Multidimensional Sustainability Framework for Metal–Organic Frameworks: Clustering Analysis Reveals Application-Specific Green Chemistry Optimization Pathways

Metal–organic frameworks (MOFs) represent promising candidates for sustainable materials applications, yet systematic evaluation of their environmental impacts across diverse technological domains remains challenging. This study introduces a comprehensive sustainability assessment framework that advances beyond traditional green chemistry metrics through a hierarchical clustering analysis of 15 quantitative parameters. Our approach defines a three-dimensional parameter space using Parameter Resilience (PR), Sustainability Momentum (SM), and Criticality Coefficient (CC) to mathematically characterize sustainability behaviors across nine MOF application domains including drug delivery, catalysis, gas separation, and energy storage. Statistical analysis of sustainability parameters reveals four distinct clustering regimes: Resilient Performers (stable cross-application impact), Rapid Evolvers (high improvement potential), Critical Constraints (limited substitution options), and Steady Optimizers (predictable enhancement pathways). Multifractal analysis demonstrates noninteger dimensionality, explaining why conventional linear sustainability approaches fail to capture parameter interdependencies. A robust power law correlation between PR and CC identifies strategic intervention points for green chemistry optimization. Application-specific analysis reveals that biomedical MOF applications exhibit 37.5% Critical Constraint parameters due to stringent toxicity requirements, while environmental applications demonstrate 42.3% Steady Optimizer parameters, indicating more predictable improvement trajectories. The Composite Green Impact Index (CGII) enables tailored sustainability assessment through application-specific parameter weighting, with solvent toxicity emerging as the dominant factor for biomedical applications (elasticity = 0.49) and energy consumption for industrial processes (elasticity = 0.44). This multidimensional framework transforms sustainability assessment from descriptive evaluation to predictive science, enabling researchers to strategically prioritize green chemistry interventions based on mathematical parameter relationships rather than conventional chemical classifications. By identifying high-leverage parameters within each application domain, this approach provides quantitative guidance for developing more sustainable MOF synthesis protocols and accelerating the transition toward environmentally responsible advanced materials.