Number Of Layers Research Articles

Characterizing randomness in parameterized quantum circuits through expressibility and average entanglement

Abstract While scalable error correction schemes and fault tolerant quantum computing seem not to be universally accessible in the near sight, the efforts of many researchers have been directed to the exploration of the contemporary available quantum hardware. Due to these limitations, the depth and dimension of the possible quantum circuits are restricted. This motivates the study of circuits with parameterized operations that can be classically optimized in hybrid methods as variational quantum algorithms, enabling the reduction of circuit depth and size. The characteristics of these Parameterized Quantum Circuits (PQCs) are still not fully understood outside the scope of their principal application, motivating the study of their intrinsic properties. In this work, we analyse the generation of random states in PQCs under restrictions on the qubits connectivities, justified by different quantum computer architectures. We apply the expressibility quantifier and the average entanglement as diagnostics for the characteristics of the generated states and classify the circuits depending on the topology of the quantum computer where they can be implemented. As a function of the number of layers and qubits, circuits following a Ring topology will have the highest entanglement and expressibility values, followed by Linear/All-to-all almost together and the Star topology. In addition to the characterization of the differences between the entanglement and expressibility of these circuits, we also place a connection between how steep is the increase on the uniformity of the distribution of the generated states and the generation of entanglement. Circuits generating average and standard deviation for entanglement closer to values obtained with the truly uniformly random ensemble of unitaries present a steeper evolution when compared to others.

Monte Carlo calculations taking into account the DLP theory to study multilayer adsorption of C2H4 and CF4 on graphite

Physisorption potentials are useful for deducing the properties of adsorption systems. The adsorption behavior is a consequence of the actual potential that describes the interactions among the adsorption system. Determining the actual isotherm is a test for the correctness of a calculated adsorption potential. The physisorption interactions decrease with the film thickness, d, according to a power law as −γ/d3, where γ is the Van der Waals coefficient. In the Dzyaloshinskii, Lifshitz, and Pitaevskii (DLP) theory, γ is a function of d where γ(d) decreases with d. In the present work, the general theory of Van der Waals forces, given by DLP, is used to derive a more exact treatment of interactions among the physisorbed systems. The Monte Carlo method is used to investigate multilayer films using a lattice gas model adsorption isotherm. Critical temperatures are determined for ethylene (C2H4) and tetrafluoromethane (CF4) on graphite. These systems exhibit comparable behavior. Unlike the findings of mean field theory, our data shows that the critical temperature rises gradually as the layer number increases. On the other hand, the comparison with experimental results and measurements has shown that our calculations are more reliable and provide a better approximation in predicting film thickness. Our isotherms appear to be in better agreement with the experimental ones.

Influences of interface bonding behavior on the arresting performance of CFRP buckle arrestors - Part I: Experiments

CFRP can effectively serve as buckle arrestors for subsea pipelines in virtue of the advantages of lightweight, high strength, and corrosion resistance. In this paper, the interface bonding tests were firstly carried out to assess the bonding performance between the CFRP and pipelines. Three types of tests were designed to evaluate the circumferential, axial, and radial bonding properties, respectively, covering the double-strap joint shear test, axial tensile test, and normal tensile test. The stress-bonding slip responses were measured and various failure modes were identified. The effect of CFRP thickness, type of adhesive, adhesive thickness, and interface roughness on the interface bonding property was explored. Subsequently, SS304 pipe specimens wrapped with CFRP buckle arrestors were tested in a hyperbaric vessel. The pressure-variation in volume curves were obtained, and the flattening and U-shape modes were observed. The influences of adhesive type, adhesive thickness, interface roughness, number of CFRP layers, CFRP thickness, and CFRP length on the crossover pressure were examined. The results demonstrate that the number of CFRP layers and CFRP thickness remarkably affect the arresting performance of CFRP arrestors, and the influence of interface bonding properties on the arresting ability is not as significant as the adhesive strength.

Prediction of Performance and Emissions Diesel Engines Fueled-Biodiesel Using Artificial Neural Network (ANN) Resilient Backpropagation Algorithm (Rprop)

In order to increase energy security and improve environmental quality, the Indonesian Goverment set a target of 23% renewable energy mix in 2025, one of which is the Mandatory Bioediesel Program. A higher biodiesel blending ratio will affect the performance and emissions of diesel engines because biodiesel is chemically different from diesel oil. Research related to the prediction of diesel engine performance and emissions using Artificial Neural Network (ANN) has been conducted, but the author sees a research opportunity for the implementation of the ANN Resilient Backpropagation (Rprop) algorithm. The data used to create the ANN model prediction was secondary data from previous research. The model designed multi input and multi output (MIMO) with 4 input variables and 7 output variables. Model building done by varying the number of neurons and hidden layers. Model evaluation selected based on the largest coefficient of determination parameter R2 and the smallest RMSE or MAPE. The results showed that the ANN single layer 4-20-7 network architecture is the best model for predicting diesel engine performance and emissions with test data R2 , RMSE and MAPE of 0.962532, 6.699428 and 6.0% respectively, while for overall data testing has a performance of 0.982869, 3.908542 and 4.3%. The results also show that based on the ANN prediction results, the increasing biodiesel ratio can increase NOx emissions and decrease HC, CO and CO emissions2 . In terms of performance, the addition of biodiesel can increase BSFC and BP and decrease BTE. The results also show that the addition of ZnO concentration can reduce emissions while in terms of performance it will increase BTE and reduce BSFC and BP.

Negative Differential Resistance Device with High Peak-to-Valley Ratio Realized by Subband Resonant Tunneling of Γ-Valley Carriers in WSe2/h-BN/WSe2 Junctions.

Resonant tunneling diodes (RTDs) are a core technology in III-V semiconductor devices. The realization of high-performance RTD using two-dimensional (2D) materials has been long awaited, but it has yet to be accomplished. To this end, we investigate a range of WSe2/h-BN/WSe2 RTD devices by varying the number of layers of source and drain WSe2. The highest peak-to-valley ratio (PVR) is demonstrated in the three-layer (3L) WSe2/h-BN/1-layer (1L) WSe2 structure. The observed PVR values of 63.6 at 2 K and 16.2 at 300 K are the highest among the 2D material-based RTDs reported to date. Our results indicate the two key conditions to achieve high PVR: (1) resonant tunneling should occur between the Γ-point bands of the source and drain electrodes, and (2) the Γ-point bands contributing to the resonant tunneling should be energetically separated from the other bands. Our results provide an important step to outperform III-V semiconductor RTDs with 2D material-based RTDs.

Large deformation of variable thickness woven composite preforms considering interlayer difference. Application: Forming simulation of a blade preform

The main objective of this work was to investigate the interlayer difference of 3D orthogonal woven fabrics (3DOWFs) during deformation. Tensile, shear, and compression tests were performed on two types of 3DOWFs with different layer numbers. To determine the variations in the deformation behavior within the fabric, a layer decomposition method was developed. This process involved the decomposition of the fabric into two layers, the inner layer and the outer layer, depending on the difference in structural features of the yarn. Then the deformation characteristics of the inner layer and outer layer were derived based on their series or parallel relationship in the load path. The results indicate that there are noticeable variations in the deformation characteristics among the layers of 3DOWF, which are associated with the interwoven structure of the yarns. In particular, in compression deformation, the difference in yarn structure causes the initial stage (Compressive strain <0.2) of fabric compression deformation to be mostly attributed to the compression of the outer layer. Subsequently, the deformations of the inner layer and outer layer gradually tend to balance. Based on a simple anisotropic hyperelasticity model, the interlayer differences were considered in the blade preform simulation. The simulation result shows a good agreement with the preforming results. The results show that the interlayer difference has a significant impact on the deformation of the blade preform, particularly in the posterior edge region, which is not strongly compressed.

Spectral prediction of all dielectric nanopore metasurface based on DBO-DNN model

Based on the optical properties of symmetric structures independent of each other in the orthogonal direction, a class of all-dielectric nanohole array metasurfaces symmetrical along the diagonal is designed. By adding nanopores of different shapes to break the symmetry of the periodic unit structure, the double Fano resonance is excited. The spectral characteristics of metasurfaces with the same structure type are studied by finitedifference timedomain (FDTD) method. The deep neural network (DNN) is used to establish the nonlinear mapping relationship between the input structural parameters and the transmission spectrum. The number of hidden layers in the DNN and the number of neurons in each layer are optimized by the dung beetle optimization (DBO) algorithm. Therefore, the number of hidden layers of the model is determined to be 5, and the number of neurons in each layer is 120, 30, 150, 60, 90, respectively. The mean square error (MSE) is used to evaluate the training effect of DNN after optimization search. After 35,000 epochs of training, MSE is reduced to 0.0003926. The influence of different gradient descent optimization algorithms on the prediction results is explored respectively, and it is found that Adamax is the most effective. The results show that the prediction model can predict the spectrum within 1 s. Compared with the traditional simulation method, the simulation time is effectively saved. Meet the requirements of efficient and rapid design of ultra-thin lenses. For the same type of metasurface structure, the transmission spectrum can be accurately predicted without multiple data sets.

Electronic, Optical and Thermoelectric Properties of Two-Dimensional Molybdenum Carbon Mo2C-MXenes

We investigate the structural, electronic, optical, and thermoelectric properties of three compositions of Mo2C-MXenes (Mo2CF2, Mo2C(OH)2, and Mo2CO2) from monolayer to multilayer by first principles calculation within Density Functional Theory (DFT) and Boltzmann transport theory. Firstly, the atomic structures of Mo2C-MXenes are optimized, and their respective structures are created with comparative research. Secondly, their electronic band structures and optical properties are studied in detail. The estimation of the bandgap energy of Mo2C-MXenes with its functionalization reveal that most Mo2CF2 and Mo2C(OH)2 layers are semiconductors, while Mo2CO2 behaves as a metal. The electrical and optical properties can be altered by controlling the on-surface functional groups and the number of layers. Computation of the thermoelectric (TE) properties of Mo2C-MXenes reveals that, upon heating to 600 K, Mo2CF2 and Mo2C(OH)2 exhibit a high Seebeck coefficient and a relatively high electrical conductivity. The Seebeck coefficient reaches ~400 µV K−1 at room temperature for all layers of Mo2CF2 MXenes. Our results prove that Mo2CF2 is considered a promising material for thermoelectric devices, while Mo2CO2 does not possess better thermoelectric performance. Mo2C-MXenes from monolayer to multilayer have outstanding properties, such as flexible bandgap energy and high thermal stability, making them promising candidates for many applications, including energy storage and electrode applications.

АНАЛІЗ ЗАСТОСУВАННЯ ЗГОРТКОВИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ ПІДВИЩЕННЯ НАДІЙНОСТІ У РОБОТІ ФЕС

This work is dedicated to the study of applying convolutional neural networks (CNN) for automatic recognition of photovoltaic panel conditions based on images. The focus is placed on the impact of CNN hyperparameter tuning, such as the number of layers, kernel size, learning rate, and activation function, on the accuracy of classi-fying various panel conditions, including physical damage, electrical defects, dust contamination, and clean pan-els. The use of CNNs is driven by their ability to automatically detect complex spatial patterns in images, which is crucial for accurately identifying different types of defects and anomalies on the panels. The main challenges include the variability in panel conditions and external factors, such as weather conditions, that affect data quali-ty. The influence of CNN hyperparameters on classification accuracy was analyzed, and their optimal values were determined to achieve high model accuracy. The relevance of the research lies in the growing role of CNNs in monitoring photovoltaic panel conditions, which enables timely defect detection and optimization of mainte-nance. The results demonstrate that proper hyperparameter tuning of CNNs significantly improves classification accuracy, contributing to increased efficiency and stability of photovoltaic stations. The paper emphasizes the importance of using neural networks for image analysis in the field of photovoltaic system maintenance, ensuring their effective integration into energy networks. The goal of the study is to improve the accuracy of photovoltaic panel condition classification by developing and tuning CNN models with optimal hyperparameters, enabling timely detection of panel defects and enhancing the efficiency of photovoltaic system maintenance.

Multilayer Adsorption Characteristics of CO2 in Organic Nanopores with Different Pore Sizes: Molecular Simulation and Ono-Kondo Lattice Model.

Shale contains numerous organic micropores with significant potential for CO2 storage. To precisely evaluate the CO2 storage potential of shale reservoirs, it is essential to accurately quantify the adsorption of CO2 within these pores. This study used Grand Canonical Monte Carlo (GCMC) molecular simulations to analyze the CO2 adsorption behavior in organic micropores of varying sizes. The study clarified the number and width of the CO2 adsorption layers in micropores of various sizes and proposed a method for segmenting the multilayer adsorption structure. Additionally, the classic Ono-Kondo lattice (OK) model was extended to characterize pore-filling adsorption, incorporating solid-gas and gas-gas interactions. Accurate characterization of CO2 multilayer adsorption and precise calculation of CO2 absolute adsorption in micropores were achieved. Results indicate that CO2 exhibits pore-filling adsorption behavior in organic micropores, forming a multilayer adsorption structure governed by the pore size. Following symmetry principles, the adsorption layer structure in organic micropores can be simplified to a maximum of three layers. When only one adsorption layer forms, its width equals the gas-accessible pore size. For two or more layers, the width of the original layer stabilizes as additional layers form. The stable adsorption layer widths, from nearest to farthest from the pore wall, are 0.33, 0.45, and 0.39 nm. The improved OK model accurately describes CO2 excess and absolute adsorption isotherms across different pore sizes and calculates the CO2 density in each adsorption layer, showing high consistency with GCMC simulation results. These findings highlight the importance of understanding the CO2 multilayer adsorption structure for accurately estimating CO2 adsorption in organic micropores.

Thermal spreading in a multilayer geometry with convective cooling along the side wall of each layer

Two-dimensional thermal conduction between a source/sink and a body of larger size results in thermal spreading and constriction, which are of much technological importance in problems such as semiconductor thermal management. While the traditional analysis of thermal spreading and constriction assumed adiabatic side walls, there is growing interest in convective heat removal from the side walls, such as in multilayer three-dimensional integrated circuits (3D ICs) and stacked Li-ion cells in a battery pack. This work presents theoretical analysis of thermal spreading in a multilayer body with distinct convective heat transfer coefficients along the side wall for each layer, in addition to anisotropic thermal conductivity in each layer and thermal contact resistance at interfaces between layers. A series solution for the temperature distributions in each layer is derived, with a unique set of eigenvalues for each layer. A set of linear algebraic equations that govern the coefficients of the series solutions is derived. Results are shown to agree well with past work for special cases, as well as with numerical simulations for general problems. The impact of source size, thermal anisotropy and Biot numbers of various layers is analyzed in detail. In particular, the interplay between Biot numbers along the sidewall and at the sink plane in determining total thermal resistance is investigated in detail. This work contributes towards a fundamental understanding of theoretical thermal conduction in problems of practical relevance, such as advanced microelectronics and Li-ion cells. Results may find application in the design and optimization of these and other engineering systems where thermal spreading or constriction play an important role.

On the lowest-frequency bandgap of 1D phononic crystals

This manuscript puts forward and verifies an analytical approach for the design of phononic crystals that feature a bandgap at the lowest possible frequencies, in the sense of finding the optimal layer thicknesses for a given set of materials in a prescribed layering order. The mathematical formulation rests upon the exact form of the half-trace function (half of the trace of the global transfer matrix) of the layered medium, which is directly connected to its bandgap structure. After showing that there is a tight relation between the frequency at which the first bandgap opens up and the curvature of the half-trace function at zero frequency, a new optimization strategy is proposed, based on the minimization of this curvature. Notably, the optimal solution is expressed as a closed-form equation and remains valid for any number of layers within the unit cell. We validate this analytical result by comparison with a numerically optimized design, finding remarkably good agreement between both solutions.

BPFNN Based Football Player Value Prediction by Introducing Historic Information

With extensively successful applications of artificial neural networks in many scenarios of classification and prediction, research related to the world most popular sport - football - has undoubtedly attracted researchers to carry out their works with aid of such powerful tools. Despite that most of these works are oriented to predict match results and game strategies, only very a few are designed for player value prediction, which is of great importance with the rapid growth of the football players’ transfer market. In this paper, we propose a BP feedforward neural network framework which integrates both the empirically determined variables and some historical information. Comparative ablation experiments on one dataset FINAL verified the necessity of some factors especially the highest_values which inspired the inclusion of available historical information into the framework. For further verifying the efficiency of integrating historical information, we embedded the value columns of FIFA17 to FIFA21 into the data of FIFA22. Comparative experiments showed an 11% improvement on the precision with the help of historical values, verifying the efficiency of this strategy. Experiments were also conducted for determining the choice of the number of layers, the ratio of the training set and test set, the learning rate, and the process of the missed information.

Graphene Chainmail-Enabled Moderate Precatalyst Phase Evolution for Sustainable Polysulfide Electrocatalysis in Li─S Batteries.

The rational design of polysulfide electrocatalysts is of vital importance to achieve longevous Li─S batteries. Notwithstanding fruitful advances made in elevating electrocatalytic activity, efforts to regulate precatalyst phase evolution and protect active sites are still lacking. Herein, an in situ graphene-encapsulated bimetallic model catalyst (CoNi@G) is developed for striking a balance between electrocatalytic activity and stability for sulfur electrochemistry. The layer numbers of directly grown graphene can be dictated by tuning the synthetic duration. Exhaustive experimental and theoretical analysis comprehensively reveals that the tailored graphene chainmail boosts catalytic durability while guaranteeing moderate phase evolution, accordingly attaining a decorated surface sulfidation with advanced catalytic essence. Benefiting from the sustainable polysulfide electrocatalysis, CoNi@G enabled sulfur electrodes to harvest a capacity output of 1276.2mAhg-1 at 0.2 C and a negligible capacity decay of 0.055% per cycle after 1000 cycles at 1.0 C. Such a maneuver can be readily extended to other metallic catalysts including NiFe, CoFe, or Co. The work elucidates the precatalyst phase evolution mechanism through a controllable graphene-armored strategy, offering meaningful guidance to realize durable electrocatalysts in Li─S batteries.

Photocatalyst immobilisation on cylindrical surface: Scale-up potential of continuous photocatalytic reactor

The use of continuous photocatalytic reactor is important for sustainable water treatment technology. Photocatalyst immobilisation on supports (of varying shapes and sizes) having the desired degree of uniformity and smoothness is challenging, and is evolving. The major concerns are the geometry of the surface (which restricts the scalability of such systems) and the appropriate adhesion of the photocatalyst. Flat surface has an inherent limitation of low photocatalyst area per floor space, and poor light distribution, whereas cylindrical surfaces can overcome all these limitations. In this work, we have developed and assessed the photocatalyst coating methodology over cylindrical (quartz) surface and have prepared a modular (continuous) photocatalytic reactor. We have immobilised titanium-dioxide on the outer surface of a cylindrical quartz tube using a rotating (coating) mechanism, using the viscous sol prepared from the sol-gel method followed by heat treatment, and not with any binder to the as-synthesized powder. Various characterization techniques, including ultraviolet-diffuse reflectance spectroscopy, energy-dispersive spectroscopy, x-ray diffraction, field-emission scanning electron microscopy, optical surface profilometry, and ultraviolet–visible spectroscopy, are used to evaluate the impact of process conditions – number of layers, rotational speed, and sol viscosity, on the coating uniformity, smoothness, and layer thickness. The performance of the custom-made (modular-type) reactor is evaluated for degradation of the model molecule (pollutant). The reactor performance and capacity can be easily scaled up by parallel connections or introducing multiple coated tubes (to increase the photocatalyst area). The outcome of the work will be of immense value in scaling up the continuous large scale photocatalytic operation and treatment process.

Effect of SiC doping on microwave absorbing properties of ball-milled carbonyl iron/MoS2 composites

Using MoS2 to prepare novel composite absorbing coatings is an effective strategy to enhance electromagnetic wave absorption. In order to explore the preparation process of large-scale preparation of MoS2 composite absorbing materials, a Fe/MoS2/SiC laminated composite absorbing material was prepared by high-energy ball milling method to realize the absorption of electromagnetic waves. By adjusting the proportion of SiC, SiC, MoS2 and Fe were ball-milled at room temperature for 20 h, and the dielectric properties were improved to achieve good absorbing effect. The microstructure and properties of the materials were measured by XRD, SEM, Raman, XPS, HRTEM, VSM and VNA. The results show that the doping of appropriate amount of SiC reduces the interplanar spacing of MoS2 and increases the edge points, which increases the conductivity of MoS2. MoS2 reduces the number of layers and increases its size and area, which improves the dielectric properties of Fe/MoS2 and optimizes the electromagnetic parameters of the composites. When the ratio (mole) of SiC, Fe and MoS2 is 3:6:2, the best electromagnetic wave absorption effect is obtained, the lowest electromagnetic wave reflection loss is −72.98 dB, and the best bandwidth is 3.04 GHz when the matching thickness is 6.53 mm.

Flexible and transparent leaf-vein electrodes fabricated by liquid film rupture self-assembly AgNWs for application of heaters and pressure sensors

The leaf veins with hierarchically interconnected network structures have great potential as self-assembly templates for transparent and flexible electrodes. In this paper, a liquid membrane ruptured assisted AgNWs self-assembly method was adopted to obtain conductive veins, which show a low sheet resistance of 3.6Ω/□ and a high transparency of 79 % at a minimal materials waste. The figure of merit of conductive veins was as high as 433Ω-1 and can be further increased by increasing the number of soaking because the light transmittance is nearly immune to the AgNWs layer number. The veins maintained a good electrical conductivity even after bending 10,000 times at a 2 mm radius of curvature and exhibited a good corrosion resistance under harsh conditions. Heaters based on the conductive veins are shown as application examples. A high temperature of 104 °C was achieved at a low voltage of 2 V, which is enough to heat cold water. They still have good heating properties even in the bending state or tensile state. Furthermore, pressure sensors based on the conductive veins were fabricated as examples of wearable devices. The sensor exhibits a high sensitivity of 3.8 kPa−1 in the low-pressure range (0–15 kPa) and 17.4 kPa−1 in the high pressure range (15–50.96 kPa). It can monitor human actions such as finger bending, finger taping, throat swallowing and wrist pulse in real-time, which has promising potential for motion monitoring and information encryption applications.

Mechanical properties of CFRP and shrinkage compensating concrete actively confined and strengthened short columns under freeze–thaw cycles

AbstractThis paper investigates the use of CFRP precast tubes filled with shrinkage‐compensating self‐consolidating concrete (SSCC) for strengthening columns. Compared to ordinary self‐consolidating concrete (OSCC), SSCC can address the stress hysteresis issue caused by the poor cooperation between CFRP and concrete structures, while also enhancing the mechanical properties of columns. Experimental and theoretical studies were conducted to examine the tensile mechanical properties of CFRP materials and the axial compressive mechanical properties of CFRP precast tubes strengthened with OSCC or SSCC under freeze–thaw cycles. Various variables were considered, such as freeze–thaw cycles, types of strengthening concrete, and the number of CFRP layers. The results indicate that freeze–thaw cycles do not affect the ultimate tensile strength of CFRP but significantly reduce its ultimate tensile strain. SSCC‐strengthened specimens exhibit higher load‐carrying capacity and better ductility compared to OSCC‐strengthened specimens. In addition, compared to the strengthening of two‐layer CFRP and SSCC, the strengthening of one‐layer CFRP and SSCC more effectively demonstrates the advantage of the post‐tensioning effect. However, the ductility coefficient of the columns decreases with an increase in the number of freeze–thaw cycles, regardless of the number of CFRP layers or type of strengthened concrete. Modified theoretical model accurately predicts the normalized peak stress and strain of the strengthened specimens.

Development and Manufacturing Method of Electrodes for a High Temperature Oxygen Sensor

The article discusses the use of zirconium dioxide stabilized with yttria as an electrolyte for the formation of a high-temperature ceramic oxygen concentration sensor. The fundamental possibility of using such a material is due to the high electrical conductivity caused by the mobility of oxygen ions in the vacancies of the solid electrolyte. The operating principle of such a sensor as an electrochemical concentration element is discussed. It has been shown that the use of such sensors is relevant for various industries (including chemical, metallurgical, oil and gas, energy and others). Options for forming electrodes of an electrochemical sensor using various materials and deposition methods are considered. A method for forming a platinum coating using a chemical method using isopropyl alcohol and subsequent pyrolysis was tested. It has been experimentally shown that the chemical method is applicable for the formation of electrodes of an electrochemical sensor. The electrical characteristics of the resulting coatings were measured. The optimal composition of the reaction mixture and the required number of layers to create a stable operating electrochemical sensor have been determined. The dependence of the potential of the formed electrochemical sensor from stabilized zirconium oxide on oxygen concentration in a wide temperature range has been studied. It has been shown that when using a scheme of sequential transformations, hydrolysis of platinum tetrachloride - deposition on a substrate - reductive pyrolysis, it is possible to obtain a high-quality conductive coating for the formation of gas sensor electrodes. The optimal composition of the reaction mixture for the formation of platinum electrodes has been selected. The optimal number of layers was determined to ensure the required level of electrical conductivity of the electrodes. It has been shown that an electrochemical element based on zirconium dioxide and platinum electrodes, formed according to the scheme developed in this work, can be used as a high-temperature oxygen sensor.

EVOLUTION OF THE PRESS BONDING PROPERTIES OF Al/Cu BIMETAL BULK COMPOSITES (EXPERIMENTAL AND NUMERICAL INVESTIGATION)

Nowadays, the application of bimetallic laminates with special capabilities is increasing and has experienced high growth. These properties include high corrosion resistance, mechanical properties, thermal stability, and lightweight. In the midst of the technologies of multilayer composite materials, accumulative press bonding (APB) as a solid-phased method of welding is one of the most common techniques for the production of multilayer composites. One of the most important aims for this choice is the press pressure, which can generate a suitable mechanical connection and strong bonding between produced metal layer components. In this study, bimetal aluminum/copper bulk composites have been fabricated via the APB technique. Then, the effect of pressing parameters such as plastic strain and the number of layers on the stress distribution has been investigated. For samples with eight layers, the shear stress between the layers reached 3.1 MPa which is a suitable condition to generate a successful bonding. The stress applied on the layers has also increased with increasing the thickness reduction ratio. The interlayer shear stresses also increase with decreeing the thickness. With a higher reduction in thickness ratios, the amount of sinking layers was greater along the entire length of the sample, which led to the crushing of copper layers. During the APB process at higher pressing passes, increasing the volume of virgin material in the direction of the press led to increasing the compaction and better bonding of Al and Cu layers to each other.