零维到三维结构中三核铜自旋阻挫簇单元的磁构关系和反对称交换

量子自旋液体是最近几年刚被人们证实除铁磁体、反铁磁体之外的第三种磁性类型,因其有望解释高温超导的运行机制、改变计算机硬盘信息存储方式而在物理、材料等领域备受关注。自旋阻挫作为量子自旋液体的最小单元可能是解开量子自旋液体诸多问题的钥匙,所以在磁学、电学研究领域再一次成为人们研究的热点。基于文献报道的三核铜配合物[Cu3(μ3-OH)(μ-OPz)3(NO3)2(H2O)2]·CH3OH(1),我们合成了三维金属有机框架配合物{[Ag(HOPz)Cu3(μ3-OH)(NO3)3(OPz)2Ag(NO3)]·6H2O}n(2)(HOPz=甲基(2-吡嗪基)酮肟),并从自旋阻挫的角度对二者磁性质进行对比和详细分析。磁化率数据表明自旋间有很强的反铁磁相互作用和反对称交换。通过包含各向同性和反对称交换的哈密顿算符对两者磁学数据进行拟合并研究其磁构关系,所获最佳拟合参数为:配合物1:Jav=-426 cm^-1,g⊥=1.83,g∥=2.00;配合物2:Jav=-401 cm^-1,g⊥=1.85,g∥=2.00。     

Fabrication of Microporous Metal–Organic Frameworks in Uninterrupted Mesoporous Tunnels: Hierarchical Structure for Efficient Trypsin Immobilization and Stabilization

Hierarchically porous metal–organic frameworks (HP-MOFs) are promising in various applications. Most reported HP-MOFs are prepared based on the generation of mesopores in microporous frameworks, and the formed mesopores are connected by microporous channels, limiting the accessibility of mesopores for bulky molecules. A hierarchical structure is formed by constructing microporous MOFs in uninterrupted mesoporous tunnels. Using the confined space in as-prepared mesoporous silica, highly dispersed metal precursors for MOFs are coated on the internal surface of mesoporous tunnels. Ligand vapor-induced crystallization is employed to enable quantitative formation of MOFs in situ, in which sublimated ligands diffuse into mesoporous tunnels and react with metal precursors. The obtained hierarchically porous composites exhibit record-high adsorption capacity for the bulky molecule trypsin. The thermal and storage stability of trypsin is improved upon immobilization on the composites.     

Single Crystal to Single Crystal (SC‐to‐SC) Transformation from a Nonporous to Porous Metal–Organic Framework and Its Application Potential in Gas Adsorption and Suzuki Coupling Reaction through Postmodification

A new amino‐functionalized strontium–carboxylate‐based metal–organic framework (MOF) has been synthesized that undergoes single crystal to single crystal (SC‐to‐SC) transformation upon desolvation. Both structures have been characterized by single‐crystal X‐ray analysis. The desolvated structure shows an interesting 3D porous structure with pendent ?NH₂ groups inside the pore wall, whereas the solvated compound possesses a nonporous structure with DMF molecules on the metal centers. The amino group was postmodified through Schiff base condensation by pyridine‐2‐carboxaldehyde and palladium was anchored on that site. The modified framework has been utilized for the Suzuki cross‐coupling reaction. The compound shows high activity towards the C?C cross‐coupling reaction with good yields and turnover frequencies. Gas adsorption studies showed that the desolvated compound had permanent porosity and was microporous in nature with a BET surface area of 2052 m² g^?1. The material also possesses good CO₂ (8 wt %) and H₂ (1.87 wt %) adsorption capabilities.     

pH-Sensitive Oscillatory Motion of a Urease Motor on the Urea Aqueous Phase

A self-propelled object coupled with an enzyme reaction between urease and urea was investigated at the air/aqueous interface. A plastic object that was fixed to a urease-immobilized filter paper was used as a self-propelled object, termed a urease motor, placed on an aqueous urea solution. The driving force of the urease motor is the difference in the surface tension around the object. Oscillatory motion or no motion was triggered depending on the initial pH of the urea solution. Both the frequency and maximum speed of the oscillatory motion varied depending on the initial pH of the water phase. The mechanisms underlying the oscillatory motion and no motion were discussed in relation to the bell-shaped enzyme activity of urease in the enzyme reaction and the surface tension around the urease motor.     

紫色球杆菌视紫红质光谱特性的机器学习研究

近年来，机器学习等人工智能技术被应用于蛋白质工程，其在蛋白质结构、功能预测、催化活性等研究中具有独特优势。在未知蛋白质结构的情况下，将蛋白质序列和功能特性与机器学习相结合，基于序列-活性关系（innovative sequence-activity relationship，ISAR）算法，将蛋白质氨基酸序列数字化，用快速傅里叶变换（fast four transform，FFT）进行预处理，再进行偏最小二乘回归建模，可在数据集较少情况下拟合得到最佳模型。通过机器学习对紫色球杆菌视紫红质（gloeobacter violaceus rhodopsin，GR）的突变体蛋白质氨基酸序列与光谱最大吸收波长进行建模，获得了最佳模型。用最佳索引LEVM760106建模得到的确定系数R² 为0.944，均方误差E为11.64。用小波变换进行的预处理，其R² 虽也约为0.944，但E大于11.64，不及FFT进行的预处理。方法较好地解决了蛋白质序列与功能特性之间的数学建模问题，在蛋白质工程中可为预测更优的突变体提供支持。     

Dynamic behavior and stability analysis of nonlinear modes in the fourth-order generalized Ginzburg–Landau model with near $$\mathcal{PT}$$ PT -symmetric potentials

Nonlinear Dynamics - Parametrically excited oscillators are used in several domains, in particular to improve the dynamical behaviour of systems like in the case of the parametric amplification or...