Izquierdo Rodolfo, Zadorosny Rafael, Rosales Merlín, Marrero-Ponce Yovani, Cubillan Néstor
Departamento de Física e Química, Universidade Estadual Paulista (UNESP), Faculdade de Engenharia, Caixa Postal 31, 15385-000 Ilha Solteira, SP, Brazil.
Grupo de Química Bioorgánica, Facultad de Ciencias Exactas y Naturales, Universidad de Cartagena, Cartagena de Indias 130015, Colombia.
ACS Omega. 2025 Apr 30;10(18):18312-18331. doi: 10.1021/acsomega.4c09503. eCollection 2025 May 13.
Developing highly active catalysts for quinoline hydrogenation is crucial for efficient hydrogen carrier technologies and clean fossil fuel hydrodenitrogenation. In this work, we employed Tensor Algebra-based 3D-Geometrical Molecular Descriptors (QuBiLS-MIDAS) to develop Quantitative Structure-Property Relationship (QSPR) models predicting the initial rate of homogeneous quinoline hydrogenation catalyzed by transition metal complexes of Ru, Rh, Os, and Ir. A data set of 32 catalytic precursors was used: 25 for model training (training set) and 7 for external validation (testing set). Multiple linear regression analysis yielded a model with good predictive ability for the training set ( = 0.90) and satisfactory external validation for the testing set (Q = 0.86). The model's descriptors highlighted the importance of hardness, softness, electrophilicity, and mass in predicting catalytic activity. The virtual screening revealed that Rh and Ir complexes with π-acidic ligands (, olefins, diolefins, and η-Cp) and nitrile ligands exhibited the highest predicted catalytic activity, suggesting potential for further improvement through ligand structural modification. Notably, iridium complexes, particularly those with tri(cyclopropyl)phosphine ligands, demonstrated significant potential for hydrogen storage, transport, and production, underscoring their relevance in sustainable energy systems. These findings demonstrate the potential of the QuBiLS-MIDAS approach for design of efficient catalysts for quinoline hydrogenation processes.
开发用于喹啉氢化的高活性催化剂对于高效氢载体技术和清洁化石燃料加氢脱氮至关重要。在这项工作中，我们采用基于张量代数的3D几何分子描述符（QuBiLS-MIDAS）来开发定量结构-性质关系（QSPR）模型，以预测由Ru、Rh、Os和Ir的过渡金属配合物催化的均相喹啉氢化的初始速率。使用了一个包含32种催化前体的数据集：25种用于模型训练（训练集），7种用于外部验证（测试集）。多元线性回归分析得出了一个对训练集具有良好预测能力（R² = 0.90）且对测试集具有令人满意的外部验证（Q² = 0.86）的模型。该模型的描述符突出了硬度、软度、亲电性和质量在预测催化活性方面的重要性。虚拟筛选表明，具有π-酸性配体（如烯烃、二烯烃和η-Cp）和腈配体的Rh和Ir配合物表现出最高的预测催化活性，这表明通过配体结构修饰有进一步改进的潜力。值得注意的是，铱配合物，特别是那些具有三（环丙基）膦配体的配合物，在储氢、输氢和制氢方面显示出巨大潜力，突显了它们在可持续能源系统中的相关性。这些发现证明了QuBiLS-MIDAS方法在设计用于喹啉氢化过程的高效催化剂方面的潜力。
