Integration of multi-physics and machine learning-based surrogate modelling approaches for multi-objective optimization of deformed GDL of PEM fuel cells

The development of artificial intelligence(AI)greatly boosts scientific and engineering innovation.As one of the promising candidates for transiting the carbon intensive economy to zero emission future,proton exchange membrane(PEM)fuel cells has aroused extensive attentions.The gas diffusion layer(GDL)strongly affects the water and heat management during PEM fuel cells operation,therefore multi-variable optimization,including thickness,porosity,conductivity,channel/rib widths and compression ratio,is essential for the improved cell performance.However,traditional experiment-based optimization is time consuming and economically unaffordable.To break down the obstacles to rapidly optimize GDLs,physics-based simulation and machine-learning-based surrogate modelling are integrated to build a sophisticated M 5 model,in which multi-physics and multi-phase flow simulation,machine-learning-based surrogate modelling,multi-variable and multi-objects optimization are included.Two machine learning methodologies,namely response surface methodol-ogy(RSM)and artificial neural network(ANN)are compared.The M 5 model is proved to be effective and efficient for GDL optimization.After optimization,the current density and standard deviation of oxygen dis-tribution at 0.4 V are improved by 20.8%and 74.6%,respectively.Pareto front is obtained to trade off the cell performance and homogeneity of oxygen distribution,e.g.,20.5%higher current density is achieved when sacrificing the standard deviation of oxygen distribution by 26.0%.

Dendritic cells maturation facilitated by group-adjustable lipopolysaccharide analogues synthesized via RAFT polymerization

Transforming immature DCs into mature state to activate cellular immunity is a critical step in initiating immunoprophylaxis and immunotherapy.Lipopolysaccharides(LPS)can promote DCs maturation by binding receptor on DCs surface,but their clinical application is limited due to biological toxicity.Although many LPS analogues have been developed,complex synthesis and purification hinder their practical application.Here,we propose a novel and simple strategy to synthesize LPS analogues with adjustable structural units.Using monomer units similar to the key functional groups of LPS,we synthesize LPS analogues with different group ratios by RAFT polymerization.The obtained analogues have little negative effect on cell viability.Compared with LPS,the analogues show greater promoting effect on DCs maturation.And the analogues can be applied to different scenarios since the degrees of promoting DCs maturation by LPS analogues with different group ratios are different.This strategy provides a new direction for synthesizing LPS analogues,and it has the potential to produce LPS analogues on a large scale with tunable promoting DCs maturation effect.