Abstract
Experiments have shown that graphene-supported Ni-single atom catalysts (Ni-SACs) provide a promising strategy for the electrochemical reduction of CO
2
to CO, but the nature of the Ni sites ...(Ni-N
2
C
2
, Ni-N
3
C
1
, Ni-N
4
) in Ni-SACs has not been determined experimentally. Here, we apply the recently developed grand canonical potential kinetics (GCP-K) formulation of quantum mechanics to predict the kinetics as a function of applied potential (U) to determine faradic efficiency, turn over frequency, and Tafel slope for CO and H
2
production for all three sites. We predict an onset potential (at 10 mA cm
−2
) U
onset
= −0.84 V (vs. RHE) for Ni-N
2
C
2
site and U
onset
= −0.92 V for Ni-N
3
C
1
site in agreement with experiments, and U
onset
= −1.03 V for Ni-N
4
. We predict that the highest current is for Ni-N
4
, leading to 700 mA cm
−2
at U = −1.12 V. To help determine the actual sites in the experiments, we predict the XPS binding energy shift and CO vibrational frequency for each site.
Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained ...vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. In the NGTO approach, each vehicle can make its own strategy on whether a task is offloaded to a multi-access edge computing (MEC) server or a cloud server to maximize its benefits. Our proposed strategy can dynamically adjust the task-offloading probability to acquire the maximum utility for each vehicle. However, we used a best response offloading strategy algorithm for the task-offloading game in order to achieve a unique and stable equilibrium. Numerous simulation experiments affirm that our proposed scheme fulfills the performance guarantees and can reduce the response time and task-failure rate by almost 47.6% and 54.6%, respectively, when compared with the local RSU computing (LRC) scheme. Moreover, the reduced rates are approximately 32.6% and 39.7%, respectively, when compared with a random offloading scheme, and approximately 26.5% and 28.4%, respectively, when compared with a collaborative offloading scheme.
The proper choice of nonprecious transition metals as single atom catalysts (SACs) remains unclear for designing highly efficient electrocatalysts for hydrogen evolution reaction (HER). Herein, ...reported is an activity correlation with catalysts, electronic structure, in order to clarify the origin of reactivity for a series of transition metals supported on nitrogen‐doped graphene as SACs for HER by a combination of density functional theory calculations and electrochemical measurements. Only few of the transition metals (e.g., Co, Cr, Fe, Rh, and V) as SACs show good catalytic activity toward HER as their Gibbs free energies are varied between the range of –0.20 to 0.30 eV but among which Co‐SAC exhibits the highest electrochemical activity at 0.13 eV. Electronic structure studies show that the energy states of active valence dz2 orbitals and their resulting antibonding state determine the catalytic activity for HER. The fact that the antibonding state orbital is neither completely empty nor fully filled in the case of Co‐SAC is the main reason for its ideal hydrogen adsorption energy. Moreover, the electrochemical measurement shows that Co‐SAC exhibits a superior hydrogen evolution activity over Ni‐SAC and W‐SAC, confirming the theoretical calculation. This systematic study gives a fundamental understanding about the design of highly efficient SACs for HER.
The origin of single atom catalytic activity for hydrogen evolution reaction is explored via mutual collaboration of computational prediction and experimental validation. It is found that single atom catalytic activity depends on their valence orbital states, showing excellent correlation with charge transfer and activity descriptors. This systematic study will open a new direction to design heterogeneous catalysts for hydrogen evolution reaction.
Two-dimensional (2D) transition metal dichalcogenides (TMDs) have stimulated the modern technology due to their unique and tunable electronic, optical, and chemical properties. Therefore, it is very ...important to study the control parameters for material preparation to achieve high quality thin films for modern electronics, as the performance of TMDs-based device largely depends on their layer number, grain size, orientation, and morphology. Among the synthesis methods, chemical vapor deposition (CVD) is an excellent technique, vastly used to grow controlled layer of 2D materials in recent years. In this review, we discuss the different growth routes and mechanisms to synthesize high quality large size TMDs using CVD method. We highlight the recent advances in the controlled growth of mono- and few-layer TMDs materials by varying different growth parameters. Finally, different strategies to control the grain size, boundaries, orientation, morphology and their application for various field of are also thoroughly discussed.
The flexible paradigm of the Resource Description Framework (RDF) has accelerated the rate at which raw data is published on the web. Therefore, the volume of generated RDF data has increased ...impressively in the last decade, which promotes the use of compression to manage and reduce the size of RDF datasets. Furthermore, researchers have recently tried to reconstruct convolution and pooling procedures to better suit the structure of graphs and make convolutional neural networks (CNNs) more applicable to RDF graph data. In this study, we propose the Multi Kernel Inductive RDF Graph Convolution Network (MKIR-GCN), which, rather than compressing nodes/edges independently, uses similarities between nodes and the structure of graphs to reduce the size of all nodes and edges simultaneously to efficiently compress the RDF graph. By considering the topology and similarity of a network's nodes, our proposed attention based on similarity for RDF graph pooling (ASGPool) picks the most informative and representative nodes. In dynamic graphs, our proposed MKIR-GCN layer may learn more generic node representations by focusing on diverse characteristics. Through extensive experimentation, we can conclude that the proposed approach significantly improves compression over the existing graph representation learning schemes and RDF graph compression schemes.
Abstract
Developing stable and efficient electrocatalysts is vital for boosting oxygen evolution reaction (OER) rates in sustainable hydrogen production. High-entropy oxides (HEOs) consist of five or ...more metal cations, providing opportunities to tune their catalytic properties toward high OER efficiency. This work combines theoretical and experimental studies to scrutinize the OER activity and stability for spinel-type HEOs. Density functional theory confirms that randomly mixed metal sites show thermodynamic stability, with intermediate adsorption energies displaying wider distributions due to mixing-induced equatorial strain in active metal-oxygen bonds. The rapid sol-flame method is employed to synthesize HEO, comprising five 3d-transition metal cations, which exhibits superior OER activity and durability under alkaline conditions, outperforming lower-entropy oxides, even with partial surface oxidations. The study highlights that the enhanced activity of HEO is primarily attributed to the mixing of multiple elements, leading to strain effects near the active site, as well as surface composition and coverage.
In the era of heterogeneous 5G networks, Internet of Things (IoT) devices have significantly altered our daily life by providing innovative applications and services. However, these devices process ...large amounts of data traffic and their application requires an extremely fast response time and a massive amount of computational resources, leading to a high failure rate for task offloading and considerable latency due to congestion. To improve the quality of services (QoS) and performance due to the dynamic flow of requests from devices, numerous task offloading strategies in the area of multi-access edge computing (MEC) have been proposed in previous studies. Nevertheless, the neighboring edge servers, where computational resources are in excess, have not been considered, leading to unbalanced loads among edge servers in the same network tier. Therefore, in this paper, we propose a collaboration algorithm between a fuzzy-logic-based mobile edge orchestrator (MEO) and state-action-reward-state-action (SARSA) reinforcement learning, which we call the Fu-SARSA algorithm. We aim to minimize the failure rate and service time of tasks and decide on the optimal resource allocation for offloading, such as a local edge server, cloud server, or the best neighboring edge server in the MEC network. Four typical application types, healthcare, AR, infotainment, and compute-intensive applications, were used for the simulation. The performance results demonstrate that our proposed Fu-SARSA framework outperformed other algorithms in terms of service time and the task failure rate, especially when the system was overloaded.
The flexible paradigm of Resource Description Framework (RDF) has accelerated the raw data published on the web. Therefore, the volume of generated RDF data has increased impressively in the last ...decade promoting compression to manage and reduce the size of RDF datasets. Universal RDF compressors can be able to detect and remove redundancy at symbolic, syntactic, or semantic levels. However, these compressors rarely exploit the graph patterns as well as structural regularities in real-world datasets. An efficient approach for compressing the RDF datasets based on the structural properties is HDT (Header-Dictionary-Triple). However, it cannot manage the RDF datasets with named graphs, the regularities of the graph structure, and structural redundancies. Because HDT considers all the triples to reside in the same default graph. Though, the triples of an RDF dataset belong to various named graphs. In this study, we have proposed a novel approach to deal with the above-mentioned challenges. We introduce hybrid TI-GI (Triple Interpreter-Graph Interpreter) to manage the RDF datasets with named graphs and use compact RDF serialization. We also propose RDF-RR (RDF-Redundancy Reducer) and object mapping that detects and removes structural redundancies by identifying the common patterns related to the predicates and objects in the RDF datasets. We employ a differential compressor to discover the frequent graph pattern in a single pass by using the data structure-oriented approach of the dataset. Evaluation of real-world datasets affirms that our proposed approach can substantially reduce the size of the experimental RDF datasets at approximately 30.52%, 24.92%, and 26.96% when compared with the existing HDT, HDT-FoQ (HDT-Focused on Querying) and the 2Tp (two Triple Predicate based index) approaches. Moreover, the indexing time of our proposed system is also reduced at approximately 17.89%, 13.70%, and 9.32% when compared with the HDT, HDT-FoQ, and 2Tp approaches.
Edge computing has emerged as a promising computing paradigm that enables real-time data processing and analysis closer to the data source and boosts decision-making applications in a safe manner. On ...the other hand, the microservice is a new type of architecture that can be dynamically deployed, migrating across edge clouds on demand. Therefore, the combination of these two technologies can provide numerous benefits, including improved performance, reduced latency, and better resource utilization. In this paper, we present a thorough analysis of state-of-the-art research on the use of microservices in edge computing environments. We take into consideration several distinct microservice research directions, including coordination, orchestration, repositories, scheduling, autoscaling, deployment, resource management, and different security issues. Furthermore, we explore the potential applications of microservices in edge computing across various domains. Finally, the unsolved research issues and future directions of emerging trends in this area are also discussed.
Multiaccess edge computing (MEC) enables autonomous vehicles to handle time-critical and data-intensive computational tasks for emerging Internet-of-Vehicles (IoV) applications via computation ...offloading. However, a massive amount of data generated by colocated vehicles is typically redundant, introducing a critical issue due to limited network bandwidth. Moreover, on the edge server side, these computation-intensive tasks further impose severe pressure on the resource-finite MEC server, resulting in low-performance efficiency of applications. To solve these challenges, we model the data redundancy and collaborative task computing scheme to efficiently reduce the redundant data and utilize the idle resources in nearby MEC servers. First, the data redundancy problem is formulated as a set-covering problem according to the spatiotemporal coverage of captured images. Next, we exploit the submodular optimization technique to design an efficient algorithm to minimize the number of images transferred to the MEC servers without degrading the quality of IoV applications. To facilitate the task execution in the MEC server, we then propose a collaborative task computing scheme, where an MEC server intentionally encourages nearby resource-rich MEC servers to participate in a collaborative computing group. Accordingly, a cost model is formulated as an optimization problem, the objective of which is to prompt the MEC server to judiciously allocate computing tasks to nearby MEC servers with the goal of achieving the minimal cost while the latency of tasks is guaranteed. Experimental results show that the proposed scheme can efficiently mitigate data redundancy, conserve network bandwidth consumption, and achieve the lowest cost for processing tasks.
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