HIGHLIGHTS• 3D mesoporous Ni 3 FeN was constructed through hard templating and thermal nitridation.• Ni 3 FeN exhibits superior electrochemical performance for OER with a small overpotential of 259 mV to achieve a 10 mA cm −2 .• Ni 3 FeN can also deliver a lower charging voltage and longer lifetime than RuO 2 in a rechargeable Zn-air battery.ABSTRACT As sustainable energy becomes a major concern for modern society, renewable and clean energy systems need highly active, stable, and low-cost catalysts for the oxygen evolution reaction (OER). Mesoporous materials offer an attractive route for generating efficient electrocatalysts with high mass transport capabilities. Herein, we report an efficient hard templating pathway to design and synthesize three-dimensional (3-D) mesoporous ternary nickel iron nitride (Ni 3 FeN). The as-synthesized electrocatalyst shows good OER performance in an alkaline solution with low overpotential (259 mV) and a small Tafel slope (54 mV dec −1 ), giving superior performance to IrO 2 and RuO 2 catalysts. The highly active contact area, the hierarchical porosity, and the synergistic effect of bimetal atoms contributed to the improved electrocatalytic performance toward OER.In a practical rechargeable Zn-air battery, mesoporous Ni 3 FeN is also shown to deliver a lower charging voltage and longer lifetime than RuO 2 . This work opens up a new promising approach to synthesize active OER electrocatalysts for energy-related devices.
Reverse micelle deposition of iron oxide nanoparticles results in monodisperse arrays of single crystalline nanoparticles with pure γ-Fe2O3 or pure α-Fe2O3 under optimized conditions, which can be effectively tracked from precursor incorporation through final particle formation using Raman spectroscopy.
Hybrid
organic–inorganic halide perovskites have emerged
as a disruptive technology in a number of fields, and recently, there
has been increased interest in developing nanostructured perovskite
materials, due to their extremely high photoluminescence quantum yields,
optical absorption, and tolerance for defects. In this study, we report
on the development of a facile room temperature synthesis method for
high density monodispersed metal–organic halide perovskite
nanoparticles using a diblock copolymer reverse micelle deposition
(RMD) method. Compared to traditional ligated methods, we show that
diblock copolymer micelle templating allows greater control over the
size distribution due to controlled nucleation and crystal growth.
By separating the precursor solvation and reaction steps through micelle
templating, we show that micelle templating is a universal, atmospheric
approach to producing a variety of perovskite nanoparticles, including
methylammonium lead iodide (MAPbI3), methylammonium lead
bromide (MAPbBr3), and formamidinium lead iodide (FAPbI3) at room temperature. Additionally, using micellar nanoreactors
rather than dynamically stabilizing ligands allows the formation of
monodisperse spherical 0D nanoparticles rather than nanoplatelets
or nanorods, as is common with most approaches. Knowledge of the synthesis
behavior of a facile versatile approach for monodisperse nanoparticles
with narrow band emission will open up new avenues for the development
of nanoparticle based applications as integral parts of next-generation
displays and optoelectronic devices.
Order classification is particularly important in photonics, optoelectronics, nanotechnology, biology, and biomedicine, as self-assembled and living systems tend to be ordered well but not perfectly. Engineering sets of experimental protocols that can accurately reproduce specific desired patterns can be a challenge when (dis)ordered outcomes look visually similar. Robust comparisons between similar samples, especially with limited data sets, need a finely tuned ensemble of accurate analysis tools. Here we introduce our numerical Mathematica package disLocate, a suite of tools to rapidly quantify the spatial structure of a two-dimensional dispersion of objects. The full range of tools available in disLocate give different insights into the quality and type of order present in a given dispersion, accessing the translational, orientational and entropic order. The utility of this package allows for researchers to extract the variation and confidence range within finite sets of data (single images) using different structure metrics to quantify local variation in disorder. Containing all metrics within one package allows for researchers to easily and rapidly extract many different parameters simultaneously, allowing robust conclusions to be drawn on the order of a given system. Quantifying the experimental trends which produce desired morphologies enables engineering of novel methods to direct self-assembly.
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