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Size-tunable crystalline core-crystalline shell Zn2GeO4 nanorods were synthesized via a facile hydrothermal reaction. High purity Zn2GeO4 nanorods were obtained at pH = 7. The length of Zn2GeO4 nanorods (L = 50-100 nm) can be controlled through a one-step process, while micro-sized nanorods with an aspect ratio of the length to the diameter of 10 were yielded in a two-step process. The single crystalline nature of Zn2GeO4 nanorods with a core-shell structure was verified by high resolution transmission electron microscopy (HRTEM) and selected area electron diffraction (SAED) measurements. The Raman study revealed that there is no oxygen defect in Zn2GeO4 nanocrystals, suggesting that photoluminescence emission of Zn2GeO4 can be attributed to the presence of the interstitial Zn defect in Zn2GeO4 nanocrystals. As the diameter of nanorods decreased, the excitation and emission peaks appeared to be redshifted due to the quantum size effect.
The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid-solid TENG and liquid-solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed.
Abstract Ordered assembly of hollow structures of silica and carbon have drawn much attention recently because of their applications in low-loss dielectrics, catalysis, filtering and photonics. The structure is ordered on the length-scale of the template spheres and the pore sizes are in submicron to micron range. Alternatively, ordered porous silica with much smaller pore sizes in nanosize range (< 30 nm), produced deliberately by introducing surfactant, has also been processed, in which the porosity is created by co-polymers. In this research, a new silica nanostructure with double length-scale ordered porosities was processed, one being at the submicron scale of hollow spheres created by a template of polystyrene (PS), and the other at nano-scale created by self-assembled co-polymers. Typical processing route includes three steps. Firstly, the ordered template of PS was created. The as-received PS with mean particle size of 203 nm was used as the raw material.