Effect of bipolar-plates design on corrosion,mass and heat transfer in proton-exchange membrane fuel cells and water electrolyzers:A review

Attaining a decarbonized and sustainable energy system,which is the core solution to global energy issues,is accessible through the development of hydrogen energy.Proton-exchange membrane water electrolyzers(PEMWEs)are promising devices for hydrogen production,given their high efficiency,rapid responsiveness,and compactness.Bipolar plates account for a relatively high percentage of the total cost and weight compared with other components of PEMWEs.Thus,optimization of their design may accelerate the promotion of PEMWEs.This paper reviews the advances in materials and flow-field design for bipolar plates.First,the working conditions of proton-exchange membrane fuel cells(PEMFCs)and PEMWEs are compared,including reaction direction,operating temperature,pressure,input/output,and potential.Then,the current research status of bipolar-plate substrates and surface coatings is summarized,and some typical channel-rib flow fields and porous flow fields are presented.Furthermore,the effects of materials on mass and heat transfer and the possibility of reducing corrosion by improving the flow field structure are explored.Finally,this review discusses the potential directions of the development of bipolar-plate design,including material fabrication,flow-field geometry optimization using threedimensional printing,and surface-coating composition optimization based on computational materials science.

Strategic comparison of membrane-assisted and membrane-less water electrolyzers and their potential application in direct seawater splitting(DSS)

Electrocatalytic splitting of water by means of renewable energy as the electricity supply is one of the most promising methods for storing green renewable energy as hydrogen. Although two-thirds of the earth’s surface is covered with water, there is inadequacy of freshwater in most parts of the world. Hence, splitting seawater instead of freshwater could be a truly sustainable alternative. However, direct seawater splitting faces challenges because of the complex composition of seawater. The composition, and hence, the local chemistry of seawater may vary depending on its origin, and in most cases, tracking of the side reactions and standardizing and customizing the catalytic process will be an extra challenge. The corrosion of catalysts and competitive side reactions due to the presence of various inorganic and organic pollutants create challenges for developing stable electro-catalysts. Hence, seawater splitting generally involves a two-step process, i.e., purification of seawater using reverse osmosis and then subsequent fresh water splitting. However, this demands two separate chambers and larger space, and increases complexity of the reactor design. Recently, there have been efforts to directly split seawater without the reverse osmosis step. Herein, we represent the most recent innovative approaches to avoid the two-step process, and compare the potential application of membrane-assisted and membrane-less electrolyzers in direct seawater splitting(DSS). We particularly discuss the device engineering, and propose a novel electrolyzer design strategies for concentration gradient based membrane-less microfluidic electrolyzer.

Technical factors affecting the performance of anion exchange membrane water electrolyzer

Anion exchange membrane(AEM)electrolysis is a promising membrane-based green hydrogen production technology.However,AEM electrolysis still remains in its infancy,and the performance of AEM electrolyzers is far behind that of well-developed alkaline and proton exchange membrane electrolyzers.Therefore,breaking through the technical barriers of AEM electrolyzers is critical.On the basis of the analysis of the electrochemical performance tested in a single cell,electrochemical impedance spectroscopy,and the number of active sites,we evaluated the main technical factors that affect AEM electrolyzers.These factors included catalyst layer manufacturing(e.g.,catalyst,carbon black,and anionic ionomer)loadings,membrane electrode assembly,and testing conditions(e.g.,the KOH concentration in the electrolyte,electrolyte feeding mode,and operating temperature).The underlying mechanisms of the effects of these factors on AEM electrolyzer performance were also revealed.The irreversible voltage loss in the AEM electrolyzer was concluded to be mainly associated with the kinetics of the electrode reaction and the transport of electrons,ions,and gas-phase products involved in electrolysis.Based on the study results,the performance and stability of AEM electrolyzers were significantly improved.

Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing

To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.

Energy-Efficient Approaches for a Machine Tool Building in a University through Field Measurement and Energy Modelling

The heating,ventilating,and air conditioning(HVAC)system consumes nearly 50%of the building’s energy,especially in Taiwan with a hot and humid climate.Due to the challenges in obtaining energy sources and the negative impacts of excessive energy use on the environment,it is essential to employ an energy-efficient HVAC system.This study conducted the machine tools building in a university.The field measurement was carried out,and the data were used to conduct energymodelling with EnergyPlus(EP)in order to discover some improvements in energy-efficient design.The validation between fieldmeasurement and energymodelling was performed,and the error rate was less than 10%.The following strategies were proposed in this study based on several energy-efficient approaches,including room temperature settings,chilled water supply temperature settings,chiller coefficient of performance(COP),shading,and building location.Energy-efficient approaches have been evaluated and could reduce energy consumption annually.The results reveal that the proposed energy-efficient approaches of room temperature settings(3.8%),chilled water supply temperature settings(2.1%),chiller COP(5.9%),using shading(9.1%),and building location(3.0%),respectively,could reduce energy consumption.The analysis discovered that using a well-performing HVAC system and building shading were effective in lowering the amount of energy used,and the energy modelling method could be an effective and satisfactory tool in determining potential energy savings.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

Advances and challenges of electrolyzers for large-scale CO_(2) electroreduction

CO_(2) electroreduction(CO_(2) ER)to high value-added chemicals is considered as a promising technology to achieve sustainable carbon neutralization.By virtue of the progressive research in recent years aiming at design and understanding of catalytic materials and electrolyte systems,the CO_(2) ER performance(such as current density,selectivity,stability,CO_(2) conversion,etc.)has been continually increased.Unfortunately,there has been relatively little attention paid to the large-scale CO 2 electrolyzers,which stand just as one obstacle,alongside series-parallel integration,challenging the practical application of this infant technology.In this review,the latest progress on the structures of low-temperature CO_(2) electrolyzers and scale-up studies was systematically overviewed.The influence of the CO_(2) electrolyzer configurations,such as the flow channel design,gas diffusion electrode(GDE)and ion exchange membrane(IEM),on the CO_(2) ER performance was further discussed.The review could provide inspiration for the design of large-scale CO_(2) electrolyzers so as to accelerate the industrial application of CO_(2) ER technology.

Transcriptomics integrated with metabolomics reveals the mechanism of CaCl_(2)-HCl electrolyzed water-induced glucosinolate biosynthesis in broccoli sprouts

Glucosinolates are important phytochemicals in Brassicaceae.We investigated the effect of CaCl_(2)-HCl electrolyzed water(CHEW)on glucosinolates biosynthesis in broccoli sprouts.The results showed that CHEW treatment significantly decreased reactive oxygen species(ROS)and malondialdeh yde(MDA)contents in broccoli sprouts.On the the 8^(th)day,compared to tap water treatment,the the total glucosinolate content of broccoli sprouts with CHEW treatment increased by 10.6%and calcium content was dramatically enhanced from 14.4 mg/g DW to 22.7 mg/g DW.Comparative transcriptome and metabolome analyses revealed that CHEW treatment activated ROS and calcium signaling transduction pathways in broccoli sprouts and they interacted through MAPK cascades.Besides,CHEW treatment not only promoted the biosynthesis of amino acids,but also enhanced the expression of structural genes in glucosinolate synthesis through transcription factors(MYBs,bHLHs,WRKYs,etc.).The results of this study provided new insights into the regulatory network of glucosinolates biosynthesis in broccoli sprouts under CHEW treatment.

NOMA-Based Energy-Efficient Task Scheduling in Vehicular Edge Computing Networks: A Self-Imitation Learning-Based Approach

Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries.In addition,Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently.Therefore,Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important,especially in highly-dynamic vehicular edge computing networks.The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers.Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields,especially in time-varying networks.In this paper,we first introduce related MEC technologies in vehicular networks.Then,we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL,with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers.Numerical results demonstrate that the proposed algorithm outperforms other methods.

Optimal Energy-Efficient Transmission for Hybrid Spectrum Sharing in Cooperative Cognitive Radio Networks

In order to improve the energy efficiency(EE)in cognitive radio(CR),this paper investigates the joint design of cooperative spectrum sensing time and the power control optimization problem for the secondary user systems to achieve the maximum energy efficiency in a cognitive network based on hybrid spectrum sharing,meanwhile considering the maximum transmit power,user quality of service(QoS)requirements,interference limitations,and primary user protection.The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem.The Dinkelbach’s method is adopted to solve this problem and to transform the non-convex optimization problem in fractional form into an equivalent optimization problem in the form of subtraction.Then,an iterative power allocation algorithm is proposed to solve the optimization problem.The simulation results show the effectiveness of the proposed algorithms for energy-efficient resource allocation in the cognitive network.

An effective oxygen electrode based on Ir0.6Sn0.4O2 for PEM water electrolyzers

An effective oxygen evolution electrode with Ir0.6Sn0.4O2 was designed for proton exchange membrane(PEM)water electrolyzers.The anode catalyst layer exhibits a jagged structure with smaller particles and pores,which provide more active sites and mass transportation channels.The prepared IrSn electrode showed a cell voltage of 1.96 V at 2.0 A cm^-2 with Ir loading as low as 0.294 mg cm^-2.Furthermore,Ir Sn electrode with different anode catalyst loadings was investigated.The IrS n electrode indicates higher mass current and more stable cell voltage than the commercial Ir Black electrode at low loading.

ENERGY-EFFICIENT MICROWAVE COMPONENTS FOR MOBILE COMMUNICATION

In the coexisted world of 3G,4G,5G and many other specialized wireless communication systems,billions of connections could be existing for various information transmission types.Unluckily,data show that the increase of network capacity is heavily more than the increase of the network energy efficiency in recent years,which could lead to more energy consumption per transmitted bit in the future network.As basic units in mobile communication systems,microwave/RF components and modules play key roles

Energy-Efficient UAV Trajectory Design for Backscatter Communication: A Deep Reinforcement Learning Approach

Recently,backscatter communication(BC)has been introduced as a green paradigm for Internet of Things(IoT).Meanwhile,unmanned aerial vehicles(UAVs)can serve as aerial base stations(BSs)to enhance the performance of BC system thanks to their high mobility and flexibility.In this paper,we investigate the problem of energy efficiency(EE)for an energy-limited backscatter communication(BC)network,where backscatter devices(BDs)on the ground harvest energy from the wireless signal of a flying rotary-wing quadrotor.Specifically,we first reformulate the EE optimization problem as a Markov decision process(MDP)and then propose a deep reinforcement learning(DRL)algorithm to design the UAV trajectory with the constraints of the BD scheduling,the power reflection coefficients,the transmission power,and the fairness among BDs.Simulation results show the proposed DRL algorithm achieves close-to-optimal performance and significant EE gains compared to the benchmark schemes.

QoS-Aware Energy-Efficient Task Scheduling on HPC Cloud Infrastructures Using Swarm-Intelligence Meta-Heuristics

Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service(HPCaaS)to users for executing HPC applications.However,the broader use of the Cloud services,the rapid increase in the size,and the capacity of Cloud data centers bring a remarkable rise in energy consumption leading to a significant rise in the system provider expenses and carbon emissions in the environment.Besides this,users have become more demanding in terms of Quality-of-service(QoS)expectations in terms of execution time,budget cost,utilization,and makespan.This situation calls for the design of task scheduling policy,which ensures efficient task sequencing and allocation of computing resources to tasks to meet the trade-off between QoS promises and service provider requirements.Moreover,the task scheduling in the Cloud is a prevalent NP-Hard problem.Motivated by these concerns,this paper introduces and implements a QoS-aware Energy-Efficient Scheduling policy called as CSPSO,for scheduling tasks in Cloud systems to reduce the energy consumption of cloud resources and minimize the makespan of workload.The proposed multi-objective CSPSO policy hybridizes the search qualities of two robust metaheuristics viz.cuckoo search(CS)and particle swarm optimization(PSO)to overcome the slow convergence and lack of diversity of standard CS algorithm.A fitness-aware resource allocation(FARA)heuristic was developed and used by the proposed policy to allocate resources to tasks efficiently.A velocity update mechanism for cuckoo individuals is designed and incorporated in the proposed CSPSO policy.Further,the proposed scheduling policy has been implemented in the CloudSim simulator and tested with real supercomputing workload traces.The comparative analysis validated that the proposed scheduling policy can produce efficient schedules with better performance over other well-known heuristics and meta-heuristics scheduling policies.

A Novel Negative Multinomial Distribution Based Energy-Efficient Reputation Modeling for Wireless Sensor Networks(WSNs)

In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of cryptographic algorithms.As a complementary mechanism,reputation has been applied to WSNs.Different from most reputation schemes that were based on beta distribution,negative multinomial distribution was deduced and its feasibility in the reputation modeling was proved.Through comparison tests with beta distribution based reputation in terms of the update computation,results show that the proposed method in this research is more energy-efficient for the reputation update and thus can better prolong the lifespan of WSNs.

Energy-efficient resource management for CCFD massive MIMO systems in 6G networks

This paper presents a co-time co-frequency fullduplex(CCFD)massive multiple-input multiple-output(MIMO)system to meet high spectrum efficiency requirements for beyond the fifth-generation(5G)and the forthcoming the sixth-generation(6G)networks.To achieve equilibrium of energy consumption,system resource utilization,and overall transmission capacity,an energy-efficient resource management strategy concerning power allocation and antenna selection is designed.A continuous quantum-inspired termite colony optimization(CQTCO)algorithm is proposed as a solution to the resource management considering the communication reliability while promoting energy conservation for the CCFD massive MIMO system.The effectiveness of CQTCO compared with other algorithms is evaluated through simulations.The results reveal that the proposed resource management scheme under CQTCO can obtain a superior performance in different communication scenarios,which can be considered as an eco-friendly solution for promoting reliable and efficient communication in future wireless networks.

Energy-efficient reconfigurable AI processor

High computational energy-efficiency and rapid real-timeresponse are the major concerns for applications of artificial intelligencein low-power mobile and Internet of Things deviceswith limited storage capacity. Due to the outstanding superiorityof less memory requirement, low computation overheadand negligible accuracy degradation, deep neural networkswith binary/ternary weights (BTNNs) have been widely adoptedto replace traditional full-precision neural networks.

Guest Editorial——Special Section on the Energy-Efficient Technologies

In the past few years, energy-efficient technologies get more and more scientific and technological interest as the energy bottleneck stops the ＂Moore＇s law＂ according to the reports of International Roadmap for Semiconductors （ITRS）. Moreover, people start to take care seriously of the environmental impact due to the Dower dissioation.

Activation of iridium site by anchoring ruthenium atoms on defects for efficient anodic catalyst in polymer electrolyte membrane water electrolyzers

1.Introduction Hydrogen is an ideal energy carrier to tackle the energy crisis and greenhouse effect,because of its high energy density and low emission.The production,storage and transportation of hydrogen are key factors to the practical application of hydrogen energy.As the scientific and technological understanding of the electrochemical devices was advancing in the past few decades,water electrolyzers based on the proton exchange membrane (PEM) have attracted much focus for its huge potential on the production of hydrogen via water splitting.PEM electrolyzers use perfluorinated sulfonic acid (PFSA) based membranes as the electrolyte.

Energy-Efficient Full-Duplex UAV Relaying with Trajectory Optimization and Power Control in Maritime Communication Environments

This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle(UAV) assisted full-duplex mobile relaying in maritime communication environments.Taking the speed and the acceleration of the UAV and the information-causality constraints into consideration,the energy-efficiency of the system under investigation is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay nodes.The optimization problem is non-convex and thus cannot be solved directly.Therefore,it is decoupled into two subproblems.One sub-problem is for the transmit power control at the source and the UAV relay nodes,and the other aims at optimizing the UAV s flight trajectory.By using the Lagrangian dual and Dinkelbach methods,the two sub-problems are solved,leading to an iterative algorithm for the joint design of transmit power control and trajectory optimization.Computer simulations demonstrated that by conducting the proposed algorithm,the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions,and the proposed algorithm can achieve signiflcantly higher energy efficiency as compared with the other benchmark schemes.