An analytical model for gas leakage through contact interface in proton exchange membrane fuel cells

Sealing performance between two contacting surfaces is of significant importance to stable operation of proton exchange membrane (PEM) fuel cells. In this work, an analytical micro-scale approach is first established to predict the gas leakage in fuel cells. Gas pressure and uneven pressure distribution at the interface are also included in the model. At first, the micro tortuous leakage path at the interface is constructed by introducing contact modelling and fractal porous structure theory. In order to obtain the leakage at the entire surface, contact pressure distribution is predicted based on bonded elastic layer model. The gas leakage through the discontinuous interface can be obtained with consideration of convection and diffusion. Then, experiments are conducted to validate the numerical model, and good agreement is obtained between them. Finally, influences of surface topology, gasket compression and gasket width on leakage are studied based on the model. The results show that gas leakage would be greatly amplified when the asperity standard deviation of surface roughness exceeds 1.0 μm. Gaskets with larger width and smaller thickness are beneficial to sealing performance. The model is helpful to understand the gas leakage behavior at the interface and guide the gasket design of fuel cells.     

A blockchain-based audit approach for encrypted data in federated learning

The development of data-driven artificial intelligence technology has given birth to a variety of big data applications. Data has become an essential factor to improve these applications. Federated learning, a privacy-preserving machine learning method, is proposed to leverage data from different data owners. It is typically used in conjunction with cryptographic methods, in which data owners train the global model by sharing encrypted model updates. However, data encryption makes it difficult to identify the quality of these model updates. Malicious data owners may launch attacks such as data poisoning and free-riding. To defend against such attacks, it is necessary to find an approach to audit encrypted model updates. In this paper, we propose a blockchain-based audit approach for encrypted gradients. It uses a behavior chain to record the encrypted gradients from data owners, and an audit chain to evaluate the gradients’ quality. Specifically, we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation, ensuring the availability of aggregated gradient. In addition, we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients. Through security analysis and experimental evaluation, we demonstrate that our approach can defend against malicious gradient attacks in federated learning.     

An optimal configuration for hybrid SOFC,gas turbine,and Proton Exchange Membrane Electrolyzer using a developed Aquila Optimizer

In this research, a technical, economic and environmental analysis has been proposed to a Hybrid Solid Oxide Fuel Cell (SOFC) system-based hybrid system including biomass, gas turbine, and Proton Exchange Membrane Electrolyzer. A multi-objective optimization technique has been utilized to improve the overall product cost and the exergy effectiveness based on a developed version of Aquila Optimizer (DAO). The main idea of using the developed version is to improve the accuracy and the precision of the original Aquila optimizer. The system is then authenticated in terms of energy/exergy effectiveness, and energy-economic efficiency. The achievements indicate that employing the optimization algorithm for different configurations provided satisfying results for the system.     

Proton exchange membrane from the blend of poly(vinylidene fluoride) and functional copolymer: Preparation,proton conductivity,methanol permeability,and stability

A polymer electrolyte membrane is considered as the heart of fuel cells. Here we report the preparation of proton exchange membranes (PEMs) of poly (vinylidene fluoride) (PVDF) blend poly (methyl methacrylate)-co-poly (sodium-4-styrene sulfonate) (PMMA-co-PSSNa) by solvent evaporation method. Three different types of PEMs have been prepared by using different ratios of PVDF and PMMA-co-PSSNa copolymer. We have investigated the effect of concentration of PVDF on water uptake, ion exchange capacity, mechanical, thermal, and oxidative stability, proton conductivity (K^m), and methanol permeability (P_M) of the blend membranes. These blend PEMs showed good physicochemical and electrochemical properties along with thermal and oxidative stability. The membrane prepared from PVDF (45% w/w) to PMMA-co-PSSNa (55% w/w) exhibited optimum P_M at room temperature (8.38 × 10^?7 cm²s^?1). This low fuel crossover and high relative selectivity can make our prepared blend membranes a potential candidate in polymer electrolyte membrane fuel cells (PEMFCs) or direct methanol fuel cells (DMFCs).     

Study on ice-melting performance of gradient gas diffusion layer in proton exchange membrane fuel cell

In this study, a three-dimensional model was established using the lattice Boltzmann method (LBM) to study the internal ice melting process of the gas diffusion layer (GDL) of the proton exchange membrane fuel cell (PEMFC). The single-point second-order curved boundary condition was adopted. The effects of GDL carbon fiber number, growth slope of the number of carbon fibers and carbon fiber diameter on ice melting were studied. The results were revealed that the temperature in the middle and lower part of the gradient distribution GDL is significantly higher than that of the no-gradient GDL. With the increase of the growth slope of the number of carbon fiber, the temperature and melting rate gradually increase, and the position of the solid-liquid interface gradually decreases. The decrease in the number of carbon fibers has a similar effect as the increase in the growth slope of the number of carbon fibers. In addition, as the diameter of the carbon fiber increases, the position of the solid-liquid interface gradually decreases first and then increases.     

Influence of mesoporous phosphotungstic acid on the physicochemical properties and performance of sulfonated poly ether ether ketone in proton exchange membrane fuel cell

This study demonstrates the successful development of hybrid mesoporous siliceous phosphotungstic acid (mPTA-Si) and sulfonated poly ether ether ketone (SPEEK) as a proton exchange membrane with a high performance in hydrogen proton exchange membrane fuel cells (PEMFC). SPEEK acts as a polymeric membrane matrix and mPTA-Si acts as the mechanical reinforcer and proton conducting enhancer. Interestingly, incorporating mPTA-Si did not affect the morphological aspect of SPEEK as dense membrane upon loading the amount of mPTA-Si up to 2.5 wt%. The water uptake reduced to 14% from 21.5% when mPTA-Si content increases from 0.5 to 2.5 wt% respectively. Meanwhile, the proton conductivity increased to 0.01 Scm^?1 with 1.0 wt% mPTA-Si and maximum power density of 180.87 mWcm^?2 which is 200% improvement as compared to pristine SPEEK membrane. The systematic study of hybrid SP-mPTA-Si membrane proved a substantial enhancement in the performance together with further improvement on physicochemical properties of parent SPEEK membrane desirable for the PEMFC application.     

Numerical simulation of a PEM fuel cell: Effect of tortuosity parameters on the construction of polarization curves

Fuel cells (PEMFCs) are considered a clean alternative for the production of electricity. To improve their efficiency, it is necessary to understand the transport phenomena that determine their performance. This work analyzes the effective diffusion coefficients in the gas diffusion and catalyst layers (GDL and CL, respectively) based on theories such as effective medium approximation and percolation theory of a 3D multiphase nonisothermal model of a single channel PEMFC. We calculate polarization curves with different effective diffusion models and tortuosity factors m, n using OpenFOAM. The best model that approaches to experimental data is the Tomadakis-Sotirchos model with n = 4.0. The exponent m that represents the tortuosity due to the geometry has a high impact on the polarization curve construction compared to the exponent n. High values of diffusibility do not mean high values of current densities showing that there are other phenomena involved, such as water flooding.     

A novel design for humidifying an open-cathode proton exchange membrane fuel cell using anode purge

The low performance of open-cathode proton-exchange-membrane fuel cells (OCPEMFCs) is attributed to the low-humidity ambient air supplied to the cathode using electric fans. To improve the OCPEMFC performance, this paper proposes a novel humidification method by collecting water purged from the anode and supplying it to the open cathode. The OCPEMFC performance is evaluated at various humidifier distances from the cathode inlet, and it is compared with that where no humidifier is used when the OCPEMFC operates under three different current levels of 1, 5, and 8 A. The results show that the novel design improves the stack power, and optimal performance is achieved at a humidifier distance of 2 cm. The energy efficiency achieves an improvement between 1.4% and 1.8% when a humidifier is used.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

Synthesis,characterization and adsorption studies of h-BN crystal for efficient removal of Cd2+ from aqueous solution

In the present study, hexagonal boron nitride (h-BN) was synthesized from boric acid and melamine by thermal annealing method in a nitrogen atmosphere. The pure h-BN was used as an efficient sorbent for the uptake of Cd²⁺ ions from the solution phase. The kinetics and sorption studies of metal ions onto the h-BN were carried out in batch adsorption experiments at different temperature, time, pH, sorbent dosage, and concentration of metal ions. The optimum pH for the removal of the Cd²⁺ ions was found to be pH 7. The effect of temperature showed that the process of Cd²⁺ sorption remained endothermic in the range of 298 K–328 K. The Lagergren's first and Ho's second kinetic models were tested to interpret the adsorption kinetic data, however the present data was explained well by Ho's model for kinetics. The thermodynamic perameters ΔG, ΔS and ΔH were determined using the available adsorption data at different temperatures. The physicochemical properties of the synthesized product were also characterized before and after adsorption by different analytical techniques like FT-IR, TGA, XRD and Point of Zero Charge (PZC). The morphology of the surface was analyzed with the help of Scanning Electron Microscopy. The h-BN proved to be an efficient adsorbent for the uptake of the Cd²⁺ ions from aqueous media.