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Metal carbides have shown great promise in a wide range of applications due to their unique catalytic, electrocatalytic and magnetic properties. However, the scalable production of dispersible metal carbide nanoparticles remains a challenge. Here, we report a simple and scalable route to dispersible iron carbide (Fe3C) nanoparticles. This uses MgO nanoparticles as a removable ‘cast’ to synthesize Fe3C nanoparticles from Prussian blue (KFeIII[FeII(CN)6]). Electron tomography demonstrates how nanoparticles of the MgO cast encase the Fe3C nanoparticles to prevent sintering and agglomeration during the high-temperature synthesis. The MgO cast is readily removed with ethylenediaminetetraacetic acid (EDTA) to generate Fe3C nanoparticles that can be used to produce a colloidal ferrofluid or dispersed on a support material.
Structural modularity of polymer frameworks is a key advantage of covalent organic polymers, however, only C, N, O, Si and S have found their way into their building blocks so far. Here, we expand the toolbox available to polymer and materials chemists by one additional nonmetal, phosphorus. Starting with a building block that contains a λ⁵‐phosphinine (C₅P) moiety, we evaluate a number of polymerisation protocols, finally obtaining a π‐conjugated, covalent phosphinine‐based framework (CPF‐1) via Suzuki‐Miyaura coupling. CPF‐1 is a weakly porous polymer glass (72.4 m2 g‐1 N2 BET at 77 K) with green fluorescence (λmax 546 nm) and extremely high thermal stability. The polymer catalyzes hydrogen evolution from water under UV and visible light irradiation without the need for additional co‐catalyst at a rate of 33.3 μmol h‐¹ g‐¹. Our results demonstrate for the first time the incorporation of the phosphinine motif into a complex polymer framework. Phosphinine‐based frameworks show promising electronic and optical properties that might spark future interest in their applications in light‐emitting devices and heterogeneous catalysis.
A round-robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions and two from powders. Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5% and half of the population width entries within 40%. Due to the added complexity of the structure factor, far fewer people submitted answers on the powder datasets. For those that did, half of the entries for the means and widths were within 44 and 86%, respectively. This round-robin experiment highlights several causes for the discrepancies, for which solutions are proposed.