Process and challenges of stainless steel based bipolar plates for proton exchange membrane fuel cells

Proton exchange membrane fuel cell(PEMFC)powered automobiles have been recognized to be the ultimate solution to replace traditional fuel automobiles because of their advantages of PEMFCs such as no pollution,low temperature start-up,high energy density,and low noise.As one of the core components,the bipolar plates(BPs)play an important role in the PEMFC stack.Traditional graphite BPs and composite BPs have been criticized for their shortcomings such as low strength,high brittleness,and high processing cost.In contrast,stainless steel BPs(SSBPs)have recently attracted much attention of domestic and foreign researchers because of their excellent comprehensive performance,low cost,and diverse options for automobile applications.However,the SSBPs are prone to corrosion and passivation in the PEMFC working environment,which lead to reduced output power or premature failure.This review is aimed to summarize the corrosion and passivation mechanisms,characterizations and evaluation,and the surface modification technologies in the current SSBPs research.The non-coating and coating technical routes of SSBPs are demonstrated,such as substrate component regulation,thermal nitriding,electroplating,ion plating,chemical vapor deposition,and physical vapor deposition,etc.Alternative coating materials for SSBPs are metal coatings,metal nitride coatings,conductive polymer coatings,and polymer/carbon coatings,etc.Both the surface modification technologies can solve the corrosion resistance problem of stainless steel without affecting the contact resistance,however still facing restraints such as long-time stability,feasibility of low-cost,and mass production process.This paper is believed to enrich the knowledge of high-performance and long-life BPs applied for PEMFC automobiles.

Hydrogen as a carrier of renewable energies toward carbon neutrality:State-of-the-art and challenging issues

Energy storage and conversion via a hydrogen chain is a recognized vision of future energy systems based on renewables and,therefore,a key to bridging the technological gap toward a net-zero CO_(2)emission society.This paper reviews the hydrogen technological chain in the framework of renewables,including water electrolysis,hydrogen storage,and fuel cell technologies.Water electrolysis is an energy conversion technology that can be scalable in megawatts and operational in a dynamic mode to match the intermittent generation of renewable power.Material concerns include a robust diaphragm for alkaline cells,catalysts and construction materials for proton exchange membrane(PEM)cells,and validation of the long-term durability for solid oxide cells.Hydrogen storage via compressed gas up to 70 MPa is optional for automobile applications.Fuel cells favor hydrogen fuel because of its superfast electrode kinetics.PEM fuel cells and solid oxide fuel cells are dominating technologies for automobile and stationary applications,respectively.Both technologies are at the threshold of their commercial markets with verified technical readiness and environmental merits;however,they still face restraints such as unavailable hydrogen fueling infrastructure,long-term durability,and costs to compete with the analog power technologies already on the market.

Safeguarding cross-silo federated learning with local differential privacy

Federated Learning(FL)is a new computing paradigm in privacy-preserving Machine Learning(ML),where the ML model is trained in a decentralized manner by the clients,preventing the server from directly accessing privacy-sensitive data from the clients.Unfortunately,recent advances have shown potential risks for user-level privacy breaches under the cross-silo FL framework.In this paper,we propose addressing the issue by using a three-plane framework to secure the cross-silo FL,taking advantage of the Local Differential Privacy(LDP)mechanism.The key insight here is that LDP can provide strong data privacy protection while still retaining user data statistics to preserve its high utility.Experimental results on three real-world datasets demonstrate the effectiveness of our framework.