La0.8Sr0.2Fe1-xScxO3-δ催化剂的制备、表征及甲烷催化燃烧性能

采用甘氨酸-硝酸盐溶液燃烧法制备了钙钛矿型氧化物催化剂La_0.8Sr_0.2Fe_1-xSc_xO_3-δ (LSFS, x=0, 0.3,0.4, 0.5, 0.6, 0.8, 1), 利用X射线衍射(XRD)、H₂程序升温还原(H₂-TPR)、扫描电子显微镜(SEM)和比表面积测试等手段对催化剂进行了系统表征, 并在常压微型固定床反应器上考察了催化剂对甲烷燃烧的催化性能. 结果表明, 经空气气氛下900 °C煅烧5 h制备的LSFS均具有单一的钙钛矿结构, 在La_0.8Sr_0.2FeO_3-δ (LSF)中掺杂Sc有助于改善催化剂的抗烧结性能, 提高催化剂的比表面积. 当LSF 中的Sc 掺杂量为0.4-0.6 时, 所形成的LSFS表现出良好的甲烷催化燃烧活性, 其中Sc 掺杂量为0.5 时, 其起燃温度(T₁₀)和完全转化温度(T₉₀)分别为406和563 °C, 与La_0.8Sr_0.2FeO_3-δ和La_0.8Sr_0.2ScO_3-δ相比, T₁₀分别降低了14和87 °C; T₉₀分别降低了59和95 °C.     

基于特征值分析的骨架机理获取方法

基于特征值分析法,建立了一套复杂化学反应动力学模型简化方法,并采用该方法对甲烷空气燃烧的详细化学反应动力学模型进行了简化.从GRI1.2得到了21个组分,83个反应的骨架机理.该机理与详细的GRI1.2机理和DRM19机理在不同化学计量比和不同压力下对比了点火延迟时间,结果表明简化机理能有效地再现详细反应动力学模型的反应机理,并具有更高的计算精度.从GRI3.0简化得到两种骨架机理分别为26个组分、120个反应和30个组分、140个反应.这两个机理都能很好地对火焰传播速度以及主要组分和NO浓度分布进行反应动力学模拟.     

Détonabilité de mélanges stoechiométriques méthane–hydrogène–oxygène–azoteDetonation characteristics of stoichiometric mixtures of methane–hydrogen–oxygen–nitrogen

The goal of this Note is to determine the influence of H₂ addition on the detonation and detonability of stoichiometric CH₄/O₂/N₂ mixtures for three values of the nitrogen dilution $\beta ={\mathrm{N}}_2/{\mathrm{O}}_2$ ($\beta =0$ (oxygen); 2; 3.76 (air)) and also of the influence of the initial temperature. It is based on the measurement of the mean cell size of the steady self-sustained detonation in these mixtures, this characteristic length being representative of the mixture detonability. Results indicate that the detonability of the (CH₄H₂) mixture is mainly controlled by the heavier fuel, i.e., CH₄ and for instance the detonability of the mixture where 20% of CH₄ volume has been replaced by H₂ is nearly the same as that of the mixture where CH₄ is the only fuel. The influence of the initial temperature on the detonability depends on N₂ concentration. To cite this article: C. Matignon et al., C. R. Mecanique 334 (2006).     

模拟含双锌核的甲烷单加氧酶催化甲烷和乙烷羟基化理论研究

采用密度泛函B3LYP方法,研究含双锌核的甲烷单加氧酶催化底物CH3X (X=H,CH3) 羟基化的反应机理.用cis-(HCOO)(C3N2H4)Zn2(μ-O)2(COOH)(C3N2H4)(其中C3N2H4＝咪唑)模拟双锌核甲烷单加氧酶中的关键化合物Q.研究表明,反应通过自由基回弹机理发生.首先,底物CH3X (X=H,CH3)与Q中的一个桥形氧发生相互作用生成分子复合物QCH3X.然后,Q中的一个桥形氧进一步夺取底物中的氢原子,生成QH和XCH2双自由基.这两种双自由基很容易结合生成醇类分子复合物PXCH2OH,其中P= cis-(HCOO)(C3N2H4)Zn(μ-O)Zn(COOH)(C3N2H4).最后,分子复合物PXCH2OH脱去羟基化合物XCH2OH生成P.在298.15K和1atm的条件下,含双锌核甲烷单加氧酶Q催化CH4和CH3CH3羟基化反应在蛋白质溶液中的速率常数分别为6.414×10-19和1.542×10-6 dm3·mol-1·s-1.     

Beyond Reduction Cocatalysts: Critical Role of Metal Cocatalysts in Photocatalytic Oxidation of Methane with Water**

Environmentally sustainable and selective conversion of methane to valuable chemicals under ambient conditions is pivotal for the development of next-generation photocatalytic technology. However, due to the lack of microscopic knowledge about non-thermal methane conversion, controlling and modulating photocatalytic oxidation processes driven by photogenerated holes remain a challenge. Here, we report novel function of metal cocatalysts to accept photogenerated holes and dominate selectivity of methane oxidation, which is clearly beyond the conventional concept in photocatalysis that the metal cocatalysts loaded on the surfaces of semiconductor photocatalysts mostly capture photogenerated electrons and dominate reduction reactions exclusively. The novel photocatalytic role of metal cocatalysts was verified by operando molecular spectroscopy combined with real-time mass spectrometry for metal-loaded Ga₂O₃ model photocatalysts under methane and water vapor at ambient temperature and pressure. Our concept of metal cocatalysts that work as active sites for both photocatalytic oxidation and reduction provides a new understanding of photocatalysis and a solid basis for controlling non-thermal redox reactions by metal-cocatalyst engineering.     

Dual Lewis Acid-Base Sites Regulate Silver-Copper Bimetallic Oxide Nanowires for Highly Selective Photoreduction of Carbon Dioxide to Methane

Highly selective photoreduction of CO₂ to valuable hydrocarbons is of great importance to achieving a carbon-neutral society. Precisely manipulating the formation of the Metal₁⋅⋅⋅C=O⋅⋅⋅Metal₂ (M₁⋅⋅⋅C=O⋅⋅⋅M₂) intermediate on the photocatalyst interface is the most critical step for regulating selectivity, while still a significant challenge. Herein, inspired by the polar electronic structure feature of CO₂ molecule, we propose a strategy whereby the Lewis acid-base dual sites confined in a bimetallic catalyst surface are conducive to forming a M₁⋅⋅⋅C=O⋅⋅⋅M₂ intermediate precisely, which can promote selectivity to hydrocarbon formation. Employing the Ag₂Cu₂O₃ nanowires with abundant Cu⋅⋅⋅Ag Lewis acid-base dual sites on the preferred exposed {110} surface as a model catalyst, 100 % selectivity toward photoreduction of CO₂ into CH₄ has been achieved. Subsequent surface-quenching experiments and density functional theory (DFT) calculations verify that the Cu⋅⋅⋅Ag Lewis acid-base dual sites do play a vital role in regulating the M₁⋅⋅⋅C=O⋅⋅⋅M₂ intermediate formation that is considered to be prone to convert CO₂ into hydrocarbons. This study reports a highly selective CO₂ photocatalyst, which was designed on the basis of a newly proposed theory for precise regulation of reaction intermediates. Our findings will stimulate further research on dual-site catalyst design for CO₂ reduction to hydrocarbons.     

预测金属有机骨架甲烷和氢气输送能力的迁移学习建模

基于深度神经网络(DNN)和迁移学习(TL),使用少量数据建立TL模型,精准预测了金属有机骨架(MOFs)的甲烷和氢气输送性能.首先,使用8414个MOFs在298 K/65 bar～298 K/5.8 bar(1 bar=0.1 MPa)条件下的甲烷输送数据训练一个决定系数(R²)为0.973的DNN[源任务(ST)模型].随后,将ST模型的部分参数冻结,使用100个MOFs在233 K/65 bar～358 K/5.8 bar条件下的甲烷输送数据和100个MOFs在198 K/100 bar～298 K/5 bar条件下的氢气输送数据分别微调ST模型,进行TL建模.结果表明,两个TL模型的R²分别为0.968和0.945,均高于其它5个传统的ML模型.所开发的TL模型在预测小数据集时具有高精度与高稳定性.最后,使用排列特征重要度方法来计算描述符重要度,明确了模型之间的“知识”共享情况,并在此基础上探讨了重要描述符和输送能力之间的关系.     

Bayesian Optimization-guided Discovery of High-performance Methane Combustion Catalysts based on Multi-component PtPd@CeZrOx Core–Shell Nanospheres

Formula regulation of multi-component catalysts by manual search is undoubtedly a time-consuming task, which has severely impeded the development efficiency of high-performance catalysts. In this work, PtPd@CeZrO_x core–shell nanospheres, as a successful case study, is explicitly demonstrated how Bayesian optimization (BO) accelerates the discovery of methane combustion catalysts with the optimal formula ratio (the Pt/Pd mole ratio ranges from 1/2.33–1/9.09, and Ce/Zr from 1/0.22–1/0.35), which directly results in a lower conversion temperature (T₅₀ approaching to 330 °C) than ones reported hitherto. Consequently, the best sample obtained could be efficiently developed after two rounds of iterations, containing only 18 experiments in all that is far less than the common human workload via the traditional trial-and-error search for optimal compositions. Further, this BO-based machine learning strategy can be straightforward extended to serve the autonomous discovery in multi-component material systems, for other desired properties, showing promising opportunities to practical applications in future.     

Hydrogen-Deuterium Isotope Exchange of Methane via Non-redox Palladium Catalysis

Under mild conditions, Pd(II) catalysts coordinated to tridentate NHC-amidate-ether ligand successfully activated the carbon-hydrogen bond to facilitate the hydrogen/deuterium isotope exchange on methane. The structural features and catalytic behavior suggested an intriguing non-redox catalytic system derived from the amidate nitrogen. As the amidate nitrogen acts as an internal base, the metal center was able to maintain the oxidation state throughout the reaction. Accordingly, the catalytic system demonstrated its reactivity and stability during the H/D exchange on methane resulting in a high degree of deuterium conversions (44 %) and turnover number (346) under low temperature conditions.