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Barium titanate (BT) perovskite particles were surface modified by means of mechanical treatment and used as inorganic component in polyvinylidene fluoride (PVDF) based composites. The changes in electrical properties of the composite films with increasing in filler content were followed by dielectric spectroscopy, breakdown strength andD-Emeasurements. A comparison of the properties of the composites prepared with untreated and mechanically activated particles revealed that there is a significant difference in their performances at low filler concentrations (<20 wt%). Introduction of the surface modified ceramic particles into PVDF matrix led to an increase of the dielectric constant without affecting significantly the electrical breakdown strength. In contrast, when as received BT particles were used a filler, both dielectric constants and breakdown strengths of the composite films were lower than the corresponding values observed for the pure PVDF. At higher concentrations, however, the influence of pre-treatment of the filler on the effective electrical properties becomes less significant. The obtained results were discussed in terms of the pronounced crystallization of polarβandγcrystal phases of PVDF in the presence of surface modified BT fillers, which is confirmed by Raman spectroscopy.

 

Electronic and excitonic states in anInSbstrongly flattened ellipsoidal quantum dot (QD) with complicated dispersion law are theoretically investigated within the framework of the geometric adiabatic approximation in the strong, intermediate, and weak quantum confinement regimes. For the lower levels of the spectrum, the square root dependence of energy on QD sizes is revealed in the case of Kane’s dispersion law. The obtained results are compared to the case of a parabolic (standard) dispersion law of charge carriers. The possibility of the accidental exciton instability is revealed for the intermediate quantum confinement regime. For the weak quantum confinement regime, the motion of the exciton's center-of-gravity is quantized, which leads to the appearance of additional Coulomb-like sub-levels. It is revealed that in the case of the Kane dispersion law, the Coulomb levels shift into the depth of the forbidden band gap, moving away from the quantum confined level, whereas in the case of the parabolic dispersion law, the opposite picture is observed. The corresponding selection rules of quantum transitions for the interband absorption of light are obtained. New selection rules of quantum transitions between levels conditioned by 2D exciton center of mass vertical motion quantization in a QD are revealed. The absorption threshold behavior characteristics depending on the QDs geometrical sizes are also revealed.

 

In this paper, we study the localization of an electron in a binary quantum system formed by a pair of quantum dots (QDs). The traditional theoretical consideration of such systems is limited to the symmetrical case when QDs in such double quantum dot (DQD) are assumed identical in all respects. In this paper, we model the effects of breaking QD similarities in a DQD by studying two-dimensional (2D) DQDs as a double quantum well (DQW). This is done by solving the Schrödinger equation, with parameters chosen to describe an InAs/GaAs heterostructure. We calculate the energy spectrum of the electron confinement and the spectral distribution of localized/delocalized spatial states. Both symmetric and asymmetric QW shapes are considered and their effects are compared. The effects of symmetry breaking are explained within the framework of the two-level system theory. We delineate the QW weak and strong coupling cases in DQW. In particular, we show that the coherence in ideal DQW is unstable in the case of a weak QW coupling. Within the framework of the proposed approach, a charge qubit realized on a DQD is discussed and, as an example, a qubit based on an almost ideal DQD is proposed.

 

BaTiO 3 (BTO) is considered the most commonly used ceramic material in multilayer ceramic capacitors due to its desirable dielectric properties. Considering that the miniaturization of electronic devices represents an expanding field of research, modification of BTO has been performed to increase dielectric constant and DC bias characteristic/sensitivity. This research presents the effect of N 2 and air atmospheres on morphological and dielectric properties of BTO nanoparticles modified with organometallic salt at sintering temperatures of [Formula: see text]C, [Formula: see text]C, [Formula: see text]C, and [Formula: see text]C. Measured dielectric constants were up to 35,000, with achieved very high values in both atmospheres. Field emission scanning electron microscopy (FESEM) was used for morphological characterization, revealing a porous structure in all the samples. The software image analysis of FESEM images showed a connection between particle and pore size distribution, as well as porosity. Based on the data from the image analysis, the prediction of dielectric properties in relation to morphology indicated that yttrium-based organometallic salt reduced oxygen vacancy generation in N 2 atmosphere. DC bias sensitivity measurements showed that samples with higher dielectric constant had more pronounced sensitivity to voltage change, but most of the samples were stable up to 100 V, making our modified BTO a promising candidate for capacitors. 

The rise of innovation in the electrical industry is driven by the controlled design of new materials. The hybrid materials based on magnetite/nanocellulose are highly interesting due to their various applications in medicine, ecology, catalysis and electronics. In this study, the structure and morphology of nanocellulose/magnetite hybrid nanomaterials were investigated. The effect of nanocellulose loading on the crystal structure of synthesized composites was investigated by XRD and FTIR methods. The presented study reveals that the interaction between the cellulose and magnetic nanoparticles depends on the nanocellulose content. Further, a transition from cellulose II to cellulose I allomorph is observed. SEM and EDS are employed to determine the variation in morphology with changes in component concentrations. By the calculation of magnetic interactions between adjacent Fe3+ and Fe2+ ions within composites, it is determined that ferromagnetic coupling predominates. 

Biophysical and condensed matter systems connection is of great importance nowadays due to the need for a new approach in microelectronic biodevices, biocomputers or biochips advanced development. Considering that the living and nonliving systems’ submicroparticles are identical, we can establish the biunivocally correspondent relation between these two particle systems, as a biomimetic correlation based on Brownian motion fractal nature similarities, as the integrative property. In our research, we used the experimental results of bacterial motion under the influence of energetic impulses, like music, and also some biomolecule motion data. Our goal is to define the relation between biophysical and physical particle systems, by introducing mathematical analytical forms and applying Brownian motion fractal nature characterization and fractal interpolation. This work is an advanced research in the field of new solutions for high-level microelectronic integrations, which include submicrobiosystems like part of even organic microelectronic considerations, together with some physical systems of particles in solid-state solutions as a nonorganic part. Our research is based on Brownian motion minimal joint properties within the integrated biophysical systems in the wholeness of nature. 

Many recently published research papers examine the representation of nanostructures and biomimetic materials, especially using mathematical methods. For this purpose, it is important that the mathematical method is simple and powerful. Theory of fractals, artificial neural networks and graph theory are most commonly used in such papers. These methods are useful tools for applying mathematics in nanostructures, especially given the diversity of the methods, as well as their compatibility and complementarity. The purpose of this paper is to provide an overview of existing results in the field of electrochemical and magnetic nanostructures parameter modeling by applying the three methods that are “easy to use”: theory of fractals, artificial neural networks and graph theory. We also give some new conclusions about applicability, advantages and disadvantages in various different circumstances. 

The particles in condensed matter physics are almost characterized by Brownian motion. This phenomenon is the basis for a very important understanding of the particles motion in condensed matter. For our previous research, there is already applied and confirmed the complex fractal correction which includes influence of parameters from grains and pores surface and also effects based on particles’ Brownian motion. As a chaotic structure of these motions, we have very complex research results regarding the particles’ trajectories in three-dimension (3D). In our research paper, we applied fractal interpolation within the idea to reconstruct the above mentioned trajectories in two dimensions at this stage. Because of the very complex fractional mathematics on Brownian motion, we found and developed much simpler and effective mathematization. The starting point is within linear interpolation. In our previous research, we presented very original line fractalization based on tensor product. But, in this paper, we applied and successfully confirmed that by fractal interpolation (Akimo polynomial method) that is possible to reconstruct the chaotical trajectories lines structures by several fractalized intervals and involved intervals. This novelty is very important because of the much more effective procedure that we can reconstruct and in that way control the particles’ trajectories. This is very important for further advanced research in microelectronics, especially inter-granular micro capacitors. 

The world energy crisis necessitated the cause of the research interest into new, renewable and alternative energy sources. From this point of view, there is research on phenomena and different synthetic methods and on structure and electronic property optimization expressed by important material and device advancement. Efficiency and electricity generation (batteries, fuel cells, hydrogen energy) are nowadays actual questions. Because of that research, innovations and applications require extended knowledge by fractal nature characterization. The electrochemical energy sources solutions, especially electrolytes, are in fractal nature science focus. Based on the research novelties, especially electronic materials, we presented an investigation on fractal structure influence in electrochemistry. We explore the activation energy and fundamental thermodynamic functions and values, also the electrode surface changed by complex fractal correction through fractal dimension of grains and pores, and Brownian motion of involved particles, as well. At the end, the electrochemical Arrhenius and Butler–Volmer equation fractalization is applied. All of these open new perspectives for electrochemical energy processes, within electrolyte bulk and related electrodes and more precise energy generation. This is important for semiconductor processing in solar cells and devices. So, we included the knowledge of fractal sciences advancement in this field for current–voltage equation. 

The materials’ consolidation, especially ceramics, is very important in advanced research development and industrial technologies. Science of sintering with all incoming novelties is the base of all these processes. A very important question in all of this is how to get the more precise structure parameters within the morphology of different ceramic materials. In that sense, the advanced procedure in collecting precise data in submicro-processes is also in direction of advanced miniaturization. Our research, based on different electrophysical parameters, like relative capacitance, breakdown voltage, and [Formula: see text], has been used in neural networks and graph theory successful applications. We extended furthermore our neural network back propagation (BP) on sintering parameters’ data. Prognosed mapping we can succeed if we use the coefficients, implemented by the training procedure. In this paper, we continue to apply the novelty from the previous research, where the error is calculated as a difference between the designed and actual network output. So, the weight coefficients contribute in error generation. We used the experimental data of sintered materials’ density, measured and calculated in the bulk, and developed possibility to calculate the materials’ density inside of consolidated structures. The BP procedure here is like a tool to come down between the layers, with much more precise materials’ density, in the points on morphology, which are interesting for different microstructure developments and applications. We practically replaced the errors’ network by density values, from ceramic consolidation. Our neural networks’ application novelty is successfully applied within the experimental ceramic material density [Formula: see text] [kg/m 3 ], confirming the direction way to implement this procedure in other density cases. There are many different mathematical tools or tools from the field of artificial intelligence that can be used in such or similar applications. We choose to use artificial neural networks because of their simplicity and their self-improvement process, through BP error control. All of this contributes to the great improvement in the whole research and science of sintering technology, which is important for collecting more efficient and faster results.