A Unified Framework for Catalytic Pathway Optimization in Green Solvents

Thesis Abstract

The increasing demand for sustainable chemical processes necessitates a coherent framework that integrates catalytic pathway optimization within green solvent systems to reduce environmental impact without compromising efficiency or selectivity. This study addresses the gap in comprehensive models that simultaneously consider catalyst design, solvent effects, and reaction energetics to guide decision-making in green chemistry. The aim is to develop a unified framework that links catalytic pathway optimization with solvent as a design variable, enabling predictive assessment of reaction performance across diverse substrates and reaction classes. Specific objectives include (1) constructing a multi-criteria decision model that quantifies activity, selectivity, and environmental metrics (E-factor, solvent waste, and energy intensity) for catalytic routes in green solvents; (2) synthesizing a mechanistic-thermodynamic framework that integrates microkinetic modeling with solvent–solute interactions derived from quantum chemical descriptors; (3) validating the framework through a curated dataset of 60–80 catalytic reactions spanning homogeneous and heterogeneous systems in bio-based and recyclable solvents; (4) identifying robust solvent-catalyst pairings via sensitivity and uncertainty analyses; and (5) delivering a software prototype and guidelines for researchers to apply the framework to novel reaction systems. A mixed-methods approach combines quantitative modeling with qualitative expert validation. The research design comprises (i) a theoretical modeling phase that develops a modular, thermodynamically consistent framework incorporating density functional theory (DFT) derived activation barriers, explicit solvent effects via continuum and cluster models, and microkinetic simulations to predict turnover frequencies and selectivities; (ii) an empirical validation phase using a dataset of 75 curated reactions from peer-reviewed studies, spanning solvents such as polar aprotic, bio-based, and ionic liquids, to calibrate the model parameters; and (iii) a scenario analysis phase to test framework robustness across substrate classes. Population and sample considerations center on published reaction data and laboratory validation experiments. Data collection instruments include high-throughput virtual screening tools, in silico descriptors (binding energies, diffusion barriers, Hansen solubility parameters), and experimental measurements from lab-scale reactions (25–30 representative runs) to corroborate model predictions. Regression analysis, Bayesian calibration, and Monte Carlo uncertainty analysis will quantify parameter sensitivity and predictive confidence, while ANOVA will assess the significance of solvent class on catalytic outcomes. Theoretical grounding rests on established frameworks in reaction engineering, catalysis science, and green-chemistry principles, notably the Principles of Green Chemistry and the Sabatier principle, extended by a directed-graph framework linking catalyst states, solvent environments, and reaction coordinates. Expected findings include (a) a validated, scalable framework that can predict optimal solvent-catalyst configurations with quantified uncertainty; (b) identification of key descriptors governing solvent-driven activation energies and reaction pathways; (c) a set of generalized rules for selecting green solvents that maximize activity and selectivity while minimizing waste; and (d) a functional software prototype enabling researchers to input reaction details and obtain ranked catalytic pathways under green solvent constraints. The study anticipates that solvent polarity, hydrogen-bonding capability, and dielectric environment modulate transition-state energies in a manner that can be captured by integrated microkinetic models, enabling accurate predictions across diverse chemistries. Contributions to knowledge include (i) the first explicit integration of a mechanistic catalytic framework with green solvent design into a single predictive model, (ii) a transparent methodology for translating solvent effects into microkinetic parameters, and (iii) actionable guidelines and a software tool that accelerates the discovery of sustainable catalytic routes. The main conclusion posits that a unified framework can reliably guide the selection of solvent-catalyst combinations to achieve desired reaction outcomes with reduced environmental burden. Recommendations emphasize expanding the dataset to include more bio-based solvents and exploring machine-learning surrogates to further enhance predictive speed, as well as applying the framework to industrial-scale processes to evaluate economic feasibility and lifecycle impacts.