Buffer influence on magnetic dead layer, critical current and thermal stability in magnetic tunnel junctions with perpendicular magnetic anisotropy

We present a thorough research on Ta/Ru-based buffers and their influence on features crucial from the point of view of applications of MTJs, such as critical switching current and thermal stability. We investigate devices consisting of buffer/FeCoB/MgO/FeCoB/Ta/Ru multilayers for three different buffers: Ta 5 / Ru 10 / Ta 3, Ta 5 / Ru 10 / Ta 10 and Ta 5 / Ru 20 / Ta 5 (all thicknesses in nm). In addition, we study systems with a single FeCoB layer deposited above as well as below the MgO barrier. The crystallographic texture and the roughness of the buffers are determined by means of XRD and atomic force microscopy measurements. Furthermore, we examine the magnetic domain pattern, the magnetic dead layer thickness and the perpendicular magnetic anisotropy fields for each sample. Finally, we investigate the effect of the current induced magnetization switching for nanopillar junctions with lateral dimensions ranging from 1 {\mu}m down to 140 nm. Buffer Ta 5 / Ru 10 / Ta 3, which has the thickest dead layer, exhibits a large increase in the thermal stability factor while featuring a slightly lower critical current density value when compared to the buffer with the thinnest dead layer Ta 5 / Ru 20 / Ta 5

Household Energy Management

Ensuring flexibility and security in power systems requires the use of appropriate management measures on the demand side. The article presents the results of work related to energy management in households in which renewable energy sources (RES) can be installed. The main part of the article is about the developed elastic energy management algorithm (EEM), consisting of two algorithms, EEM1 and EEM2. The EEM1 algorithm is activated in time periods with a higher energy price. Its purpose is to reduce the power consumed by the appliances to the level defined by the consumer. In contrast, the EEM2 algorithm is run by the Distribution System Operator (DSO) when peak demand occurs. Its purpose is to reduce the power of appliances in a specified time period to the level defined by the DSO. The optimization tasks in both algorithms are based on the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic algorithm. The EEM1 and EEM2 algorithms also provide energy consumer comfort. For this purpose, both algorithms take into account the smart appliance parameters proposed in the article: sections of the working devices, power reduction levels, priorities and enablingof time shifting devices. The EEM algorithm in its operation also takes into account the information about the production of power, e.g., generated by the photovoltaic systems. On this basis, it makes decisions on the control of smart appliances. The EEM algorithm also enables inverter control to limit the power transferred from the photovoltaic system to the energy system. Such action is taken on the basis of the DSO request containing the information on the power limits. Such a structure of EEM enables the balancing of energy demand and supply. The possibility of peak demand phenomenon will be reduced. The simulation and experiment results presented in the paper confirmed the rationality and effectiveness of the EEM algorithm

Household Energy Management

Ensuring flexibility and security in power systems requires the use of appropriate management measures on the demand side. The article presents the results of work related to energy management in households in which renewable energy sources (RES) can be installed. The main part of the article is about the developed elastic energy management algorithm (EEM), consisting of two algorithms, EEM1 and EEM2. The EEM1 algorithm is activated in time periods with a higher energy price. Its purpose is to reduce the power consumed by the appliances to the level defined by the consumer. In contrast, the EEM2 algorithm is run by the Distribution System Operator (DSO) when peak demand occurs. Its purpose is to reduce the power of appliances in a specified time period to the level defined by the DSO. The optimization tasks in both algorithms are based on the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic algorithm. The EEM1 and EEM2 algorithms also provide energy consumer comfort. For this purpose, both algorithms take into account the smart appliance parameters proposed in the article: sections of the working devices, power reduction levels, priorities and enablingof time shifting devices. The EEM algorithm in its operation also takes into account the information about the production of power, e.g., generated by the photovoltaic systems. On this basis, it makes decisions on the control of smart appliances. The EEM algorithm also enables inverter control to limit the power transferred from the photovoltaic system to the energy system. Such action is taken on the basis of the DSO request containing the information on the power limits. Such a structure of EEM enables the balancing of energy demand and supply. The possibility of peak demand phenomenon will be reduced. The simulation and experiment results presented in the paper confirmed the rationality and effectiveness of the EEM algorithm

Zinc Phthalocyanine Sensing Mechanism Quantification for Potential Application in Chemical Warfare Agent Detectors

Rapid and accurate detection of lethal volatile compounds is an emerging requirement to ensure the security of the current and future society. Since the threats are becoming more complex, the assurance of future sensing devices’ performance can be obtained solely based on a thorough fundamental approach, by utilizing physics and chemistry together. In this work, we have applied thermal desorption spectroscopy (TDS) to study dimethyl methylophosphate (DMMP, sarin analogue) adsorption on zinc phthalocyanine (ZnPc), aiming to achieve the quantification of the sensing mechanism. Furthermore, we utilize a novel approach to TDS that involves quantum chemistry calculations for the determination of desorption activation energies. As a result, we have provided a comprehensive description of DMMP desorption processes from ZnPc, which is the basis for successful future applications of sarin ZnPc-based sensors. Finally, we have verified the sensing capability of the studied material at room temperature using impedance spectroscopy and took the final steps towards demonstrating ZnPc as a promising sarin sensor candidate

Toward Efficient Toxic-Gas Detectors: Exploring Molecular Interactions of Sarin and Dimethyl Methylphosphonate with Metal-Centered Phthalocyanine Structures

The rapid and reliable detection of lethal agents such as sarin is of increasing importance. Here, density-functional theory (DFT) is used to compare the interaction of sarin with single-metal-centered phthalocyanine (MPc) and MPc layer structures to a benign model system, i.e., the adsorption of dimethyl methylphosphonate (DMMP). The calculations show that sarin and DMMP behave nearly identical to the various MPcs studied. Among NiPc, CuPc, CoPc, and zinc phthalocyanine (ZnPc), we find the interaction of both sarin and DMMP to be the strongest with ZnPc, both in terms of interaction energy and adsorption-induced work function changes. ZnPc is thus proposed as a promising sensor for sarin detection. Using X-ray photoelectron spectroscopy, the theoretically predicted charge transfer from DMMP to ZnPc is confirmed and identified as a key component in the sensing mechanism