Recent advances in electrocatalysts for seawater splitting in hydrogen evolution reaction

Electrocatalytic water splitting is an important method to produce green and renewable hydrogen (H₂). One of the hindrances for wide applications of electrocatalysis in H₂ production is the lack of freshwater resources. Comparatively, seawater splitting has become an effective approach for large-scale H₂ production due to its abundant reserves. However, the increased complexity of seawater content emerged more problems in electrocatalytic seawater splitting. Recently, various strategies have been reported on improving the performance of electrocatalysts applied in seawater. Herein, this review firstly analyzed the mechanisms and challenges of electrocatalytic seawater splitting to evolve H₂, and summarized the recent progress on H₂ production in electrocatalytic seawater splitting. Furthermore, suggestions for future work have been provided for guidance.     

A review of hydrogen production processes by photocatalytic water splitting – From atomistic catalysis design to optimal reactor engineering

‘Renewable energy is an essential part of our strategy of decarbonization, decentralization, as well as digitalization of energy.’ – Isabelle Kocher.Current climate, health and economic condition of our globe demands the use of renewable energy and the development of novel materials for the efficient generation, storage and transportation of renewable energy. Hydrogen has been recognised as one of the most prominent carriers and green energy source with challenging storage, enabling decarbonization. Photocatalytic H₂ (green hydrogen) production processes are targeting the intensification of separated solar energy harvesting, storage and electrolysis, conventionally yielding O₂/H₂. While catalysis is being investigated extensively, little is done on bridging the gap, related to reactor unit design, optimisation and scaling, be it that of material or of operation. Herein, metals, oxides, perovskites, nitrides, carbides, sulphides, phosphides, 2D structures and heterojunctions are compared in terms of parameters, allowing for efficiency, thermodynamics or kinetics structure–activity relationships, such as the solar-to-hydrogen (STH). Moreover, prominent pilot systems are presented summarily.     

Investigation of liquid water heterogeneities in large area proton exchange membrane fuel cells using a Darcy two-phase flow model in a multiphysics code

Proper management of the liquid water and heat produced in proton exchange membrane (PEM) fuel cells remains crucial to increase both its performance and durability. In this study, a two-phase flow and multicomponent model, called two-fluid model, is developed in the commercial COMSOL Multiphysics® software to investigate the liquid water heterogeneities in large area PEM fuel cells, considering the real flow fields in the bipolar plate. A macroscopic pseudo-3D multi-layers approach has been chosen and generalized Darcy's relation is used both in the membrane-electrode assembly (MEA) and in the channel. The model considers two-phase flow and gas convection and diffusion coupled with electrochemistry and water transport through the membrane. The numerical results are compared to one-fluid model results and liquid water measurements obtained by neutron imaging for several operating conditions. Finally, according to the good agreement between the two-fluid and experimentation results, the numerical water distribution is examined in each component of the cell, exhibiting very heterogeneous water thickness over the cell surface.     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

The dynamic effect of Micro-MHD convection on bubble grown at a horizontal microelectrode

Relatively low efficiency is the biggest obstacle to the popularization of water electrolysis, which is a particularly feasible way to produce super-pure hydrogen. Imposing a magnetic field can increase the hydrogen production efficiency of water electrolysis. However, the enhancement's detailed mechanism still lacks an insightful understanding of the bubbles' micro vicinity. Our recent work aims to understand why the micro-magnetohydrodynamic (MHD) convection hinders single bubbles' detachment on the microelectrode. A water electrolysis experiment by microelectrode is performed under an electrode-normal magnetic field, and dynamic analysis of the single bubble growing on microelectrodes is performed. The variation of bubble diameter with time in the presence or absence of the magnetic field was measured, and the forces acting on the bubble were quantified. The result shows that the micro-MHD convection, induced by Lorentz force, can give rise to a downward hydrodynamic pressure force that will not appear in large-scale MHD convection. This force can be of the same magnitude as the surface tension, so it dramatically hinders bubbles' detachment. Besides, the Kelvin force provides a new potential way for further improving the efficiency of water electrolysis.     

Designing independent water transport channels to improve water flooding in ultra-thin nanoporous film cathodes for PEMFCs

Utilization of 3D nanostructured Pt cathodes could obviously improve performances of proton exchange membrane fuel cells (PEMFCs) owing to the reduced tortuosity and the bi-continuous nanoporous structure. However, these cathodes usually suffer from the flooding problem ascribed to the ionomer-free and nanoscale pores which are more susceptible to water condensation. In this paper, ultra-thin nanoporous metal films (100 nm) were utilized to construct PEMFC cathodes and independent transport channels were designed separately for water and gas aiming at the flooding problem. Nanoporous gold (NPG) film was used as the model support for loading Pt nanoparticles owing to its controllable and stable structure. After optimizing the polytetrafluoroethylene (PTFE) content and carbon loading in the gas diffusion layer (GDL), plasma treatment under O₂ atmosphere was used to pattern the GDL with independent water transport channels. The obtained liquid permeation coefficients and oxygen gains demonstrated the obviously improved water and O₂ transport. By using a home-made optimized GDL and a nanoporous film cathode with pore size ～60 nm, the flooding problem could be facilely solved. With a Pt loading of ～16 μg cm^?2, this 3D nanostructured cathode exhibits a PEMFC performance of ～957 mW cm^?2 at 80 °C. The Pt power efficiency is about 4 times higher than that of the commercial Pt/C cathode (50 μg cm^?2, 756 mW cm^?2). Obviously, this study provides a simple but effective methodology to solve the water flooding problem in the ultra-thin nanoporous film cathodes which is applicable for other types of 3D nanostructured PEMFC cathodes.     

Numerical study of entropy generation in Darcy-Forchheimer (D-F) Bödewadt flow of CNTs

Three-dimensional Bödewadt flow (fluid rotates at a large enough distance from the stationary plate) of carbon nanomaterial is examined. Single walled and multi walled CNTs are dissolved in water and gasoline oil baseliquids. Darcy-Forchheimer porous medium is considered. Stationary disk is further stretched linearly in radial direction. Heat transfer effect is examined in presence of radiation and convection. Effect of viscous dissipation is accounted. Entropy generation rate is studied. By using adequate transformation (von Kármán relations), the flow field equations (PDEs) are transmitted into ODEs. Solutions to these ODEs are constructed via implementation of shooting method (bvp4c). In addition to Entropy generation rate, Bejan number, heat transfer rate (Nusselt number), skin friction and temperature of fluid are examined through involved physical parameters. Axial component of velocity intensifies with increment in nanoparticles volume fraction and ratio of stretching rate to angular velocity parameter while it decays with higher porosity parameter. Higher nanoparticles volume fraction and porosity parameter lead to decay in radial as well as tangential component of velocity. However it enhances with higher ratio of stretching rate to angular velocity parameter. Temperature of fluid directly varies with higher ratio of stretching rate to angular velocity parameter, radiation parameter, Eckert number, Biot number and nanoparticles volume fraction. Rate of Entropy generation is reduced with higher estimations of porosity parameter, nanoparticles volume fraction and radiation parameter. Skin friction coefficient decays with higher porosity parameter and ratio of stretching rate to angular velocity parameter. Intensification in porosity parameter, nanoparticles volume fraction and Biot number leads to higher Nusselt number. Prominent impact is shown by multiple-walled CNTs with gasoline oil basefluid than single-walled CNTs with water basefluid.     

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.     

Fault handling in large water networks with online dictionary learning

Fault detection and isolation in water distribution networks is an active topic due to the nonlinearities of flow propagation and recent increases in data availability due to sensor deployment. Here, we propose an efficient two-step data driven alternative: first, we perform sensor placement taking the network topology into account; second, we use incoming sensor data to build a network model through online dictionary learning. Online learning is fast and allows tackling large networks as it processes small batches of signals at a time. This brings the benefit of continuous integration of new data into the existing network model, either in the beginning for training or in production when new data samples are gathered. The proposed algorithms show good performance in our simulations on both small and large-scale networks.     

Correlation between motion of source bender element and feedback signal induced by self-monitoring

Since the introduction of bender element tests to soil testing, the reliability of the estimated travel time has been the most serious problem. The author has previously shown a potential solution whereby removing the response of the bender element subsystem from the whole response could dramatically improve the accuracy of the travel time estimation. In order to lay the foundation for estimating the response of the bender element subsystem, this paper examines the correlation between the displacements of the element and the induced feedback signals by employing a self-monitoring element. The response of the self-monitoring element is modeled as a transfer function involving two internal transfer functions that relate the input signals to the displacements and the displacements to the feedback signals, respectively. Using a laser displacement sensor, the displacements are directly measured through the entire surface and reveal the three-dimensional bending motion of the element oscillating in both longitudinal and width directions. The feedback signals are similar to, but inconsistent with, the tip displacements, and an attempt is made to correct the feedback signals. Finally, a conclusion is given on the potential for estimating the response of the bender element subsystem using the self-monitoring element.