RESUMEN
The strong ligand effect in B-doped Pd-based (PdB) catalysts renders them a promising anode for constructing formic acid fuel cells (FAFCs) exhibiting high power density and outstanding stability. However, the enhancement of the oxidation barrier is unavoidable in this alloy system owing to the electron transfer (ET) from B to Pd. In this study, a hydrogen doping strategy is employed to open charge freedom in PdB compounds and boost their formic acid oxidation reaction (FAOR) activity by suppressing the ET process. The resulting hydrogen-doped PdB (PdBH) exhibits an ultrahigh mass activity of up to 1.2A mg-1 Pd, which is 3.23 times that of the PdB catalyst and 9.55 times that of Pd black. Detailed experimental and theoretical studies show that the interstitial hydrogen leads to enhanced orbital hybridization and reduced electron density around Pd. This optimized ligand effect weakens the carbon monoxide adsorption and increases the direct pathway preference of PdBH, resulting in its outstanding catalytic activity for the FAOR. The development of this high-performance hydrogen-doped PdB catalyst is an important step toward the construction of advanced light element co-doped metal catalysts.
RESUMEN
Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to run-to-failure data sets of C-MAPSS test-bed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.
RESUMEN
Circular dichroism (CD) is useful in polarization conversion, negative refraction chemical analysis, and bio-sensing. To achieve strong CD signals, researchers constantly break the symmetry of nanostructures. However, how to further enhance the CD based on a new mechanism has become a new challenge in this field. In this work, a hybrid plasmonic chiral system composed of an array of graphene ribbons (GRs) over h-shaped sliver chiral nanostructures (HSCNs) is theoretically investigated. Results demonstrate that the plasmonic coupling between HSCNs and GRs results in different enhanced absorptions for different circularly polarized lights. The absorbance of right circularly polarized light is enhanced to perfect absorption; the absorption of left circularly polarized light is enhanced weakly. It leads to the CD effect of HSCNs@GRs approaching 88%. The loss distributions of HSCNs and HSCNs@GRs reveal that the absorption is enhanced and transferred from HSCNs to GRs. Moreover, the current distributions of HSCNs@GRs are simplified to equivalent LC resonant circuits, which can qualitatively explain the change of CD signals by tuning geometrical parameters of HSCNs@GRs. The findings of this work provide a new method of enhancing chirality and benefit the design of graphene-based chiral optoelectronic devices.
