Application of response surface methodology and central composite rotatable design for modeling and optimization of sulfuric leaching of rutile containing slag and ilmenite

In this study, application of the Response Surface Methodology and the Central Composite Design (CCD) technique for modeling and optimization of the influence of several operating variables on titanium recovery in a leaching process were investigated. The four main leaching parameters, namely temperature, acid concentration, leaching time and solid to liquid ratio, were changed during-the leaching experiments based on the CCD. A total of 30 leaching experiments were designed and carried out in the CCD method according to software-based designed matrix. According to the results, i.e., titanium recoveries with these four parameters as well as empirical model equations were developed. The model equations were then individually optimized by using quadratic programming to maximize titanium recoveries for both ilmenite and slag within the experimental range. The predicted values for titanium recoveries for both ilmenite and slag were found to be in a reasonable agreement with the experimental values, with R ² as correlation factor being 0.963 and 0.916 for ilmenite and slag, respectively.     

Development of solution-gated graphene transistor model for biosensors

The distinctive properties of graphene, characterized by its high carrier mobility and biocompatibility, have stimulated extreme scientific interest as a promising nanomaterial for future nanoelectronic applications. In particular, graphene-based transistors have been developed rapidly and are considered as an option for DNA sensing applications. Recent findings in the field of DNA biosensors have led to a renewed interest in the identification of genetic risk factors associated with complex human diseases for diagnosis of cancers or hereditary diseases. In this paper, an analytical model of graphene-based solution gated field effect transistors (SGFET) is proposed to constitute an important step towards development of DNA biosensors with high sensitivity and selectivity. Inspired by this fact, a novel strategy for a DNA sensor model with capability of single-nucleotide polymorphism detection is proposed and extensively explained. First of all, graphene-based DNA sensor model is optimized using particle swarm optimization algorithm. Based on the sensing mechanism of DNA sensors, detective parameters (I_ds and V_gmin) are suggested to facilitate the decision making process. Finally, the behaviour of graphene-based SGFET is predicted in the presence of single-nucleotide polymorphism with an accuracy of more than 98% which guarantees the reliability of the optimized model for any application of the graphene-based DNA sensor. It is expected to achieve the rapid, quick and economical detection of DNA hybridization which could speed up the realization of the next generation of the homecare sensor system.     

Analytical modeling of trilayer graphene nanoribbon Schottky-barrier FET for high-speed switching applications

Recent development of trilayer graphene nanoribbon Schottky-barrier field-effect transistors (FETs) will be governed by transistor electrostatics and quantum effects that impose scaling limits like those of Si metal-oxide-semiconductor field-effect transistors. The current–voltage characteristic of a Schottky-barrier FET has been studied as a function of physical parameters such as effective mass, graphene nanoribbon length, gate insulator thickness, and electrical parameters such as Schottky barrier height and applied bias voltage. In this paper, the scaling behaviors of a Schottky-barrier FET using trilayer graphene nanoribbon are studied and analytically modeled. A novel analytical method is also presented for describing a switch in a Schottky-contact double-gate trilayer graphene nanoribbon FET. In the proposed model, different stacking arrangements of trilayer graphene nanoribbon are assumed as metal and semiconductor contacts to form a Schottky transistor. Based on this assumption, an analytical model and numerical solution of the junction current–voltage are presented in which the applied bias voltage and channel length dependence characteristics are highlighted. The model is then compared with other types of transistors. The developed model can assist in comprehending experiments involving graphene nanoribbon Schottky-barrier FETs. It is demonstrated that the proposed structure exhibits negligible short-channel effects, an improved on-current, realistic threshold voltage, and opposite subthreshold slope and meets the International Technology Roadmap for Semiconductors near-term guidelines. Finally, the results showed that there is a fast transient between on-off states. In other words, the suggested model can be used as a high-speed switch where the value of subthreshold slope is small and thus leads to less power consumption.     

An investigation on the photocatalytic activity of sub-stoichiometric TiO2-x coatings produced by suspension plasma spray

To enhance the photocatalytic activity, sub-stoichiometric TiO_2-x films were coated on stainless steel substrates by Suspension Plasma Spraying. Because the TiO₂ particles are exposed to high temperature during deposition by plasma spray, TiO_2-x coating are typically produced. To achieve different levels of oxygen vacancies, as-sprayed TiO_2-x coatings were annealed at four different temperatures for 48 h in air. In this work, the degradation of methylene blue was performed to evaluate the photocatalytic activity under visible light. The results indicated that oxygen vacancy positively affects the photocatalytic activity of TiO_2-x by introducing some energy levels into the bandgap of titania. Moreover, these energy levels could act as traps for photo-excited holes and electrons, reducing the recombination rate of charges, thus improving the photocatalytic activity under the visible lamp. Additionally, coatings were analyzed by X-ray diffraction, confocal laser microscopy, scanning electron microscopy, Raman spectroscopy, thermogravimetric analysis, Fourier-transform infrared spectroscopy, and UV–vis spectroscopy.     

Analytical modelling of monolayer graphene-based ion-sensitive FET to pH changes

Graphene has attracted great interest because of unique properties such as high sensitivity, high mobility, and biocompatibility. It is also known as a superior candidate for pH sensing. Graphene-based ion-sensitive field-effect transistor (ISFET) is currently getting much attention as a novel material with organic nature and ionic liquid gate that is intrinsically sensitive to pH changes. pH is an important factor in enzyme stabilities which can affect the enzymatic reaction and broaden the number of enzyme applications. More accurate and consistent results of enzymes must be optimized to realize their full potential as catalysts accordingly. In this paper, a monolayer graphene-based ISFET pH sensor is studied by simulating its electrical measurement of buffer solutions for different pH values. Electrical detection model of each pH value is suggested by conductance modelling of monolayer graphene. Hydrogen ion (H⁺) concentration as a function of carrier concentration is proposed, and the control parameter (Ƥ) is defined based on the electro-active ions absorbed by the surface of the graphene with different pH values. Finally, the proposed new analytical model is compared with experimental data and shows good overall agreement.