Robust nickel silicate catalysts with high Ni loading for CO2 methanation

CO₂ is the main component of greenhouse gases and also an important carbon source. The hydrogenation of CO₂ to methane using Ni-based catalysts can not only alleviate CO₂ emissions but also obtain useful fuels. However, Ni-based catalysts face one major problem of the sintering of Ni nanoparticles in the process of CO₂ methanation. Thus, this work has synthesized a series of efficient and robust nickel silicate catalysts (NiPS−X) with different nickel content derived from nickel phyllosilicate by the hydrothermal method. It was found that the Ni loading plays a critical role in the structure and catalytic performance of the NiPS−X catalysts. The catalytic performance gradually increases with the increase of Ni loading. In particular, the highly dispersed NiPS-1.6 catalyst with a high Ni loading of 34.3 wt% could obtain the CO₂ conversion greater than 80%, and the methane selectivity was close to 100% for 48 h at 330 °C and the GHSV of 40,000 mL g⁻¹ h⁻¹. The excellent catalytic property can be assigned to the high dispersion of Ni nanoparticles and the strong interaction between the active component and the carrier, which is derived from a unique layered silicate structure with lots of nickel phyllosilicate and a large number of Lewis acid sites.     

Pattern transition of two-dimensional Faraday waves at an extremely shallow depth

In this paper, we experimentally investigate the pattern transition of two-dimensional Faraday waves at an extremely shallow depth in a Hele-Shaw cell. Several patterns of Faraday waves are observed, which have some significant differences in wave profile, wave height and wave length. It is found that, in a wide range of the forcing frequency f, there always exists a region of the acceleration amplitude A, in which there exist the so-called hysteretic jumps between different patterns of Faraday waves. All of these experimental observations could enrich our knowledges about the Faraday waves and would be helpful to the further theoretical studies on the related topic in future.     

Stability analysis of neural networks with interval time-varying delays

The global exponential stability is investigated for neural networks with interval time-varying delays. Based on the Leibniz-Newton formula and linear matrix inequality technique, delay-dependent stability criteria are proposed to guarantee the exponential stability of neural networks with interval time-varying delays. Some numerical examples and comparisons are provided to show that the proposed results significantly improve the allowable upper and lower bounds of delays over some existing ones in the literature.     

Selective image encryption using a spatiotemporal chaotic system

A universal selective image encryption algorithm, in which the spatiotemporal chaotic system is utilized, is proposed to encrypt gray-level images. In order to resolve the tradeoff between security and performance, the effectiveness of selective encryption is discussed based on simulation results. The scheme is then extended to encrypt RGB color images. Security analyses for both scenarios show that the proposed schemes achieve high security and efficiency.     

Exponential stabilization of chaotic systems with delay by periodically intermittent control

This paper studies the exponential stabilization problem for a class of chaotic systems with delay by means of periodically intermittent control. A unified exponential stability criterion, together with its simplified versions, is established by using Lyapunov function and differential inequality techniques. A suboptimal intermittent controller is designed with respect to the general cost function under the assumption that the control period is fixed. Numerical simulations on two chaotic oscillators are presented to verify the theoretical results.     

Contradictory effect of gold nanoparticle-decorated molybdenum sulfide nanocomposites on amyloid-β-40 aggregation

The effect of gold nanoparticle-decorated molybdenum sulfide (AuNP-MoS₂) nanocomposites on amyloid-β-40 (Aβ₄₀) aggregation was investigated. The interesting discovery was that the effect of AuNP-MoS₂ nanocomposites on Aβ₄₀ aggregation was contradictory. Low concentration of AuNP-MoS₂ nanocomposites could enhance the nucleus formation of Aβ₄₀ peptides and accelerate Aβ₄₀ fibrils aggregation. However, although high concentration of AuNP-MoS₂ nanocomposites could enhance the nucleus formation of Aβ₄₀ peptides, it eventually inhibited Aβ₄₀ aggregation process. It might be attributed to the interaction between AuNP-MoS₂ nanocomposites and Aβ₄₀ peptides. For low concentration of AuNP-MoS₂ nanocomposites, it was acted as nuclei, resulting in the acceleration of the nucleation process. However, the structural flexibility of Aβ₄₀ peptides was limited as the concentration of AuNP-MoS₂ nanocomposites was increased, resulting in the inhibition of Aβ₄₀ aggregation. These findings suggested that AuNP-MoS₂ nanocomposites might have a great potential to design new multifunctional material for future treatment of amyloid-related diseases.     

Ag@硅氧倍半聚合物的合成及其对对硝基苯酚的催化还原性能

以氨丙基三乙氧基硅烷和草酰氯为原料,合成得到链上含亚氨基和羰基官能团的硅氧倍半聚合物。以此聚合物为载体,通过配位吸附和还原得到银纳米粒子（平均粒径约为15 nm）高度分散于硅氧烷聚合物表面的复合物。研究结果表明,复合物中银负载量（质量分数）约为13.66%,在水溶液中25℃、6 min内可将对硝基苯酚（4-NP）完全催化还原为对氨基苯酚（4-AP）,且7次循环使用后依然保持95%以上的催化活性。在常温常压条件下,催化剂的最高活性达到33.0 g_4-AP·g_Ag^-1,表现出优异的催化还原性能。