Toward next-generation acid-stable nanofiltration membranes: Polyamide focused insights and amide-free alternatives on materials, degradation pathways, and performance optimization

Abstract

Acidic process streams from hydrometallurgy, lithium-ion battery recycling, semiconductor and steel manufacturing, phosphoric acid production, acid mine drainage, and biorefineries demand separation technologies capable of maintaining stability under highly corrosive conditions. This review provides an integrated perspective on acid-resistant nanofiltration membranes by correlating monomer chemistry, fabrication methods, and degradation mechanisms with operational performance. Semi-aromatic, fully aromatic, polysulfonamide, and triazine (cyanuric chloride)-based systems, along with polyelectrolyte multilayer and covalent organic framework architectures, are compared to elucidate the interrelationships among structure, properties, and stability. The effects of pressure, feed concentration, solution pH, salt type, and temperature are analyzed to elucidate the trade-off between flux and selectivity. Mechanistic insights obtained from scanning electron microscopy, contact angle measurements, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, zeta potential analysis, and time-of-flight secondary ion mass spectrometry (ToF-SIMS) are integrated with density functional theory and molecular dynamics simulations. In particular, ToF-SIMS enables molecular-level identification of surface chemical transformations induced by acid exposure, providing a powerful tool for differentiating degradation pathways and kinetics as a function of polymer structure. These combined analyses identify protonation-assisted amide hydrolysis as the rate-determining degradation pathway and highlight planarity-driven resonance stabilization as the principal factor governing acid tolerance. The review concludes with design guidelines for next-generation membranes that integrate intrinsic chemical robustness, scalable fabrication, and predictive modeling to enable circular resource recovery from acid-laden industrial effluents.