A radical route from methane to methanol: The conversion of methane into chemicals usually proceeds through high-temperature routes that first form more reactive carbon monoxide and hydrogen. Agarwal et al. report a low-temperature (50°C) route in aqueous hydrogen peroxide (H ₂ O ₂ ) for oxidizing methane to methanol in high yield (92%). They used colloidal gold-palladium nanoparticles as a catalyst. The primary oxidant was O ₂ ; isotopic labeling showed that H ₂ O ₂ activated methane to methyl radicals, which subsequently incorporated O ₂ . Science , this issue p. 223

Direct hydrogen peroxide synthesis: Hydrogen peroxide is synthesized industrially without direct contact of hydrogen and oxygen in order to achieve high concentrations. For many applications, only dilute aqueous solutions are needed. Freakley et al. report an improvement in the direct synthesis of hydrogen peroxide over using palladium-tin alloys. This catalyst still achieves selectivities of >95%, like palladium-gold alloys, but is cheaper and can suppress reactions that decompose the product. Science , this issue p. 965

In this review, we discuss selected examples from recent literature on the role of the support on directing the nanostructures of Au-based monometallic and bimetallic nanoparticles. The role of support is then discussed in relation to the catalytic properties of Au-based monometallic and bimetallic nanoparticles using different gas phase and liquid phase reactions. The reactions discussed include CO oxidation, aerobic oxidation of monohydric and polyhydric alcohols, selective hydrogenation of alkynes, hydrogenation of nitroaromatics, CO₂ hydrogenation, C–C coupling, and methane oxidation. Only studies where the role of support has been explicitly studied in detail have been selected for discussion. However, the role of support is also examined using examples of reactions involving unsupported metal nanoparticles (i.e., colloidal nanoparticles). It is clear that the support functionality can play a crucial role in tuning the catalytic activity that is observed and that advanced theory and characterization add greatly to our understanding of these fascinating catalysts.

The electrochemical nitrate reduction reaction (NO₃RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO₃ is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO₃RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 V_RHE and a yield rate of 6.25 mol h⁻¹ g⁻¹ at −0.6 V_RHE. This study provides machine-learned design rules besides the d-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.