Study the Smart Nanomaterials Operating as Non-Equilibrium Systems for Intelligent Air Pollution Control.

Abstract: Air pollution control has traditionally relied on passive adsorption and steady-statecatalytic conversion, implicitly assuming near-equilibrium operation. However, realatmospheric environments are inherently non-stationary, fluctuating, and far fromequilibrium, with pollutant concentrations, humidity, temperature, and flow exhibiting strongtemporal variability. In this work, we study a new physics-based paradigm for air pollution mitigation based on adaptive smart nanomaterials operating as non-equilibrium, feedback-controlled systems. We propose that pollutant capture, release, and transformation should be understood as a dynamical phase-space problem, governed by coupled transport, adsorptionkinetics, energy fluxes, and information feedback. A theoretical framework is developedcombining non-equilibrium statistical mechanics, reaction–diffusion theory, and active matterphysics, and demonstrates how stimuli-responsive nanomaterials can function as adaptivedissipative structures, dynamically reconfiguring their energy landscape to maximizepollutant removal efficiency while minimizing entropy production and secondary emissions.Performance metrics, stability criteria, and safety limits are derived from first-principlesconsiderations. This work establishes a foundational physics roadmap for next-generationintelligent air purification systems.