Abstract
The safe widespread application of hydrogen-based fuels requires sensors that are long-term stable, inexpensive, hydrogen-specific, and have a short response time. In this regard, optical sensing based on nanostructured Pd alloys has shown great potential, but challenges remain, including understanding and controlling the surface composition under operation conditions. While the latter is crucial for long-term functionality and stability, it is experimentally very challenging to obtain accurate atomic-scale information. Here, we therefore scrutinize the behavior of two particularly relevant surface alloys, {111} AuPd and {111} CuPd, in H environments ranging from vacuum to fully covered surfaces. To this end, we employ a combination of alloy cluster expansions trained using density functional theory calculations, Monte Carlo simulations, and thermodynamic analysis to obtain the H coverage as well as the layer-by-layer composition of the near-surface region as a function of H partial pressure, temperature, and bulk composition. To overcome the symmetry reduction implicit to surface systems, we exploit local symmetries, which enables us to achieve accurate and reliable models at a low computational cost. In the case of AuPd, Au segregates to the surface in vacuum while Pd segregates to the surface at 100% H coverage, and the transition between these regimes occurs over a narrow H pressure interval. In the case of CuPd, on the other hand, the H coverage varies much more gradually with H pressure and is coupled to a nonmonotonic variation of the Cu concentration in the topmost surface layer. While there is a pronounced Cu depletion both at 0 and 100% H coverage, Cu enrichment is observed at 50% coverage at Cu bulk concentrations up to at least 10%, providing a nontrivial explanation for an apparent discrepancy between experiment and calculations that was observed previously. At the same time, layer 2 continuously shifts from Cu enrichment to Cu depletion with increasing H coverage. The results demonstrate the rich behavior that can result from the coupling of metal–metal and metal–hydrogen interactions at surfaces, even in apparently simple but concentrated systems. Moreover, they underline the advantages of simulations that account for temperature and pressure effects as well as models that can accurately capture the interactions over a wide composition range.
Highlights
Given the urgent need to replace fossil fuels in the transport sector, hydrogen has emerged as a candidate fuel
We have found that this simple approach results in models that are less prone to overfitting, produce accurate segregation energy predictions, and maintain low cross-validated (CV)root mean square errors (RMSEs) (Figure S9)
We constructed cluster expansion (CE) based on density functional theory (DFT) calculations that we subsequently sampled by Monte Carlo (MC) simulations
Summary
Given the urgent need to replace fossil fuels in the transport sector, hydrogen has emerged as a candidate fuel. There are, a number of issues related to production,[1,5] storage,[1,6] and safety[7] that need to be solved before hydrogen fuel cell vehicles can be deployed at a large scale. The present work is related to the safety aspect and the need for reliable hydrogen sensors to enable safe handling of potential hydrogen leaks. There is, a need for further optimization of such systems.[12] Pure Pd sensors suffer from hysteresis between the H absorption and desorption half-cycles[13] and carbon monoxide (CO) poisoning.[14] These effects directly limit the reliability and stability of LSPR-based H sensors. A promising solution exists, namely, alloying Pd with other metals: The observed hysteresis is caused by the first-order phase transition between Pd and Pd:H, which can be suppressed by alloying with gold
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