With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, WiFi fingerprinting has become one of the most practical approaches to localize mobile users. However, most fingerprint-based localization algorithms are computation-intensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization system with WiFi fingerprints for multi-building and multi-floor localization. Specifically, we devise a novel classification model and a novel positioning model by combining a Stacked Auto-Encoder (SAE) with a one-dimensional CNN. The SAE is utilized to precisely extract key features from sparse Received Signal Strength (RSS) data while the CNN is trained to effectively achieve high accuracy in the positioning phase. We evaluate the proposed system on the UJIIndoorLoc dataset and Tampere dataset and compare the performance with several state-of-the-art methods. Moreover, we further propose a newly collected WiFi fingerprinting dataset UTSIndoorLoc and test the positioning model of CNNLoc on it. The results show CNNLoc outperforms the existing solutions with 100% and 95% success rates on building-level localization and floor-level localization, respectively.

Efficient and low-cost electrocatalysts for oxygen evolution reaction (OER), particularly in neutral conditions, are of significant importance for renewable energy technologies such as CO2 reduction and seawater splitting electrolysis. High-valent transition-metal sites have been considered as OER active sites; however, the rational design and construction of these sites remain a big challenge. Here, we report a trimetallic NiFeCu oxyhydroxide electrocatalyst, in which high-valent Ni sites are promoted and stabilized by the atomically embedded Cu, as evidenced by X-ray photoelectron spectroscopy and X-ray absorption spectroscopy. Through compositional optimization, Ni6Fe1Cu1 catalysts achieved an overpotential of 385 mV at 10 mA cm–2, a Tafel slope of 164 mV dec–1, and a stability of 100 h at pH = 7.2. Density function theory calculations demonstrated that the Cu-doping facilitates the formation of high-valent Ni and thus promotes OER electrocatalysis through modulating the d-band center of Ni and reducing the adsorption energy of oxygenated intermediates on the surface of the catalyst. This work paves a promising avenue for the construction of desired high-valent metal OER catalysts by embedding redox inactive metals.

Recent advances in the thermodynamic description of reactions and phase transformations at interfaces between metals, semiconductors, oxides and the ambient have been reviewed. Unanticipated nanostructures, characterized by the presence of phases at interfaces and surfaces which are unstable as bulk phases, can be thermodynamically stabilized due to the dominance of energy contributions of interfaces and surfaces in the total Gibbs energy of the system. The basic principles and practical guidelines to construct realistic, practically and generally applicable thermodynamic model descriptions of microstructural evolutions at interfaces and surfaces have been outlined. To this end, expressions for the estimation of the involved interface and surface energies have been dealt with extensively as a function of, e. g., the film composition and the growth temperature. Model predictions on transformations at interfaces (surfaces) in nanosized systems have been compared with corresponding experimental observations for, in particular, ultrathin (< 5 nm) oxide overgrowths on metal surfaces, as well as the metal-induced crystallization of semi-conductors in contact with various metals.

Convective transportation of materials in the solid state occurring in a prototype solid bilayer system of Al and Si with negligible mutual solubility has been directly imaged in real time at nanoscale using a valence energy-filtered transmission electron microscope. Such solid-state convection is driven by the stress gradient developing in the bilayer system due to the amorphous to crystalline phase transformation of the Si sublayer. The process is characterized by compression experienced in the Si phase crystallizing within the Al sublayer, as well as by the development of mushroom-shaped "plumes" of Al nanocrystals in the Si sublayer as a result of compressive stress relaxation and discrete, new nucleation of crystalline Al. The real-time, atomistic observation and the thus-obtained fundamental understanding of solid-state convection enable highly sophisticated applications of such a complex process in advanced fabrication and processing of nanomaterials and solid-state devices.

Precisely carving of multi‐shelled manganese–cobalt oxide hollow dodecahedra (Co/Mn‐HD) with shell number up to three is achieved by a controlled calcination of the Mn‐doped zeolitic imidazolate framework ZIF‐67 precursor (Co/Mn‐ZIF). The unique multi‐shelled and polycrystalline structure not only provides a very large electrochemically active surface area (EASA), but also enhances the structural stability of the material. The residual C and N in the final structures might aid stability and increase their conductivity. When used in alkaline rechargeable battery, the triple‐shelled Co/Mn‐HD exhibits high electrochemical performance, reversible capacity (331.94 mAh g−1 at 1 Ag−1), rate performance (88 % of the capacity can be retained with a 20‐fold increase in current density), and cycling stability (96 % retention over 2000 cycles).