3D porous nanostructures built from 2D δ-MnO2 nanosheets are an environmentally friendly and industrially scalable class of supercapacitor electrode material. While both the electrochemistry and defects of this material have been studied, the role of defects in improving the energy storage density of these materials has not been addressed. In this work, δ-MnO2 nanosheet assemblies with 150 m2 g−1 specific surface area are prepared by exfoliation of crystalline KxMnO2 and subsequent reassembly. Equilibration at different pH introduces intentional Mn vacancies into the nanosheets, increasing pseudocapacitance to over 300 F g−1, reducing charge transfer resistance as low as 3 Ω, and providing a 50% improvement in cycling stability. X-ray absorption spectroscopy and high-energy X-ray scattering demonstrate a correlation between the defect content and the improved electrochemical performance. The results show that Mn vacancies provide ion intercalation sites which concurrently improve specific capacitance, charge transfer resistance and cycling stability.

The incorporation of atomic scale defects, such as cation vacancies, in electrode materials is considered an effective strategy to improve their electrochemical energy storage performance. In fact, cation vacancies can effectively modulate the electronic properties of host materials, thus promoting charge transfer and redox reaction kinetics. Such defects can also serve as extra host sites for inserted proton or alkali cations, facilitating the ion diffusion upon electrochemical cycling. Altogether, these features may contribute to improved electrochemical performance. In this review, the latest progress in cation vacancies‐based electrochemical energy storage materials, covering the synthetic approaches to incorporate cation vacancies and the advanced techniques to characterize such vacancies and identify their fundamental role, are provided from the chemical and materials point of view. The key challenges and future opportunities for cation vacancies‐based electrochemical energy storage materials are also discussed, particularly focusing on cation‐deficient transition metal oxides (TMOs), but also including newly emerging materials such as transition metal carbides (MXenes).

No abstract

Roughness of a surface as characterized by an atomic force microscope (AFM) is typically expressed using conventional statistical measurements including root-mean-square, peak-to-valley ratio, and average roughness. However, in these measurements only the vertical distribution of roughness (z-axis) is considered. Additionally, roughness of a surface as determined by AFM is a function of the scanning scale, sampling interval and/ or scanning methods; therefore, the consideration and quantification of the lateral distribution (x and y) is necessary. Power spectral density (PSD) analysis provides both lateral and vertical signals captured from AFM images. By applying one of the commonly adopted models to the PSD data, the fractal model and k-correlation model, the equivalent root mean squared roughness, correlation length, fractal dimension and Hurst exponent are quantified. These parameters describe the spatial distribution of roughness and spatial length scale of the roughness values. Longer correlation length is preferred for the comprehensive measurement of roughness of surface at a given spatial wavelength. However, a method to enhance correlation length has yet to be discussed. In this paper, we discuss the state-of-the-art issues associated with roughness evaluation from AFM analysis and propose that the spatial correlation length can be enhanced through the combination PSD profiles over a wide range of spatial frequencies.

No abstract