Fabrication and characteristics of black silicon for solar cell applications: An overview

Electrochemical deposition of ZnO nanostructures onto porous silicon and their enhanced gas sensing to NO2 at room temperature

CO2 gas sensing properties of DC reactive magnetron sputtered ZnO thin film

The traditional devices, used to measure the surface roughness, are very sensitive, and they are obtained by scratching the surface of materials. Therefore, the optic systems are used as alternatives to these devices to avoid the unwanted processes that damage the surface. In this study, face milling process was applied to American Iron and Steel Institute (AISI) 1040 carbon steel and aluminium alloy 5083 materials using the different tools, cutting speeds and depth of cuts. After these processes, surface roughness values were obtained by the surface roughness tester, and the machined surface images were taken using a polarise microscope. The obtained images were converted into binary images, and the images were used as input data to train network using the MATLAB neural network toolbox. For the training networks, log-sigmoid function was selected as transfer function, scaled conjugate gradient (SCG) algorithm was used as training algorithm, and performance of the trained networks was achieved as an average of 99.926 % for aluminium alloy (AA) 5083 aluminium and as an average of 99.932 % for AISI 1040 steel. At the end of the study, a prediction programme for optical surface roughness values using MATLAB m-file and GUI programming was developed. Then, the prediction programme and neural network performance were tested by the trial experiments. After the trial experiments, surface roughness values obtained with stylus technique for the carbon steel and aluminium alloy materials were compared with the developed programme values. When the developed programme values were compared with the experimental results, the results were confirmed each other at a rate of 99.999 %.

Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network

The precursor glass in the ZnO–Al2O3–B2O3–SiO2 (ZABS) system doped with Eu2O3 was prepared by the melt-quench technique. The transparent willemite, Zn2SiO4 (ZS) glass–ceramic nanocomposites were derived from this precursor glass by a controlled crystallization process. The formation of willemite crystal phase, size, and morphology with increase in heat-treatment time was examined by X-ray diffraction (XRD) and field-emission scanning electron microscopy (FESEM) techniques. The average calculated crystallite size obtained from XRD is found to be in the range 18–70 nm whereas the grain size observed in FESEM is 50–250 nm. The refractive index value is decreased with increase in heat-treatment time which is caused by the partial replacement of ZnO4 units of ZS nanocrystals by AlO4 units due to generation of vacancies. Fourier transform infrared (FTIR) reflection spectroscopy was used to evaluate its structural evolution. Vickers hardness study indicates marked improvement of hardness in the resultant glass-ceramics compared with its precursor glass. The photoluminescence spectra of Eu3+ ions exhibit emission transitions of 5D0→7Fj (j = 0, 1, 2, 3, and 4) and its excitation spectra show an intense absorption band at 395 nm. These spectra reveal that the luminescence performance of the glass–ceramic nanocomposites is enhanced up to 17-fold with the process of heat treatment. This enhancement is caused by partitioning of Eu3+ ions into glassy phase instead of into the willemite crystals with progress of heat treatment. Such luminescent glass–ceramic nanocomposites are expected to find potential applications in solid-state red lasers, phosphors, and optical display systems.

Thermal, Structural, and Enhanced Photoluminescence Properties of Eu3+-doped Transparent Willemite Glass–Ceramic Nanocomposites

Measurement of surface roughness of metals using binary speckle image analysis

White light luminescence from annealed thin ZnO deposited porous silicon

Organic vapor sensor using photoluminescence of laser ablated gold nanoparticles on porous silicon

A facile approach to improve the performance and stability of perovskite solar cells via FA/MA precursor temperature controlling in sequential deposition fabrication

Ag-metallization effects on optical and electrical properties of porous silicon

Ersin Kayahan

Papers

Measurement of surface roughness of metals using binary speckle image analysis

White light luminescence from annealed thin ZnO deposited porous silicon

Organic vapor sensor using photoluminescence of laser ablated gold nanoparticles on porous silicon

A facile approach to improve the performance and stability of perovskite solar cells via FA/MA precursor temperature controlling in sequential deposition fabrication

Ag-metallization effects on optical and electrical properties of porous silicon