Samir H. Al-Hilfi

As the alternatives to expensive Pt-based materials for the oxygen reduction reaction (ORR), iron/nitrogen co-doped carbon catalysts (FeNC) with dense FeN_x active sites are promising candidates to promote the commercialization of proton exchange membrane fuel cells. Herein, we report a synthetic approach using perfluorotetradecanoic acid (PFTA)-modified metal–organic frameworks as precursors for the synthesis of fluorine-doped FeNC (F-FeNC) with improved ORR performance. The utilization of PFTA surfactants causes profound changes of the catalyst structure including F-doping into graphitic carbon, increased micropore surface area and Brunauer–Emmett–Teller (BET) surface area (up to 1085 m² g⁻¹), as well as dense FeN_x sites. The F-FeNC catalyst exhibits an improved ORR activity with a high E_1/2 of 0.83 V (VS. RHE) compared to the pristine FeNC material (E_1/2 = 0.80 V). A fast decay occurs in the first 10 000 potential cycles for the F-FeNC catalyst, but high durability is still maintained up to another 50 000 cycles. Density functional theory calculations reveal that the strongly withdrawing fluorine atoms doped on the graphitic carbon can optimize the electronic structure of the FeN_x active center and decrease the adsorption energy of ORR intermediates.

Chemical vapour deposition (CVD) is the most promising graphene synthesis route for film and electronic applications but the growth mechanism is still not fully understood. Herein, we investigate the role of the solubility of carbon in the underlying growth substrate on the CVD growth of graphene. A range of Cu-Ni alloys compositions that cover the carbon (C) solubility range between low C solubility (pure Cu) and high C solubility (pure Ni) were used as the catalytic growth substrates. The CVD of graphene on Cu-Ni alloys showed a transition from bilayer graphene (BLG) to few-layer graphene (FLG) at a substrate Ni concentration of 45 wt.%, which was attributed to an increase in the bulk diffusion of C. The Cu-rich alloys had a high graphene coverage (BLG) at a fast-cooling rate (367 °C/min), while the Ni-rich alloys had a low coverage (FLG) under the same cooling condition. In contrast, at slow cooling rates (27 °C/min), the Cu-rich alloys had a low coverage of graphene (BLG) and the Ni-rich alloys had a high coverage of graphene (FLG). Glow discharge optical emission spectroscopy (GDOES) was used to profile the subsurface composition, particularly the C concentration, as a function of depth.

Chemical vapour deposition (CVD) of graphene on transition metals is generally believed to be the fabrication route best suited for the production of high-quality large-area graphene sheets. The mechanism of CVD graphene growth is governed by interactions in both the gas phase and at the surface. Here we present a simulation of the CVD graphene growth mechanism which includes thermodynamics, gas phase kinetics and the surface reaction in a sequential manner. The thermodynamic simulation shows that the deposition driving force is the greatest for high carbon to hydrogen ratios and reaches a maximum at around 850 °C. No graphene growth is observed below this temperature. The surface kinetic model also shows that below this temperature, the carbon surface concentration is less than the solubility limit, thus no film can grow. The effect of the reaction chamber geometry on the product concentrations was clear from the gas phase decomposition reactions. The gas residence times studied here (around 0.07 s) show that the optimum gas phase composition is far from that expected at thermodynamic equilibrium. The surface kinetics of CH₄ reactions on Ni, Cu and Cu–Ni surfaces shows good agreement with the experimental results for different growth pressures (0.1 to 0.7 mbar), temperatures (600 to 1200 °C) and different Ni thicknesses (25–500 μm). Also, the model works well when substrates with various C solubilities are used. The thermodynamic and kinetic models described here can be used for the design of improved reactors to optimise the production of graphene with differing qualities, either single or multi-layer and sizes. More importantly, the transfer to a continuous process with a moving substrate should also be possible using the model if it is extended from 2D to 3D.