Metastable face-centered cubic ruthenium-based binary alloy for efficient alkaline hydrogen oxidation electrocatalysis

Metastable nanostructured electrocatalyst with a completely different surface environment compared to conventional phase-based electrocatalyst often shows distinctive catalytic property.Although Ru-based electrocatalysts have been widely investigated toward hydrogen oxidation reaction(HOR)under alkaline electrolytes,these studies are mostly limited to conventional hexagonal-close-packed(hcp)phase,mainly arising from the lack of sufficient synthesis strategies.In this study,we report the precise synthesis of metastable binary RuW alloy with face-centered-cubic(fcc)phase.We find that the introduction of W can serve as fcc phase seeds and reduce the formation energy of metastable fcc-RuW alloy.Impressively,fcc-RuW exhibits remarkable alkaline HOR performance and stability with the activity of 0.67 mA cm_(Ru)^(-2)which is almost five and three times higher than that of hcp-Ru and commercial Pt/C,respectively,which is attributed to the optimized binding strength of adsorbed hydroxide intermediate derived from tailored electronic structure through W doping and phase engineering.Moreover,this strategy can also be applied to synthesize other metastable fcc-RuCr and fcc-RuMo alloys with enhanced HOR performances.

基于水动力载荷混合数据集的高精度神经网络代理模型构建

In this work,we constructed a neural network proxy model(NNPM)to estimate the hydrodynamic resistance in the ship hull structure design process,which is based on the hydrodynamic load data obtained from both the potential flow method(PFM)and the viscous flow method(VFM).Here the PFM dataset is applied for the tuning,pre-training,and the VFM dataset is applied for the fine-training.By adopting the PFM and VFM datasets simultaneously,we aim to construct an NNPM to achieve the high-accuracy prediction on hydrodynamic load on ship hull structures exerted from the viscous flow,while ensuring a moderate data-acquiring workload.The high accuracy prediction on hydrodynamic loads and the relatively low dataset establishment cost of the NNPM developed demonstrated the effectiveness and feasibility of hybrid dataset based NNPM achieving a high precision prediction of hydrodynamic loads on ship hull structures.The successful construction of the high precision hydrodynamic prediction NNPM advances the artificial intelligence-assisted design(AIAD)technology for various marine structures.

An artificial intelligence-aided design (AIAD) of ship hull structures

Ship-hull design is a complex process because the any slight local alteration in ship hull structure may significantly change the hydrostatic and hydrodynamic performances of a ship.To find the optimum hull shape under the design requirements,the state-of-art of ship hull design combines computational fluid dynamics computation with geometric modeling.However,this process is very computationally intensive,which is only suitable at the final stage of the design process.To narrow down the design parameter space,in this work,we have developed an AI-based deep learning neural network to realize a real-time prediction of the total resistance of the ship-hull structure in its initial design process.In this work,we have demonstrated how to use the developed DNN model to carry out the initial ship hull design.The validation results showed that the deep learning model could accurately predict the ship hull’s total resistance accurately after being trained,where the average error of all samples in the testing dataset is lower than 4%.Simultaneously,the trained deep learning model can predict the hip’s performances in real-time by inputting geometric modification parameters without tedious preprocessing and calculation processes.The machine learning approach in ship hull design proposed in this work is the first step towards the artificial intelligence-aided design in naval architectures.

The Role of Hydroxide Binding Energy in Alkaline Hydrogen Oxidation Reaction Kinetics on RuCr Nanosheet

Unveiling the role of adsorbed hydroxide involved in the hydrogen oxidation reaction(HOR)under alkaline electrolyte is crucial for the development of advanced HOR electrocatalysts for the alkaline polymer electrolyte fuel cells(APEFCs).Herein,we report the synthesis of amorphous RuCr nanosheets with different molar ratios and their HOR performances under alkaline media.We find a volcano correlation between the Cr content in RuCr nanosheets and their alkaline HOR performance.Experimental results and density functional theory(DFT)calculation reveals that the optimized Cr content in RuCr nanosheets could lead to the optimum hydroxide binding energy(OHBE),contributes to their remarkable alkaline HOR performance with mass activity of 568.1 A·gPGM^(–1) at 50 mV,13-fold higher than that of Ru catalyst.When RuCr nanosheet is further used as the anodic electrocatalyst,a peak power density of 1.04 W·cm^(–2 )can be achieved in an APEFC.

IrWOx上吸附的OH^*物种对碱性氢电催化的不同促进作用

碱性氢电极反应过程包括氢氧化反应(HOR)和氢析出反应(HER),改善这两个反应复杂的动力学过程对于实现碱性交换膜燃料电池(AEMFCs)和电解水(AEMWEs)的商业化是非常重要的.但是,它们的反应机理仍旧存在很大的争议性.据此,本文设计合成了一种由无定形的氧化钨团簇修饰的铱钨纳米晶(Ir WOx)材料,在电化学催化碱性HOR和HER的过程中, Ir WOx表现出明显高于商业铂碳催化剂(Pt/C)三倍的性能,具有超高的交换电流密度和质量活性.密度泛函理论(DFT)计算表明,铱钨纳米晶附近的氧化钨团簇对于促进碱性条件下的可逆氢电极反应起着关键性作用,且表现出不同的反应机理,包括HOR的氢结合能(HBE)机理和HER的双功能机理.这项研究工作有望促进研究者们加深对碱性HOR和HER催化机理的认识,为合理设计高效催化碱性HOR和HER的电催化剂提供了新途径.