A Framework for Quantifying Catalytic Active Site Heterogeneity in Porous Materials

Thesis Abstract

The efficiency and selectivity of catalytic processes in porous materials are fundamentally governed by heterogeneity in active sites, which arises from variations in coordination environment, defect distribution, and local pore topology. This study addresses the persistent challenge of quantifying active site heterogeneity and translating it into predictive catalytic performance metrics. The aim is to develop a unified framework that couples spectroscopic fingerprints with statistical descriptors of site diversity to predict reaction rates and selectivity for representative reactions (e.g., CO oxidation, olefin epoxidation) on metal-doped zeolites and mesoporous oxides. Specific objectives include (i) identifying robust spectroscopic and microscopic indicators of active site populations, (ii) constructing a hierarchical statistical model that links site heterogeneity to kinetic observables, (iii) validating the framework against benchmark experimental data and density functional theory (DFT) calculations, and (iv) delivering a quantitative toolkit suitable for material design optimization. A mixed-methods approach is employed. The research design integrates experimental synthesis of three porous catalysts with controlled defect and dopant levels (n = 9 samples per material class; total n ? 27) and quantitative characterization using operando X-ray absorption spectroscopy (XAS), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), high-resolution transmission electron microscopy (HRTEM), and probe-based chemisorption measurements. Kinetic data are gathered through flow reactor experiments under standardized conditions, producing datasets of turnover frequency (TOF), selectivity, and reaction order across 15–20 conditions per sample. The data collection instruments include synchrotron-based XAS for oxidation state and coordination chemistry, DRIFTS for adsorbate geometry, and online mass spectrometry for product analysis. The analytical framework comprises (i) multivariate regression and partial least squares (PLS) to correlate spectral features with kinetic outputs, (ii) a hierarchical Bayesian model to quantify uncertainty in active site fractions and their reactivities, and (iii) a machine learning surrogate model to map site descriptors to performance metrics. DFT calculations provide atomistic descriptors of active site configurations to anchor experimental observables and to validate proposed site-structure–function relationships. The study anticipates that site heterogeneity can be decomposed into discrete populations with distinct turnover characteristics, and that a small set of spectrokinetic descriptors will capture most of the variance in reactivity. Expected findings include (i) identification of specific XAS-derived coordination environments and DRIFTS signatures that strongly correlate with rate enhancements, (ii) quantification of the relative contributions of isolated active sites versus ensemble or cooperative sites to overall activity, (iii) a validated predictive model with R2 > 0.8 and predictive intervals suitable for design optimization, and (iv) a transferable framework applicable to multiple porous materials classes. The theoretical basis draws on concepts from active site heterogeneity theory, Ligand Field Theory as applied to transition metal centers in solid matrices, and information-theoretic measures of site diversity to complement kinetic modeling. The contribution to knowledge comprises (i) a novel, experimentally grounded framework for quantifying catalytic active site heterogeneity in porous materials, (ii) a statistical toolkit that integrates spectroscopic fingerprints with kinetic data to yield interpretable site-level contributions to macroscopic performance, and (iii) a generalizable methodology that informs material design strategies for enhanced activity and selectivity. The study concludes that precise control and robust characterization of active-site distributions can materially improve predictive accuracy for catalyst performance, enabling targeted synthesis to concentrate favorable site populations while mitigating detrimental heterogeneity. Recommendations include adopting operando spectrokinetic diagnostics as standard practice in catalyst development, extending the framework to other reaction families, and investing in integrated data repositories to facilitate cross-material benchmarking. Potential limitations relate to spectral overlap among similar sites and the computational cost of extensive DFT benchmarking, which are addressed through regularization in the Bayesian model and selective, high-signal experiments.