Neural network predictions of oxygen interactions on a dynamic Pd surface

<p>Artificial neural networks (NNs) are increasingly common in quantum chemistry applications. These models can be trained to higher-level <i>ab-initio</i> calculations and are capable of achieving arbitrary levels of accuracy. The most common applications thus far have been specialised for either bulk or surface structures of up to two chemical components. However, very few of these studies utilise NNs trained to high-dimensional potential energy surfaces, and there are even fewer studies which examine adsorbate–adsorbate and adsorbate–surface interactions with those NNs. The goal of this work is to determine the feasibility of and develop methodologies for producing a high-dimensional NN capable of reproducing coverage-dependent oxygen interactions with a dynamic Pd fcc(1 1 1) surface. We utilise the atomistic machine-learning potential software package to generate a Behler–Parrinello local symmetry function NN trained on a large database of density functional theory (DFT) calculations. These training methods are flexible, and thus easily expanded upon as demonstrated in previous work. This allows the database of high quality PdO DFT calculations to be used as a basis for future work, such as the inclusion of a third chemical species, for example a binary Pd alloy, or another adsorbate atom such as hydrogen.</p

Alkaline Electrolyte and Fe Impurity Effects on the Performance and Active-Phase Structure of NiOOH Thin Films for OER Catalysis Applications

The effects of varying alkaline electrolyte and electrolyte Fe levels on the performance and active-phase structure of NiOOH thin films for catalysis of the oxygen evolution reaction were studied. An electrolyte effect on catalytic performance was observed. Under purified conditions, current densities followed the trend Cs⁺ > K⁺ ≈ Na⁺ ≈ Li⁺ at current densities > 1 mA/cm². Under Fe-saturated conditions, current densities followed the trend K⁺ ≈ Na⁺ > Cs⁺ > Li⁺ at all current densities. Voltammetry was coupled with Raman spectroscopy for studies in LiOH and CsOH. Raman spectra were fit to Gaussian functions and analyzed quantitatively based on mean peak positions. Both purified and Fe-saturated CsOH promoted slightly lower peak positions than purified and Fe-saturated LiOH, indicating that CsOH promoted a NiOOH active-phase structure with longer Ni–O bonds. Both Fe-saturated CsOH and LiOH promoted slightly lower Raman peak positions than purified CsOH and LiOH, but only for one of the two Raman peaks. These results indicate that Fe promoted an active-phase structure with slightly longer Ni–O bonds. This study shows that the catalytic performance and active-phase structure of NiOOH can be tuned by simply varying the alkaline electrolyte and electrolyte Fe levels