Rationally designed Ru catalysts supported on TiN for highly efficient and stable hydrogen evolution in alkaline conditions.

Electrocatalysis holds the key to enhancing the efficiency and cost-effectiveness of water splitting devices, thereby contributing to the advancement of hydrogen as a clean, sustainable energy carrier. This study focuses on the rational design of Ru nanoparticle catalysts supported on TiN (Ru NPs/TiN) for the hydrogen evolution reaction in alkaline conditions. The as designed catalysts exhibit a high mass activity of 20 A mg-1Ru at an overpotential of 63 mV and long-term stability, surpassing the present benchmarks for commercial electrolyzers. Structural analysis highlights the effective modification of the Ru nanoparticle properties by the TiN substrate, while density functional theory calculations indicate strong adhesion of Ru particles to TiN substrates and advantageous modulation of hydrogen adsorption energies via particle-support interactions. Finally, we assemble an anion exchange membrane electrolyzer using the Ru NPs/TiN as the hydrogen evolution reaction catalyst, which operates at 5 A cm-2 for more than 1000 h with negligible degradation, exceeding the performance requirements for commercial electrolyzers. Our findings contribute to the design of efficient catalysts for water splitting by exploiting particle-support interactions.

Error Awareness in the Volcano Plots of Oxygen Electroreduction to Hydrogen Peroxide.

Electrocatalysis holds the key to the decentralized production of hydrogen peroxide via the two-electron oxygen reduction reaction (ORR, O2g+2H++e-→H2O2aq). However, cost-effective, active, and selective catalysts are still sought after. While density functional theory (DFT) has already led to the discovery of various enhanced catalysts, it has a severe yet often unnoticed drawback: the ill description of O2 and H2O2. Here, we analyze the impact of the errors in those two species on the most widespread activity plots in the literature, namely free-energy diagrams and Sabatier-type volcano plots. Uncorrected or partially corrected gas-phase energies lead to appreciably different activity plots that may provide inaccurate predictions. Indeed, we show for a variety of electrocatalysts that only when the errors in O2 and H2O2 are corrected can DFT mimic the experiments. In sum, this work provides concrete guidelines to avoid a common pitfall of computational models for electrocatalytic hydrogen peroxide production.

Minimum conditions for accurate modeling of urea production via co-electrolysis.

Co-electrolysis of carbon oxides and nitrogen oxides promise to simultaneously help restore the balance of the C and N cycles while producing valuable chemicals such as urea. However, co-electrolysis processes are still largely inefficient and numerous knowledge voids persist. Here, we provide a solid thermodynamic basis for modelling urea production via co-electrolysis. First, we determine the energetics of aqueous urea produced under electrochemical conditions based on experimental data, which enables an accurate assessment of equilibrium potentials and overpotentials. Next, we use density functional theory (DFT) calculations to model various co-electrolysis reactions producing urea. The calculated reaction free energies deviate significantly from experimental values for well-known GGA, meta-GGA and hybrid functionals. These deviations stem from errors in the DFT-calculated energies of molecular reactants and products. In particular, the error for urea is approximately -0.25 ± 0.10 eV. Finally, we show that all these errors introduce large inconsistencies in the calculated free-energy diagrams of urea production via co-electrolysis, such that gas-phase corrections are strongly advised.

Automated versus Chemically Intuitive Deconvolution of Density Functional Theory (DFT)-Based Gas-Phase Errors in Nitrogen Compounds.

Catalysis models involving metal surfaces and gases are regularly based on density functional theory (DFT) calculations at the generalized gradient approximation (GGA). Such models may have large errors in view of the poor DFT-GGA description of gas-phase molecules with multiple bonds. Here, we analyze three correction schemes for the PBE-calculated Gibbs energies of formation of 13 nitrogen compounds. The first scheme is sequential and based on chemical intuition, the second one is an automated optimization based on chemical bonds, and the third one is an automated optimization that capitalizes on the errors found by the first scheme. The mean and maximum absolute errors are brought down close to chemical accuracy by the third approach by correcting the inaccuracies in the NNO and ONO backbones and those in N-O and N-N bonds. This work shows that chemical intuition and automated optimization can be combined to swiftly enhance the predictiveness of DFT-GGA calculations of gases.