcement learning (RL) approach for efficient and reliable solution to the unit commitment problem. In particular, we cast UCP as a multiagent fuzzy reinforcement learning task wherein individual generators act as players for optimizing the cost to meet a given load over a twenty-four-hour period. Unit commitment task has been fuzzified, and the most optimal unit commitment solution is generated by employing RL on this fuzzy multigenerator setup. Our proposed multiagent RL framework does not assume any a priori task or system knowledge, and the generators gradually learn to produce most optimal output solely based on their collective generation. We look at the UCP as a sequential decision-making task with reward/penalty to reduce the collective generation cost of generators. To the best of our knowledge, ours is a first attempt at solving UCP by employing fuzzy reinforcement learning. We test our approach on a ten-generating-unit system with several equality and inequality constraints. Simulation results and comparisons against several recent UCP solution methods prove superiority and viability of our proposed multiagent fuzzy reinforcement learning technique.