A Framework for Predicting Catalytic Activity of Metal-Organic Frameworks

Thesis Abstract

The catalytic performance of metal-organic frameworks (MOFs) has garnered significant interest due to their unique structural tunability, high surface area, and potential applications in sustainable chemical transformations. However, the lack of a comprehensive predictive framework hampers the systematic design and optimization of MOFs for specific catalytic functions, thereby impeding their broader deployment in industrial and environmental processes. This study aims to develop a predictive model that accurately estimates the catalytic activity of MOFs based on their structural and chemical characteristics, facilitating targeted synthesis and application. The specific objectives include (1) identifying key physicochemical parameters influencing catalytic activity; (2) formulating a conceptual framework integrating structural, electronic, and surface properties; (3) creating a predictive model using advanced statistical and machine learning techniques; and (4) validating the model through experimental data and case studies. To achieve these aims, a mixed-methods research design was adopted, combining quantitative data analysis with qualitative insights from existing literature and experimental results. The population of the study comprised 120 MOF samples with well-characterized structures sourced from peer-reviewed databases, with a focus on MOFs employed in catalysis, such as UiO-66, MIL-101, and ZIF-based frameworks. A stratified sampling technique was used to select 60 MOF samples with diverse topologies, metal nodes, and functional groups, ensuring representative coverage across different categories. Data collection involved detailed physicochemical characterization using techniques such as powder X-ray diffraction (PXRD), Brunauer-Emmett-Teller (BET) surface area analysis, X-ray photoelectron spectroscopy (XPS), and Fourier-transform infrared spectroscopy (FTIR). Catalytic activity data were obtained from standardized reaction trials involving organic transformations, including oxidation and hydrolysis reactions, conducted under controlled laboratory conditions. The analysis employed multiple regression analysis and machine learning algorithms, including random forest and support vector machines (SVM), to identify critical predictors of catalytic performance. The robustness of the models was assessed via cross-validation and receiver operating characteristic (ROC) curve analysis. A theoretical foundation based on the Sabatier principle and electronic structure theory underpins the conceptual model, emphasizing the interplay between MOF surface electronic properties and catalytic efficacy. Expected key findings include the identification of structural parameters such as pore size distribution, metal oxidation state, and functional group density as primary predictors of catalytic activity. The developed predictive framework is anticipated to yield a high degree of accuracy, with predictive coefficients of determination (R²) exceeding 0.85, facilitating reliable estimations of catalytic performance based solely on structural and spectroscopic data. This framework advances current knowledge by integrating multiple analytical techniques into a unified model, providing a systematic approach to MOF design. The study's contribution to knowledge lies in establishing an empirical and theoretical foundation for the rational design of MOF catalysts, bridging the gap between material characterization and catalytic performance prediction. It offers a versatile tool for researchers and industry practitioners to accelerate the development of tailored MOFs for targeted applications, reducing reliance on trial-and-error synthesis. In conclusion, this research underscores the importance of a data-driven, integrative approach in catalysis science, demonstrating that combining statistical modeling with structural analysis can significantly enhance the predictability and customization of MOF catalysts. It is recommended that future work extend the framework to include in situ spectroscopic data and operando studies for real-time performance prediction, as well as exploring the applicability to other classes of porous materials. The findings provide a vital step toward the systematic engineering of advanced catalytic materials, supporting sustainable chemical processes and environmental remediation efforts.