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This paper reports a systematic study into the effect of nitrogen on iron-catalyzed graphitization of biomass. Chitin, chitosan, N-acetylglucosamine, gelatin and glycine were selected to represent nitrogen-rich saccharides and amino-acid/polypeptide biomass precursors. The materials were pyrolyzed with an iron catalyst to produce carbons with a wide range of chemical and structural features such as mesoporosity and nitrogen-doping. Many authors have reported the synthesis of nitrogen-doped carbons by pyrolysis and these have diverse applications. However, this is the first systematic study of how nitrogen affects pyrolysis of biomass and importantly the catalytic graphitization step. Our data demonstrates that nitrogen inhibits graphitization but that some nitrogen survives the catalytic graphitization process to become incorporated into various chemical environments in the carbon product.
A versatile software package in the form of a Python extension, named CDEF (computing Debye’s scattering formula for extraordinary form factors), is proposed to calculate approximate scattering profiles of arbitrarily shaped nanoparticles for small-angle X-ray scattering (SAXS). CDEF generates a quasi-randomly distributed point cloud in the desired particle shape and then applies the open-source software DEBYER for efficient evaluation of Debye’s scattering formula to calculate the SAXS pattern (https://github.com/j-from-b/CDEF). If self-correlation of the scattering signal is not omitted, the quasi-random distribution provides faster convergence compared with a true-random distribution of the scatterers, especially at higher momentum transfer. The usage of the software is demonstrated for the evaluation of scattering data of Au nanocubes with rounded edges, which were measured at the four-crystal monochromator beamline of PTB at the synchrotron radiation facility BESSY II in Berlin. The implementation is fast enough to run on a single desktop computer and perform model fits within minutes. The accuracy of the method was analyzed by comparison with analytically known form factors and verified with another implementation, the SPONGE, based on a similar principle with fewer approximations. Additionally, the SPONGE coupled to McSAS3 allows one to retrieve information on the uncertainty of the size distribution using a Monte Carlo uncertainty estimation algorithm.
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.
A user-friendly open-source Monte Carlo regression package (McSAS) is presented, which structures the analysis of small-angle scattering (SAS) using uncorrelated shape-similar particles (or scattering contributions). The underdetermined problem is solvable, provided that sufficient external information is available. Based on this, the user picks a scatterer contribution model (or 'shape') from a comprehensive library and defines variation intervals of its model parameters. A multitude of scattering contribution models are included, including prolate and oblate nanoparticles, core-shell objects, several polymer models, and a model for densely packed spheres. Most importantly, the form-free Monte Carlo nature of McSAS means it is not necessary to provide further restrictions on the mathematical form of the parameter distribution; without prior knowledge, McSAS is able to extract complex multimodal or odd-shaped parameter distributions from SAS data. When provided with data on an absolute scale with reasonable uncertainty estimates, the software outputs model parameter distributions in absolute volume fraction, and provides the modes of the distribution (e.g. mean, variance etc.). In addition to facilitating the evaluation of (series of) SAS curves, McSAS also helps in assessing the significance of the results through the addition of uncertainty estimates to the result. The McSAS software can be integrated as part of an automated reduction and analysis procedure in laboratory instruments or at synchrotron beamlines.
Iron nitride (Fe3N) and iron carbide (Fe3C) nanoparticles can be prepared via sol−gel synthesis. While sol−gel methods are simple, it can be difficult to control the crystalline composition, i.e., to achieve a Rietveld-pure product. In a previous in situ synchrotron study of the sol−gel synthesis of Fe3N/Fe3C, we showed that the reaction proceeds as follows:
Fe3O4 → FeOx → Fe3N → Fe3C. There was considerable overlap between the different phases, but we were unable to ascertain whether this was due to the experimental setup (side-on heating of a quartz capillary which could lead to thermal gradients) or whether individual particle reactions proceed at different rates. In this paper, we use in situ wide- and small-angle X-ray scattering (wide-angle X-ray scattering (WAXS) and small-angle X-ray scattering (SAXS)) to demonstrate that the overlapping phases are indeed due to variable reaction rates. While the initial oxide nanoparticles have a small range of diameters, the size range expands considerably and very rapidly during the oxide−nitride transition. This has implications for the isolation of Rietveld-pure Fe3N, and in an extensive laboratory study, we were indeed unable to isolate phasepure Fe3N. However, we made the surprising discovery that Rietveld-pure Fe3C nanoparticles can be produced at 500 °C with a sufficient furnace dwell time. This is considerably lower than the previous reports of the sol−gel synthesis of Fe3C nanoparticles.