Binary Hybrid Research Articles

Optimization of the Effects of Binary Hybrid Nanofluid Synthesis Parameters on the Thermal and Hydraulic Characteristics

Due to the growing interest in hybrid nanofluids, researchers have been primarily focused to obtain the thermophysical properties of hybrid nanofluids. Several parameters such as temperature, volume or weight fractions, nanoparticle types and shapes affected the thermophysical properties of nanofluids. Accordingly, the optimization in thermal conductivity and viscosity of nanofluids obtained by mixing binary nanocomposite particles GnP-Fe3O4 in an ethylene glycol-water base fluid with a mixing ratio of 20-80 % was investigated in this study. The Taguchi approach is used for single-objective optimization and the significance values of the synthesis parameters were determined using the ANOVA technique. Five different factors, including mechanical strring time, ultrasonic mixing time and power, surfactant mixing ratio, and nanofluid weight ratio, were optimized at three different levels during the synthesis of hybrid nanofluids. The experimental L27(35) orthogonal array trial was built in order to carry out the optimization. According to the results, mechanical striring time was found to have the least impact on both thermophysical parameters, whereas ultrasonic mixing power, nanofluid weight ratio, and ultrasonic mixing time were also ranked from low to high impact. The usage of surfactant was shown to be the parameter that had the greatest impact, with rates of 63.57% and 65.31%, on thermal conductivity and viscosity, respectively.

Long-Cycling-Life Sodium-Ion Battery Using Binary Metal Sulfide Hybrid Nanocages as Anode.

Due to the relatively high capacity and lower cost, transition metal sulfides (TMS) as anode show promising potential in sodium-ion batteries (SIBs). Herein, a binary metal sulfide hybrid consisting of carbon encapsulated CoS/Cu2 S nanocages (CoS/Cu2 S@C-NC) is constructed. The interlocked hetero-architecture filled with conductive carbon accelerates the Na+ /e- transfer, thus leading to improved electrochemical kinetics. Also the protective carbon layer can provide better volume accommondation upon charging/discharging. As a result, the battery with CoS/Cu2 S@C-NC as anode displays a high capacity of 435.3mAhg-1 after 1000 cycles at 2.0Ag-1 (≈3.4C). Under a higher rate of 10.0Ag-1 (≈17C), a capacity of as high as 347.2mAhg-1 is still remained after long 2300 cycles. The capacity decay per cycle is only 0.017%. The battery also exhibits a better temperature tolerance at 50 and -5°C. A low internal impedance analyzed by X-ray diffraction patterns and galvanostatic intermittent titration technique, narrow band gap, and high density of states obtained by first-principle calculations of the binary sulfides, ensure the rapid Na+ /e- transport. The long-cycling-life SIB using binary metal sulfide hybrid nanocages as anode shows promising applications in versatile electronic devices.

Synergism lubrication of graphene and carbon nanotube in polymeric composites under drying sliding condition

The internal lubrication of polyimide (PI) with added graphene oxides (GOs) and carbon nanotubes (MWCNTs), as well as their binary hybrid nano-additives (GCNTs), were contrastively investigated under dry sliding conditions. The friction coefficient of neat PI or its nanocomposites decreases as a stepwise increase of the load fitting a negative exponential equation while growing linearly as a stepwise increase of the velocity. Micromorphological and physicochemical variations occurring at friction interfaces have been analyzed using X-ray photoelectron spectroscopy, Raman spectroscopy, and high-resolution transmission electron microscopy results. Given the structural discrepancy between GOs and MWCNTs, the obtaining results showed that the different microcosmic tribological mechanisms for GCNTs enable to produce a synergistic lubricating effect, resulting in facilitating the complicating interface sliding between nanocomposites and steel balls.

Dual Polarization DQPSK ROF System with Duo Binary Precoding and Hybrid Amplification for Effective Hybrid SMF FSO Transmission Medium

This article demonstrates the utilization of a hybrid amplification technique on the transmission part of the proposed hybrid medium transmission system which includes both using Single Mode Fiber (SMF) with 60 km and Free Space Optic (FSO) with a range between 5 km and 25 km. It has included four weather situations with an investigation of the FSO medium-based system performance. The system uses 64 channels based on Dual Polarization-Differential Quadratic Phase Shift Keying (DP-DQPSK) Dense Wavelength Division Multiplexing (DWDM) for the Radio over Fiber (RoF) system using the Duo Binary coding scheme to improve the system efficiency in general and the cross-talk in particular. The investigations will include using the hybrid amplifiers of Erbium Doped Fiber Amplifier (EDFA) and Optical Amplifier (OA) for transmission. The analysis results show that using the hybrid amplification could improve the transmission to reach 25 km for light air case, meanwhile, for light haze, it could reach up to 20 km. Moderate haze could improve transmission efficiency for a distance of 7 km. Finally, for moderate rain situations, the transmission could be further improved to cover a distance of up to 5 km.

Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach

While the world is working quietly to repair the damage caused by COVID-19's widespread transmission, the monkeypox virus threatens to become a global pandemic. There are several nations that report new monkeypox cases daily, despite the virus being less deadly and contagious than COVID-19. Monkeypox disease may be detected using artificial intelligence techniques. This paper suggests two strategies for improving monkeypox image classification precision. Based on reinforcement learning and parameter optimization for multi-layer neural networks, the suggested approaches are based on feature extraction and classification: the Q-learning algorithm determines the rate at which an act occurs in a particular state; Malneural networks are binary hybrid algorithms that improve the parameters of neural networks. The algorithms are evaluated using an openly available dataset. In order to analyze the proposed optimization feature selection for monkeypox classification, interpretation criteria were utilized. In order to evaluate the efficiency, significance, and robustness of the suggested algorithms, a series of numerical tests were conducted. There were 95% precision, 95% recall, and 96% f1 scores for monkeypox disease. As compared to traditional learning methods, this method has a higher accuracy value. The overall macro average was around 0.95, and the overall weighted average was around 0.96. When compared to the benchmark algorithms, DDQN, Policy Gradient, and Actor-Critic, the Malneural network had the highest accuracy (around 0.985). In comparison with traditional methods, the proposed methods were found to be more effective. Clinicians can use this proposal to treat monkeypox patients and administration agencies can use it to observe the origin and current status of the disease.

Review on Mono and Hybrid Nanofluids: Preparation, Properties, Investigation, and Applications in IC Engines and Heat Transfer

Nano fluids are widely used today for various energy-related applications such as coolants, refrigerants, and fuel additives. New coolants and design modifications are being explored due to renewed interest in improving the working fluid properties of heat exchangers. Several studies have investigated nanofluids to enhance radiator and heat exchanger performance. A new class of coolants includes single, binary, and tertiary nanoparticle-based hybrid nano-coolants using ethylene glycol/deionized water combinations as base fluids infused with different nanoparticles. This review article focuses on the hydrothermal behavior of heat exchangers (radiators for engine applications) with mono/hybrid nanofluids. The first part of the review focuses on the preparation of hybrid nanofluids, highlighting the working fluid properties such as density, viscosity, specific heat, and thermal conductivity. The second part discusses innovative methodologies adopted for accomplishing higher heat transfer rates with relatively low-pressure drop and pump work. The third part discusses the applications of mono and hybrid nanofluids in engine radiators and fuel additives in diesel and biodiesel blends. The last part is devoted to a summary of the research and future directions using mono and hybrid nanofluids for various cooling applications.

Significance of radiant-energy and multiple slips on convective flow of mono, binary and ternary hybrid nanofluids: A comparative study

To meet the energy requirement of the world, this work addresses the MHD flow of ternary hybrid nanofluid between two rotating and stretchable disks having porous medium. Thermal radiation effects along with velocity slips at the interface of fluid and disk are considered in this study. Three types of nanoparticles, namely, Al2O3 (alumina), CuO (copper oxide) and Cu (copper), have been taken with water as the host fluid. The flow and thermal characteristics of hybrid (Al2O[Formula: see text]) and ternary (Al2O[Formula: see text]) nanofluids formed by the combination of nanoparticles have been observed. This combination aids in the breakdown of dangerous compounds, environmental purification and the cooling of other equipment. The corresponding equations are modeled in terms of PDEs, and these PDEs are transformed into ODEs using Von Karman similarity transformations. The resulting equations are solved by using finite difference method based ND solver. The rate of heat transfer increases with the rise in thermal Biot numbers. Moreover, the solution predicts that for variations in radiation parameter from 0.5 to 1.5, heat transfer upsurges 88% for traditional nanoparticles, 92.4% for hybrid nanoparticles while for trihybrid nanoparticles, it grows up from 0% to 95%.

A novel feature selection using binary hybrid improved whale optimization algorithm

A novel feature selection using binary hybrid improved whale optimization algorithm

A nanowire-assembled Co3S4/Cu2S@carbon binary metal sulfide hybrid as a sodium-ion battery anode displaying high capacity and recoverable rate-performance.

A binary metal sulfide hybrid consisting of nanowire-assembled and polypyrrole-coated Co3S4/Cu2S spheres after nitrogen-doped carbon coating (Co3S4/Cu2S@NC) is developed as an anode, which displays a capacity exceeding 412.3 mA h g-1 after 550 cycles under 1.0 A g-1. Recoverable rate-performance and good temperature tolerance under 50 °C and -10 °C are achievable; a full cell delivers 339.5 mA h g-1, indicating promising potential for applications in various conditions.

Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data. There are 2n potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches. Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed. When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead to instability. Because of this, we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process. For the selection of the best subset of features, we recommend using the binary ASSOA search strategy we developed before. According to the suggested approach, the number of features picked is reduced while maximizing classification accuracy. A ten-feature dataset from the University of California, Irvine (UCI) repository was used to test the proposed method’s performance vs. eleven other state-of-the-art approaches, including binary grey wolf optimization (bGWO), binary hybrid grey wolf and particle swarm optimization (bGWO-PSO), bPSO, binary stochastic fractal search (bSFS), binary whale optimization algorithm (bWOA), binary modified grey wolf optimization (bMGWO), binary multiverse optimization (bMVO), binary bowerbird optimization (bSBO), binary hybrid GWO and genetic algorithm (bGWO-GA), binary firefly algorithm (bFA), and bGA methods. Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

Role of Nanoparticles in Acoustical and Dielectric Measurements using Binary Non-Polar Fluid

In this work, the experimental values of dielectric constant and acoustical parameters for binary mixture (benzene + diethylamine) and hybrid CuO nanofluid (CuO + diethylamine + benzene) were studied for various molar fraction from 0.01 to 0.06 M with different temperatures ranging from 298 to 318 K. Molecular interaction depends on the concentration, inclusion of CuO nanoparticles and temperature. FTIR analyses also revealed that despite the rise in temperature, no bond breakdown between the molecules is found. The results demonstrate that the weak interaction due to van der Waals forces exist between the unlike molecules due to dipole-induced dipole interactions.

Optimal planning and operation of energy hub by considering demand response algorithms and uncertainties based on problem-solving approach in discrete and continuous space

• Energy hub optimal planning and operation based on stochastic-probability indices. • Implement and evaluate the impact of DR/IDR algorithms. • Load uncertainty analysis and probability behaviour of renewable energy sources. In this research, the two-level structure of optimal planning and operation of the energy hub (EH) based on demand uncertainty and renewable energy resources (RES) is presented. The optimal planning based on stochastic-probability models is presented at the primary and optimal operation based on stochastic-probability models is also presented at the secondary level. The proposed method is planned based on the problem-solving approach in continuous and discrete space. The optimal planning objectives include determining the optimal capacity of EH equipment and minimizing investment costs. Optimal operation objectives are also planned and formulated based on minimizing EH operation cost, reducing greenhouse gas emissions, increasing RES utilization based on stochastic-probability modelling, and examining the demand response/integrated demand response (DR/IDR) effect. The DR programs implementation causes a reduction of 14.3% of the EH total cost, and the IDR programs implementation also causes a reduction of 16.56% of the EH total cost. The use of the proposed optimal two-level model, in addition to efficiency in different operation scenarios, has also reduced the calculation time. The optimization problem is solved based on Mixed Integer Linear Programming (MILP) and Binary Real-Coded Hybrid Genetic Algorithm (HRBC-GA).

Binary ionic liquids hybrid electrolyte based supercapacitors with high energy & power density.

Supercapacitors with high energy and power densities have become highly desirable in practical applications. Ionic liquids (ILs) are considered as promising electrolytes of supercapacitors owing to their excellent electrochemical stability window (approx. 4-6 V) and good thermal stability. However, the high viscosity (up to 102 mPa s) and low electric conductivity (<10 mS cm-1) at room-temperature extremely reduce the ion diffusion dynamics in the energy storage process, resulting in the unsatisfactory power density and rate performance of supercapacitors. Herein we propose a novel binary ionic liquids (BILs) hybrid electrolyte composed of two kinds of ILs in an organic solvent. Along with the organic solvent with high dielectric constant and low viscosity, the addition of binary cations effectively improves the electric conductivity and reduces the viscosity of IL electrolytes. By mixing trimethyl propylammonium bis(trifluoromethanesulfonyl)imide ([TMPA][TFSI]) and N-butyl-N-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide ([Pyr14][TFSI]) with an equal mole ratio in acetonitrile (1 M), the as-prepared BILs electrolyte shows superior electric conductivity (44.3 mS cm-1), low viscosity (0.692 mPa s), and a wide electrochemical stability window (4.82 V). The supercapacitors assembled with activated carbon electrodes (commercial mass loading) and this BILs electrolyte achieve a high working voltage of 3.1 V, leading to a maximum energy density of 28.3 W h kg-1 at 803.35 W kg-1 and a maximum power density of 32.16 kW kg-1 at 21.17 W h kg-1, which are obviously superior to those of commercial supercapacitors based on organic electrolytes (2.7 V).

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning. Each feature in a dataset has 2n possible subsets, making it challenging to select the optimum collection of features using typical methods. As a result, a new metaheuristics-based feature selection method based on the dipper-throated and grey-wolf optimization (DTO-GW) algorithms has been developed in this research. Instability can result when the selection of features is subject to metaheuristics, which can lead to a wide range of results. Thus, we adopted hybrid optimization in our method of optimizing, which allowed us to better balance exploration and harvesting chores more equitably. We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes. In the proposed method, the number of features selected is minimized, while classification accuracy is increased. To test the proposed method’s performance against eleven other state-of-the-art approaches, eight datasets from the UCI repository were used, such as binary grey wolf search (bGWO), binary hybrid grey wolf, and particle swarm optimization (bGWO-PSO), bPSO, binary stochastic fractal search (bSFS), binary whale optimization algorithm (bWOA), binary modified grey wolf optimization (bMGWO), binary multiverse optimization (bMVO), binary bowerbird optimization (bSBO), binary hysteresis optimization (bHy), and binary hysteresis optimization (bHWO). The suggested method is superior and successful in handling the problem of feature selection, according to the results of the experiments.

Novel synthesis of nickel oxide-copper hexacyanoferrate binary hybrid nanocomposite for high-performance supercapacitor application

Novel synthesis of nickel oxide-copper hexacyanoferrate binary hybrid nanocomposite for high-performance supercapacitor application

A binary Bi-phase mutation-based hybrid Equilibrium Optimizer for feature selection in medical datasets classification

With the rapid expansion in Biological Sciences, biomedical data classification has become challenging. These datasets generally consist of missing values, redundant features and irrelevant information. A novel hybrid wrapper-based feature selection method is proposed to tackle these issues effectively. In order to improve the exploration ability of the particles, the Sine factor is integrated with the Equilibrium Optimizer (EO) technique. A Bi-phase Mutation (BM) scheme is integrated to enhance the exploitation phase of the EO algorithm (BM-based Hybrid EO, BMHEO). The BMHEO method is evaluated by employing four different classifiers – KNN, SVM, Random Forest (RF) and Discriminant Analysis (DA). It is observed that the Random Forest classifier exhibits superior performance compared to the other three classifiers. Eight S-shaped and V-shaped transfer functions are integrated to convert the solutions to binary form. Based on the above enhancements, eight different versions of BMHEO are produced. The performance of these versions is assessed using twenty biomedical datasets. Experiments on these datasets demonstrate that the BMHEO-S2 version outperforms other methods in terms of fitness values and classification accuracies. This approach is also tested on a MED-NODE image dataset for classification, and higher accuracy of 97.02% is achieved.

Decorating ZnS by ZnIn2S4 to fabricate hybrid photocatalyst ZnIn2S4/ZnS for high photocatalytic hydrogen generation performance

The ZnIn2S4/ZnS (ZIS/ZnS) binary hybrid photocatalysts were fabricated by modifying ZnS with ZnIn2S4 employing the facile hydrothermal method. The photocatalytic hydrogen evolution was carried out to evaluate the photocatalytic activity of the as-obtained samples. The results indicated that the 5 %ZIS/ZnS exhibits the best photocatalytic performance with a hydrogen generation rate of 8502 μmol g−1h−1. The enhanced photocatalytic performance can be ascribed to the introduction of ZnIn2S4, whose unique optimized structures, electronic structures and charge mobility and separation are in favor of the light-harvesting ability and separation and transfer of charge carriers.

Heat transfer and flow characteristic of a flat confined loop thermosyphon with ternary hybrid nanofluids for electronic devices cooling

The purpose of this study is to explore the feasibility of the application of ternary hybrid nanofluids in heat pipes and the effect mechanism of nanofluids on the operation performance of flat confined loop thermosiphon (FCLTS), which are also the novelty of this study. Al2O3:Cu:TiO2 ternary hybrid nanofluids with different mixing ratios and concentrations were prepared. The synergistic effect of thermal conductivity enhancement was analyzed to obtain the ternary hybrid nanofluids with the optimal mixing ratio of 4:2:4. Thermal performance of a FCLTS with Al2O3:Cu:TiO2 nanofluids was investigated. This study focused on the effect of nanofluid concentrations, heat loads and inclination angles. This study revealed a unique phenomenon that nanofluids could be cleverly attached to the upper heated surface of evaporator, i.e., above the liquid level, improving the wettability of the upper surface. The optimal inclination angle was 90° due to the gravity-assisted effect. In the coupling of assistance and resistance, the optimal concentration was 0.3 vol%. The lowest thermal resistance of 0.09 K/W and maximum heat transfer enhancement efficiency of 22.5 % were observed for nanofluids with concentration of 0.3 vol% at a heat load of 80 W and inclination angle of 90°. Compared with miniature loop heat pipe with single nanofluids or binary hybrid nanofluids in the literature, ternary hybrid nanofluids exhibit favorable heat transfer enhancement for FCLTS at lower cost. The research provides theoretical basis and technical support for heat pipe design.

Integrated Scheduling of the Production and Maintenance of Parallel Machine Job-shop Considering Stochastic Machine Breakdowns

The integrated scheduling of production and maintenance can make equipment maintenance in line with the production pace, so as to effectively prevent anormal interruptions of the production process due to equipment failure, and ensure the smooth implementation of the production scheduling plan. Aiming at the parallel machine job-shop environment, and considering stochastic machine failures and different degradation speeds of parallel machines, this paper introduced the minimal maintenance and preventive maintenance strategies to establish an integrated scheduling model for production and maintenance, designed a genetic algorithm based on process coding and binary hybrid coding to solve the model, and verified the correctness of the proposed model and the effectiveness of the algorithm through an instance. This study provided an effective decision-making method for parallel machine job-shop scheduling problems.

Study of high‐temperature proton exchange membrane through one‐step encapsulation of ionic liquid in sulfonated poly(ether ether ketone)

AbstractThe proton exchange membrane (PEM) is the core component of a high‐performance proton exchange membrane fuel cell (PEMFC). Since the traditional PEM has the disadvantages of poor cell performance and high cost, a new kind of PEM with good proton conductivity, low cost and simple preparation should be explored. In this paper, several different binary hybrid membranes were successfully prepared through one‐step encapsulation of different ionic liquids (ILs) in sulfonated poly(ether ether ketone) (SPEEK). The prepared membranes were characterized by scanning electron microscope (SEM), thermogravimetric analysis (TG), Fourier transform infrared spectroscopy (FT‐IR), X‐ray photoelectron spectroscopy (XPS), proton conductivity measurements and dynamic mechanical analysis (DMA). SEM images showed that ILs were fully doped into SPEEK. FT‐IR and XPS proved that SPEEK and IL formed a new chemical bond combined with intermolecular hydrogen bonds. The TG results showed that the binary hybrid membranes could maintain stability even at 300°C. The water uptake and swelling ratio showed that the water absorption capacity of the binary composite membrane played a vital role in improving proton conductivity. The proton conductivity study showed that ILs doping also helped to improve the proton conductivity of the SPEEK membrane. When the doping amount of IL was maintained at 30 wt.%, it has the highest proton conductivity, 25 mS cm−1 at 120°C. It was proved that anhydrous hybrid membrane tetraphenyl imidazole sulfate/SPEEK ([IM2][H2PO4]/SPEEK) could be used in PEMFC at medium temperature.