Heterogeneous Systems Research Articles

Enhancing the Electrochemical Performance of ZnO-Co3O4 and Zn-Co-O Supercapacitor Electrodes Due to the In Situ Electrochemical Etching Process and the Formation of Co3O4 Nanoparticles

Zinc oxide (ZnO) and materials based on it are often used to create battery-type supercapacitor electrodes and are considered as promising materials for hybrid asymmetric supercapacitors. However, when creating such electrodes, it is necessary to take into account the instability and degradation of zinc oxide in aggressive environments with a non-neutral pH. To the best of our knowledge, studies of the changes in the properties of ZnO-containing electrodes in alkaline electrolytes have not been carried out. In this work, changes in the structure and properties of these electrodes under alkaline treatment were investigated using the example of ZnO-containing composites, which are often used for the manufacturing of supercapacitor electrodes. Supercapacitor electrodes made of two materials containing ZnO were studied: (i) a heterogeneous ZnO-Co3O4 system, and (ii) a hexagonal h-Zn-Co-O solid solution. A comparison was made between the structure and properties of these materials before and after in situ electrochemical oxidation in the process of measuring cyclic voltammetry and galvanostatic charge/discharge. It has been shown that the structure of both nanoparticles of the heterogeneous ZnO-Co3O4 system and the h-Zn-Co-O solid solution changes due to the dissolution of ZnO in the alkaline electrolyte 3.5 M KOH, with the short-term alkaline treatment producing cobalt and zinc hydroxides, and long-term exposure leading to electrochemical cyclic oxidation–reduction, forming cobalt oxide Co3O4. Since the resulting cobalt oxide nanoparticles are immobilized in the electrode structure, a considerable specific capacity of 446 F g−1 or 74.4 mA h g−1 is achieved at a mass loading of 0.0105 g. The fabricated hybrid capacitor showed a good electrochemical performance, with a series resistance of 0.2 Ohm and a capacitance retention of 87% after 10,000 cycles.

High-frequency optimally windowed chirp rheometry for rapidly evolving viscoelastic materials: Application to a crosslinking thermoset

Knowledge of the evolution in the mechanical properties of a curing polymer matrix is of great importance in composite parts or structure fabrication. Conventional rheometry, based on small amplitude oscillatory shear, is limited by long interrogation times. In rapidly evolving materials, time sweeps can provide a meaningful measurement albeit at a single frequency. To overcome this constraint, we utilize a combined frequency- and amplitude-modulated chirped strain waveform in conjunction with a homemade sliding plate piezo-operated rheometer (PZR) and a dual-head commercial rotational rheometer (Anton Paar MCR 702) to probe the linear viscoelasticity of these time-evolving materials. The direct controllability of the PZR, resulting from the absence of any kind of firmware and the microsecond actuator-sensor response renders this device ideal for exploring the advantages of this technique. The high frequency capability allows us to extend the upper limits of the accessible linear viscoelastic spectrum and, most importantly, to shorten the length of the interrogating strain signal (OWCh-PZR) to subsecond scales, while retaining a high time-bandwidth product. This short duration ensures that the mutation number (NMu) is kept sufficiently low, even in fast-curing resins. The method is validated via calibration tests in both instruments, and the corresponding limitations are discussed. As a proof of concept, the technique is applied to a curing vinylester resin. The linear viscoelastic (LVE) spectrum is assessed every 20 s to monitor the rapid evolution in the time and frequency dependence of the complex modulus. Comparison of the chirp implementation, based on parameters such as duration of the experiment, sampling frequency, and frequency range, in a commercial rotational rheometer with the PZR provides further information on the applicability of this technique and its limitations. Finally, FTIR spectroscopy is utilized to gain insights into the evolution of the chemical network, and the gap dependence of the evolving material properties in these heterogeneous systems is also investigated.

Collective relational inference for learning heterogeneous interactions

Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. Revealing interaction laws is of fundamental importance but also particularly challenging due to underlying configurational complexities. These challenges become exacerbated for heterogeneous systems that are prevalent in reality, where multiple interaction types coexist simultaneously and relational inference is required. Here, we propose a probabilistic method for relational inference, which possesses two distinctive characteristics compared to existing methods. First, it infers the interaction types of different edges collectively by explicitly encoding the correlation among incoming interactions with a joint distribution, and second, it allows handling systems with variable topological structure over time. We evaluate the proposed methodology across several benchmark datasets and demonstrate that it outperforms existing methods in accurately inferring interaction types. The developed methodology constitutes a key element for understanding interacting systems and may find application in graph structure learning.

Photogeneration of Hydrogen: Insights from a Pt(II)‐Complex Incorporated into a Covalent Organic Framework

Pt(II)‐based molecular catalysts stand as a prototypical system in hydrogen evolution reactions (HER) owing to their consistently elevated activity levels. Their integration into heterogeneous systems thus provides an ideal platform to develop catalytic materials with optimal atom economy. In this work, by rational molecular design, we have synthesized a novel two‐dimensional photoactive Covalent Organic Framework (COF), wherein the pore walls host a quinoline‐based α‐diimine ligand serving as a coordination site for anchoring a Pt(II) molecular catalyst. Thorough structural analyses, employing X‐ray photoelectron spectroscopy (XPS), infrared spectroscopy (FT‐IR), diffuse reflectance spectroscopy (DR), coupled with DFT calculations, distinctly confirm the Pt(II) complexation through the coordination site of the α‐diimine ligand. The Pt(II)‐metalated COF exhibits photocatalytic activity with a hydrogen evolution rate reaching up to 1300 umol g‐1 h‐1. Nevertheless, the occurrence of platinum nanoparticles in postcatalytic samples, along with reduced photocatalytic activity in the presence of chloride ions, suggests that Pt(II) anchored into the COF backbone might not be the primary catalytic site.

Oscillatory Neural Network-Based Ising Machine Using 2D Memristors.

Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore's law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems.

Task Allocation of Heterogeneous Multi-Unmanned Systems Based on Improved Sheep Flock Optimization Algorithm

The objective of task allocation in unmanned systems is to complete tasks at minimal costs. However, the current algorithms employed for coordinating multiple unmanned systems in task allocation tasks frequently converge to local optima, thus impeding the identification of the best solutions. To address these challenges, this study builds upon the sheep flock optimization algorithm (SFOA) by preserving individuals eliminated during the iterative process within a prior knowledge set, which is continuously updated. During the reproduction phase of the algorithm, this prior knowledge is utilized to guide the generation of new individuals, preventing their rapid reconvergence to local optima. This approach aids in reducing the frequency at which the algorithm converges to local optima, continually steering the algorithm towards the global optimum and thereby enhancing the efficiency of task allocation. Finally, various task scenarios are presented to evaluate the performances of various algorithms. The results show that the algorithm proposed in this paper is more likely than other algorithms to escape from local optima and find the global optimum.

Behavioural Types for Heterogeneous Systems (Position Paper)

Behavioural Types for Heterogeneous Systems (Position Paper)

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments.

The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.

Analysis the interaction of two cylindrical surfaces under shock impact load

The reactions of pipeline systems to shock impact load and the possibilities of the simulation and evaluation of dynamic processes are investigated in the paper. A heterogeneous system is composed of the elements with different properties, e.g. a pipeline with the sediment inside. Impact effect on it is one of the most effective methods to make substantial qualitative and quantitative changes in such system. The mathematical model for calculation of the elastically plastic displacements of the sediment was derived. It allows the analysis of dynamics of the change of the system’s specific state. The paper presents the calculation results of elastically plastic displacements of the substance caused by explosions in pipeline systems.

A novel lightweight CNN for chest X-ray-based lung disease identification on heterogeneous embedded system

AbstractThe global spread of epidemic lung diseases, including COVID-19, underscores the need for efficient diagnostic methods. Addressing this, we developed and tested a computer-aided, lightweight Convolutional Neural Network (CNN) for rapid and accurate identification of lung diseases from 29,131 aggregated Chest X-ray (CXR) images representing seven disease categories. Employing the five-fold cross-validation method to ensure the robustness of our results, our CNN model, optimized for heterogeneous embedded devices, demonstrated superior diagnostic performance. It achieved a 98.56% accuracy, outperforming established networks like ResNet50, NASNetMobile, Xception, MobileNetV2, DenseNet121, and ViT-B/16 across precision, recall, F1-score, and AUC metrics. Notably, our model requires significantly less computational power and only 55 minutes of average training time per fold, making it highly suitable for resource-constrained environments. This study contributes to developing efficient, lightweight networks in medical image analysis, underscoring their potential to enhance point-of-care diagnostic processes.

Development of a New Correlation for Predicting Initial Water Saturation in Carbonate Reservoirs

The Middle East, rich in oil and gas within carbonate rocks, accounts for a significant portion of global reserves, drawing extensive exploration by major oil firms. Unlike Southeast Asia's fracture and cavity-dominated carbonate reservoirs, the Middle East features thick-bedded, pore-structured reservoirs with vast reserves. These complex and varied pore structures cause reservoir inhomogeneity, challenging the technical evaluation of these unconventional reservoirs. Characterization of carbonate reservoirs differs in terms of their mineralogical compositions and heterogenous pore systems from that of clastic reservoirs. Reservoir characterization seeks to build geological and petrophysical models for reservoir simulation. Rock types represent the most crucial characteristics of reservoirs for specialized facies modelling within specific ranges of porosity and permeability. Rock typing is an essential method routinely used by petroleum engineers for characterizing and predicting the reservoir quality of carbonate reservoirs by classifying reservoir rocks into distinct units based on similar petrophysical properties. It is imperative to predict these reservoir properties accurately and precisely. The J-function technique is considered the most effective rock typing procedure. In this study, a new correlation for predicting initial water saturation (Swi) for a reservoir producing from a Permian carbonate formation, located in the Arabian Peninsula, has been developed. The new empirical equation is an augmented Lucia model that utilizes capillary pressure (P�), porosity (), and permeability (k), as independent variables. The coefficient of multiple R2 , the student’s t and F-tests p-value were used in the model evaluation. R2 for the new model was about 0.92, t-test and F-test p-values were much lower than 0.05, indicating that the independent variables are significant. The model was also tested against an independent data set and yielded an R2 of 0.88. Likewise, the new correlation was compared to Lucia’s model and showed better results. The goal of the study is to use the developed correlation in the geostatistical modeling of connate water saturation for analogous formations in the region.

Montmorillonite-Silica-Graphene oxide composite incorporating with chiral thiourea for the Strecker reaction

The present study has successfully intercalated graphene oxide (GO) nanosheets and silica into the montmorillonite (MMT) interlayer and deposited them onto its external surface to make montmorillonite, silica, and graphene oxide composite (MMT-silica-GO). The as-prepared composite demonstrates improved thermal stability compared to neat MMT and GO. It was followed by incorporating chiral thiourea (CTU) scaffold into MMT-silica-GO-based composite to establish chiral MMT-silica-GO-CTU-based heterogeneous system and tested over Strecker reaction, revealing high activity and selectivity. FTIR, Raman spectroscopy, powder X-ray diffraction, SAXS, XPS, BET, SEM, EDAX, and thermogravimetric analysis were used to characterize the samples. It was credited to the synergistic effect between the composite components that enhanced access to active sites and dispersion of catalyst. Across the board, the chiral MMT-silica-GO-CTU-based heterogeneous system appears to be a promising candidate for the Strecker reaction.

Fully distributed event-triggered formation-containment control for heterogeneous multi-agent systems with guaranteed positive MTI

In this work, we address the event-triggered output formation-containment control problem for heterogeneous multi-agent systems with multiple leaders. The aim is to achieve formation of follower agents and containment of multiple leaders by designing fully distributed event-triggered control strategy, while the minimum triggering interval (MTI) is guaranteed to be strictly positive, which is a stronger condition than simply excluding Zeno behavior. First, an output feedback fully distributed control law is proposed. Then, a novel event-triggering mechanism (ETM) is designed by introducing an auxiliary function with time-varying coefficients. By means of the auxiliary function, the time it takes for the auxiliary function to decrease from given initial condition to zero can be imposed to be the MTI. Moreover, a novel Lyapunov function depending on the auxiliary function and error term is constructed for stability analysis and control design. Finally, the effectiveness of the obtained results is illustrated by simulation results.

Influence of shear deformation on the formation of dynamic structure in protic ionic liquids. Representation of the temperature dependence of viscosity within the framework of Angell’s fragility concept

The rheological properties of a liquid containing two types of basic centers and hydroxyl groups (BGE-Im), as well as protic ionic liquids (ILs) derived from it, were studied in a wide range of shear rates (γ̇) (10−6–103 s−1) at various temperatures from 10 to 50 °C. Viscosity (η) of the BGE-Im does not depend on the scanning direction of shear stress (σ), i.e. this compound behaves like a Newtonian fluid over the entire range of σ. The dependence log η (1/T) of BGE-Im in Arrhenius coordinates is linear. Protonation of basic centers of BGE-Im leads to a strong increase in the viscosity of ILs obtained on its basis, and the temperature dependence of the viscosity of these ILs is described by the Vogel–Fulcher–Tammann (VFT) equation. It has been established that in the region of low shear rates, the viscosity of the ILs increases with decreasing shear rate, which leads to the appearance of a yield stress (σY). This indicates the formation of a fluctuation rheopectic structure in the ILs. In this sense, ILs are heterogeneous systems and their behavior is similar to that of highly concentrated suspensions and emulsions. For the first time, a quantitative comparison between the parameters of the VFT equation, the fragility (according to Angell’s model) of ILs, the relative coordination number of ILs molecules and the rheological yield stress characterizing the shear-induced fluctuation rheopectic structure of ILs has been made and the relationship between these characteristics has been shown.

Weighted probability kernel multi-granularity three-way decision integrating GRA and its application in medical diagnosis

Three-way decision, an outstanding method to handle decision-making uncertainties, relies on essentially the loss functions derived from the Bayesian risk decision process. Actually, there are plentiful loss functions that depend on the subjective judgment of decision-makers under different decision scenarios, lacking uniform and objective measurement frameworks. This study pays attention to the real clinical diagnosis, and constructs a weighted probability kernel multi-granularity three-way decision method (WKMG-TWD) integrating grey relation analysis (GRA) over a multi-source heterogeneous decision information system (MHDIS). The method establishes a standardized data-driven calculation framework of loss functions. Foremost, the multi-kernel probabilistic similarity is defined and granularity's weights with knowledge consistency are explored. Subsequently, a weighted probability kernel multi-granularity rough set (WKMGRS) is constructed in this paper. Secondly, to introduce the three-way decision, this study proposes the cost-sensitive individual loss functions considering the correlation determined by GRA between decision objects and different decision classes. Ultimately, this study establishes and applies a three-way iterative classification model to hypertension diagnosis. The experimental results confirm the effectiveness and superiority of the model. The main contribution of this paper is twofold. One is to offer a uniform calculation framework for loss functions and granularity's weights. The other is to furnish invaluable guidance for solving complex medical decision-making problems.

The Method of Finite Averages: A rigorous upscaling methodology for heterogeneous porous media

Rigorous upscaling techniques offer accurate and computationally-efficient strategies for modeling the average behaviors of multi-physical, multiscale phenomena in geological porous media. However, such techniques often rely on a variety of methodological assumptions that prohibit their rigorous application to practical systems (e.g., systems involving heterogeneous porous media, system-scale boundary conditions, and fine-scale dynamics that are not diffusion-dominant). In this work, we aim to formulate an upscaling methodology with few methodological assumptions to provide high levels of model generality and foster the utilization of rigorously-derived upscaled models in practice. In particular, we introduce the Method of Finite Averages (MoFA), a novel upscaling methodology for rigorously modeling heterogeneous porous media and system-scale boundary conditions. We detail MoFA’s implementation for the advective-diffusive transport of a single species and compare the methodology with classic numerical techniques, as well as other rigorous upscaling techniques, to highlight MoFA’s unique combination of rigor and generality. We then validate the derived model while demonstrating its benefits in three numerical experiments. The results suggest that (1.) the applicability and a priori error guarantees of MoFA models do not directly depend on system geometry, (2.) a model’s applicability and error guarantees can be can arbitrarily expanded and reduced, respectively, with further computational expense, and (3.) downscaling with MoFA provides an efficient strategy for generating accurate pore-scale solutions from upscaled results. Ultimately, the results evidence that upscaled models can be rigorously derived for heterogeneous porous media systems and resolved in a fraction of the time it takes to perform the equivalent pore-scale simulations.

Distributed Multihop Task Offloading in Massive Heterogeneous IoT Systems

Distributed Multihop Task Offloading in Massive Heterogeneous IoT Systems

GOFL: An Accurate and Efficient Federated Learning Framework Based on Gradient Optimization in Heterogeneous IoT Systems

GOFL: An Accurate and Efficient Federated Learning Framework Based on Gradient Optimization in Heterogeneous IoT Systems

Treatment of mature landfill leachate using chemical and electrical Fenton with novel Fe-loaded GAC heterogeneous catalysts

In this study, heterogeneous Fenton catalysts were prepared by loading iron oxides on granular activated carbon (GAC), with which as heterogeneous Fenton catalysts, mature landfill leachate (MLL) was treated by chemical and electrical Fenton method. For chemical Fenton treatment of MIL, the optimum conditions were optimised by orthogonal experiments and the removal of COD, NH3-N and colour reached 93.7 %, 65.5 % and 84 % respectively for static experiments, whereas the heterogeneous Fenton fluidized bed reactor removed >90 % of COD in all the 12 h of continuous operation. For electrical Fenton treatment of MIL, the removal rates of COD, colour, NH3-N, and TN were 97.4 %, 99 %, 100 %, and 94.2 %, respectively under the optimum treatment conditions of pH = 5, the GAC/α-FeOOH catalyst dosage of 13.3 g L−1, and the applied voltage of 8 V with the energy consumption of only 0.23 kWh (g COD)−1. Upon characterization, humic acid, fulvic acid and xanthohumic acid in the MLL were completely degraded and the complex macromolecules such as polycyclic aromatic hydrocarbons and heterocyclic rings were reduced and converted to simple small molecules such as alkanes. The quenching experiments verified that the ·OH produced in the heterogeneous electrical Fenton system plays an important role in the degradation of MLL, while the removal of NH3-N also relies on indirect oxidation with the active chlorine produced by the electrochemical system. GAC-based heterogeneous Fenton catalysts with high catalytic capacity and easy recycling have good application prospects in treating MLL.

Finite-Time Time-Varying Formation Tracking for Heterogeneous Nonlinear Multiagent Systems Using Adaptive Output Regulation.

The finite-time output time-varying formation tracking (TVFT) problem for heterogeneous nonlinear multiagent system (MAS) is investigated in this article, where the dynamics of the agents can be nonidentical, and leader's input is unknown. The target of this article is that the outputs of followers need to track leader's output and realize the desired formation in finite time. First, for removing the assumption that all agents are required to know the information of leader's system matrices and the upper boundary of its unknown control input in previous studies, a kind of finite-time observer is constructed by exploiting the neighboring information, which can estimate not only the leader's state and system matrices but also can compensate for the effects of unknown input. On the basis of the developed finite-time observers and adaptive output regulation method, a novel finite-time distributed output TVFT controller is proposed with the help of the technique of coordinate transformation by introducing an extra variable, which removes the assumption that the generalized inverse matrix of follows' input matrix needs to be found in the existing results. By means of the Lyapunov and finite-time stability theory, it is proven that the expected finite-time output TVFT can be realized by the considered heterogeneous nonlinear MASs within a finite time. Finally, simulation results demonstrate the efficacy of the proposed approach.