Phosphoric Acid-assisted Pretreatment Strategy for the Rational Synthesis of Lignin-derived Hierarchical Porous Carbon Toward High-performance Supercapacitors

In this article, a facile two-step activation method, coupled with phosphoric acid (H₃PO₄)-assisted pretreatment and followed KOH activation, was reported for constructing hierarchical porous carbon (HPC) materials derived from lignin. The introduction of H₃PO₄, cross-linked with lignin sources generated phosphate (and/or polyphosphate) ester groups throughout the lignin structure, which endowed the pre-activated intermediate char (IC) with a hierarchical porous structure. Such phosphate esters contributed to the multi-scale pore structure within the pre-activated IC, which was beneficial for the uniform distribution and impregnation of subsequent KOH activators, thus leading to the formation of HPC materials. The as-prepared HPC exhibited a large specific surface area (SSA) of 1345.1 m²/g, which ensures the accessibility of the ion diffusion pathways. The supercapacitors integrated with HPC delivered a high specific capacitance of 241 F/g (in a three-electrode system) and outstanding rate capability with an 80.9% capacitance retention from 0.5 A/g to an ultra-high current density of 50 A/g.     

机械结构分层优化技术研究进展

复杂机械结构优化设计一般表现为多目标、多约束、多参数的优化问题,所以复杂结构优化设计过程通常存在计算复杂、不易收敛等困难。分层优化技术是复杂结构优化设计的一种有效途径,通过将优化问题中复杂的约束、设计变量以及功能目标合理分解为若干子层问题进行求解,然后通过协调得到复杂机械结构整体优化问题的结果。文章对机械结构分层优化技术的理论、应用研究现状进行了总结并探讨了其关键技术环节及发展趋势。     

一种分散在多孔碳上的碳包覆硅负极的制备及应用

硅负极具有高比容量的显著优势,其理论比容量（4 200 mA∙h/g）达到传统石墨负极的10倍以上,被认为是锂离子电池最有潜力的负极之一。然而,硅负极存在导电性较差、充放电过程中体积膨胀巨大等诸多问题,导致其循环性能较差,限制了大规模实际应用。本文提供了一种高性能硅负极的制备方法及应用,通过将硅负极分散在多级孔碳中,连同黏结剂聚丙烯腈涂覆在集流体上,再对极片进行热处理实现聚丙烯腈碳包覆,有效提高电极的整体导电性并能为巨大的体积变化提供空间,从而提升硅负极的大倍率性能和循环稳定性。     

Synthesis of Hierarchical 4H/fcc Ru Nanotubes for Highly Efficient Hydrogen Evolution in Alkaline Media

Hierarchical metal nanostructures containing 1D nanobuilding blocks have stimulated great interest due to their abundant active sites for catalysis. Herein, hierarchical 4H/face‐centered cubic (fcc) Ru nanotubes (NTs) are synthesized by a hard template‐mediated method, in which 4H/fcc Au nanowires (NWs) serve as sacrificial templates which are then etched by copper ions (Cu²⁺) in dimethylformamide. The obtained hierarchical 4H/fcc Ru NTs contain ultrathin Ru shells (5–9 atomic layers) and tiny Ru nanorods with length of 4.2 ± 1.1 nm and diameter of 2.2 ± 0.5 nm vertically decorated on the surface of Ru shells. As an electrocatalyst for the hydrogen evolution reaction in alkaline media, the hierarchical 4H/fcc Ru NTs exhibit excellent electrocatalytic performance, which is better than 4H/fcc Au‐Ru NWs, commercial Pt/C, Ru/C, and most of the reported electrocatalysts.     

Hierarchical Coherent Phonons in a Superatomic Semiconductor

The coupling of phonons to electrons and other phonons plays a defining role in material properties, such as charge and energy transport, light emission, and superconductivity. In atomic solids, phonons are delocalized over the 3D lattice, in contrast to molecular solids where localized vibrations dominate. Here, a hierarchical semiconductor that expands the phonon space by combining localized 0D modes with delocalized 2D and 3D modes is described. This material consists of superatomic building blocks (Re₆Se₈) covalently linked into 2D sheets that are stacked into a layered van der Waals lattice. Using transient reflectance spectroscopy, three types of coherent phonons are identified: localized 0D breathing modes of isolated superatom, 2D synchronized twisting of superatoms in layers, and 3D acoustic interlayer deformation. These phonons are coupled to the electronic degrees of freedom to varying extents. The presence of local phonon modes in an extended crystal opens the door to controlling material properties from hierarchical phonon engineering.     

Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation

The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.     

Scalable Production of Wearable Solid-State Li-Ion Capacitors from N-Doped Hierarchical Carbon

Smart and wearable electronics have aroused substantial demand for flexible portable power sources, but it remains a large challenge to realize scalable production of wearable batteries/supercapacitors with high electrochemical performance and remarkable flexibility simultaneously. Here, a scalable approach is developed to prepare wearable solid-state lithium-ion capacitors (LICs) with superior performance enabled by synergetic engineering from materials to device architecture. Nitrogen-doped hierarchical carbon (HC) composed of 1D carbon nanofibers welded with 2D carbon nanosheets is synthesized via a unique self-propagating high-temperature synthesis (SHS) technique, which exhibits superior electrochemical performance. Subsequently, inspired by origami, here, wave-shaped LIC punch-cells based on the above materials are designed by employing a compatible and scalable post-imprint technology. Finite elemental analysis (FEA) confirms that the bending stress of the punch-cell can be offset effectively, benefiting from the wave architecture. The wearable solid-state LIC punch-cell exhibits large energy density, long cyclic stability, and superior flexibility. This study demonstrates great promise for scalable fabrication of wearable energy-storage systems.     

自适应融合层级特征的混合退化图像复原算法

多种退化类型混合的图像比单一类型的退化图像降质更严重,很难建立精确模型对其复原,研究端到端的神经网络算法是复原的关键.现有的基于操作选择注意力网络的算法(operation-wiseattentionnetwork,OWAN)虽然有一定的性能提升,但是其网络过于复杂,运行较慢,复原图像缺乏高频细节,整体效果也有提升的空间.针对这些问题,提出一种基于层级特征融合的自适应复原算法.该算法直接融合不同感受野分支的特征,增强复原图像的结构;用注意力机制对不同层级的特征进行动态融合,增加模型的自适应性,降低了模型冗余;另外,结合L1损失和感知损失,增强了复原图像的视觉感知效果.在DIV2K,BSD500等数据集上的实验结果表明,该算法无论是在峰值信噪比和结构相似性上的定量分析,还是在主观视觉质量方面,均优于OWAN算法,充分证明了该算法的有效性.