Good dielectric performance and broadband dielectric polarization in Ag,Nb co-doped TiO2

Due to the demand of miniaturization and integration for ceramic capacitors in electronic components market, TiO₂-based ceramics with colossal permittivity has become a research hotspot in recent years. In this work, we report that Ag⁺/Nb⁵⁺ co-doped (Ag_1/4Nb_3/4)_xTi_1−xO₂ (ANTOx) ceramics with colossal permittivity over a wide frequency and temperature range were successfully prepared by a traditional solid–state method. Notably, compositions of ANTO0.005 and ANTO0.01 respectively exhibit both low dielectric loss (0.040 and 0.050 at 1 kHz), high dielectric permittivity (9.2 × 10³ and 1.6 × 10⁴ at 1 kHz), and good thermal stability, which satisfy the requirements for the temperature range of application of X9R and X8R ceramic capacitors, respectively. The origin of the dielectric behavior was attributed to five dielectric relaxation phenomena, i.e., localized carriers' hopping, electron–pinned defect–dipoles, interfacial polarization, and oxygen vacancies ionization and diffusion, as suggested by dielectric temperature spectra and valence state analysis via XPS; wherein, electron-pinned defect–dipoles and internal barrier layer capacitance are believed to be the main causes for the giant dielectric permittivity in ANTOx ceramics.     

Zeolitic-imidazolate frameworks-derived Co3S4/NiS@Ni foam heterostructure as highly efficient electrocatalyst for oxygen evolution reaction

Transition metals sulfide-based nanomaterials have recently received significant attention as a promising cathode electrode for the oxygen evolution reaction (OER) due to their easily tunable electronic, chemical, and physical properties. However, the poor electrical conductivity of metal-sulfide materials impedes their practical application in energy devices. Herein, firstly nano-sized crystals of cobalt-based zeolitic-imidazolate framework (Co-ZIF) arrays were fabricated on nickel-form (NF) as the sacrificial template by a facile solution method to enhance the electrical conductivity of the electrocatalyst. Then, the Co₃S₄/NiS@NF heterostructured arrays were synthesized by a simple hydrothermal route. The Co-ZIFs derived Co₃S₄ nanosheets are grown successfully on NiS nanorods during the hydrothermal sulfurization process. The bimetallic sulfide-based Co₃S₄/NiS@NF-12 electrocatalyst demonstrated a very low overpotential of 119 mV at 10 mA cm^?2 for OER, which is much lower than that of mono-metal sulfide NiS@NF (201 mV) and ruthenium-oxide (RuO₂) on NF (440 mV) electrocatalysts. Furthermore, the Co₃S₄/NiS@NF-12 electrocatalyst showed high stability during cyclic voltammetry and chronoamperometry measurements. This research work offers an effective strategy for fabricating high-performance non-precious OER electrocatalysts.     

Porous CoNiOOH nanosheets derived from the well-mixed MOFs as stable and highly efficient electrocatalysts for water oxidation

The development of efficient and stable electrocatalysts is of great significance for improving water splitting. Among them, transition metal oxyhydroxides show excellent performance in oxygen evolution reactions (OER), but there are certain difficulties in direct preparation. Recently, Metal–organic frameworks (MOFs) as precatalysts or precursors have shown promising catalytic performance in OER and can be decomposed under alkaline conditions. Therefore, using a mild and controllable way to convert MOFs into oxyhydroxides and retaining the original structural advantages is crucial for improving the catalytic activity. Herein, a rapid electrochemical strategy is used to activate well-mixed MOFs to prepare Co/Ni oxyhydroxide nanosheets for efficient OER catalysts, and the structural transformation in this process was investigated in detail by using scanning electron microscope, X-ray diffraction, Raman, X-ray photoelectron spectroscopy and electrochemical methods. It is discovered that electrochemical activation can promote ligand substitution of well-mixed MOFs to form porous oxyhydroxide nanosheets and tune the electronic structure of the metal (Co and Ni), which can lead to more active site exposure and accelerate charge transfer. In addition, the change of structure also improves hydrophilicity, as well as benefiting from the strong synergistic effect between multiple species, the optimal a-MCoNi–MOF/NF has excellent OER performance and long-term stability. More obviously, the porous CoNiOOH nanosheets are formed in situ during electrochemical activation process through structural transformation and acts as the active centers. This work provides new insights for mild synthesis of MOFs derivatives and also provides ideas for the preparation of highly efficient catalysts.     

Influence of metal-support interactions on reaction pathways over Ni/CeZrOx–Al2O3 catalysts for ethanol steam reforming

This work investigates selective Ni locations over Ni/CeZrO_x–Al₂O₃ catalysts at different Ni loading contents and their influences on reaction pathways in ethanol steam reforming (ESR). Depending on the Ni loading contents, the added Ni selectively interacts with CeZrO_x–Al₂O₃, resulting in the stepwise locations of Ni over CeZrO_x–Al₂O₃. This behavior induces a remarkable difference in hydrogen production and coke formation in ESR. The selective interaction between Ni and CeZrO_x for 10-wt.% Ni generates more oxygen vacancies in the CeZrO_x lattice. The Ni sites near the oxygen vacancies enhance reforming via steam activation, resulting in the highest hydrogen production rate of 1863.0 μmol/g_cat·min. In contrast, for 15 and 20-wt.% Ni, excessive Ni is additionally deposited on Al₂O₃ after the saturation of Ni–CeZrO_x interactions. These Ni sites on Al₂O₃ accelerate coking from the ethylene produced on the acidic sites, resulting in a high coke amount of 19.1 mg_c/g_cat·h (20Ni/CZ-Al).     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Electrodeposition of the manganese-doped nickel-phosphorus catalyst with enhanced hydrogen evolution reaction activity and durability

Hydrogen technology is widely considered a novel clean energy source, and electrolysis is an effective method for hydrogen evolution. Therefore, efficient hydrogen evolution reaction (HER) catalysts are urgently needed to replace precious metal catalysts and meet ecological and environmental protection standards. Herein, Ni–Mn–P electrocatalysts are synthesized using facile electrodeposition technology. The influence of the Mn addition on the catalytic behavior is studied by the comprehensive analysis of catalytic performance and morphology of the catalysts. Among them, the Ni–Mn–P0.01 catalyst exhibits small coral-like structures, greatly improving the adsorption and desorption of hydrogen ions and reducing the overpotential hydrogen evolution. Consequently, overpotential at 10 mA cm^?2 electric current density is 113 mV, and the value of the Tafel slope achieves 74 mV/dec. Furthermore, the Ni–Mn–P catalyst shows long-time (20 h) stability at current densities of 10 and 60 mA/cm². The results confirm that the synergistic effect of Ni, Mn, and P accelerates the electrochemical reaction. Meanwhile, the addition of manganese element can change the micromorphology of the catalyst, thereby exposing more active sites to participate in the reaction, enhancing water ionization, improving the catalytic performance. This study opens a new way toward improving the activity of the catalyst by adjusting Mn concentration during the electrodeposition process.     

Generation,validation and application of dynamic load profiles in flow measurement using cavitating Herschel–Venturi nozzles

Today, utility meters for water are tested for measurement behavior at stable operating conditions at specified flow rates as part of the approval process. The measurement error that occurs during start and stop or when changing between flow rates may not be taken into account. In addition, there are new technologies whose measuring behavior under real-world conditions is only known to a limited extend. To take these facts into account, a new method has been developed and tested to determine the measurement behavior of water meters under dynamic load profiles as they occur in the real application. For this purpose, a test rig for flow rate measurement was extended by a cavitation nozzle apparatus and the generation of dynamic load profiles was validated. For the cavitation nozzles used, possible factors influencing the flow rate, such as temperature and purity of the water as well as the upstream pressure were investigated. Using different types of domestic water meters, the applicability of the dynamic test procedure was demonstrated and the measurement behavior of the meters was characterised.     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.