DOE OSTI · 3377364
A Unified Workflow for Sensitivity-Based Kinetic Analysis in Microkinetic Models
Abstract
Degrees of rate control (DRC), apparent activation energies, and apparent reaction orders are established local sensitivity diagnostics for interpreting microkinetic models, but applying them routinely to large mechanisms often requires substantial reaction-specific bookkeeping, perturbation design, and postprocessing. Here, in this study, we present a unified derivative-based workflow that evaluates these quantities from a single compiled reaction-network model and target-rate definition. For any user-provided microkinetic model, the workflow compiles the mechanism into stoichiometrically consistent mass-action rate equations, solves the surface dynamics, and uses automatic differentiation to compute sensitivities with respect to rate constants, temperature, and gas partial pressures. By combining their calculations in the same framework, the workflow clearly demonstrates the relationships between different DRCs and the apparent activation energy. Using existing examples of propylene partial oxidation and methane oxidation on Pd(100), we verify expected transient redistribution of rate control, distinguish net Campbell DRCs from one-sided directional sensitivities, and show how apparent activation energy can be reconstructed either from one-sided DRCs or from state-based DRCs while critical mechanistic insights are obtained consistently. In the methane oxidation case, a pathway-subset test further illustrates how a simplified mechanism preserves key kinetic signatures of a full model, showing the potential of our user-friendly tool for model construction beyond kinetic analysis.
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Shukla, Jay [Stony Brook University, NY (United States)], Wu, Qin [Brookhaven National Laboratory (BNL), Upton, NY (United States). Center for Functional Nanomaterials (CFN); Stony Brook University, NY (United States)] (ORCID:0000000163506672). 2026-07-06. A Unified Workflow for Sensitivity-Based Kinetic Analysis in Microkinetic Models. https://doi.org/10.1021/acs.iecr.6c01462
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