Reticular framework architectures for coagulation-flocculation and colloidal separation: Mechanistic insights, structure-performance correlation, and AI-guided discovery

dc.citation.epage30
dc.citation.spage1
dc.citation.volume416
dc.contributor.authorAsma Sohail
dc.contributor.authorBadrul Mohamed Jan
dc.contributor.authorNazeri Abdul Rahman
dc.contributor.authorMahar Diana Hamid
dc.contributor.authorRabia Ikram
dc.contributor.departmentFaculty of Engineering
dc.date.accessioned2026-09-10T08:41:10Z
dc.date.issued2026-12-01
dc.description.abstractThe controlled destabilization of colloidal systems remains a central challenge in water purification and related separation processes. Although reticular framework architectures (RFAs), including covalent organic frameworks (COFs) and metal-organic frameworks (MOFs), have been extensively explored for adsorption-driven separations, their role in active coagulation-flocculation remains comparatively underdeveloped. This review critically evaluates RFAs as emerging coordination-based coagulants and flocculants, emphasizing their limited but rapidly growing application space and identifying key mechanistic and design constraints. A unified mechanistic framework is developed by integrating colloidal science with RFAs chemistry, describing pollutant removal through charge regulation, adsorption bridging, and pore-mediated aggregation within the Derjaguin-Landau-Verwey-Overbeek (DLVO) theoretical framework. Unlike conventional coagulants, which primarily rely on nonspecific charge neutralization, RFAs enable structurally programmable and coordination-driven interactions that enhance selective aggregation and floc formation. The structure-performance relationships governing coagulation performance are critically analyzed with respect to framework topology, surface chemistry, porosity, and interfacial electrostatics. In addition, emerging data-driven approaches, including artificial intelligence (AI), are highlighted as powerful tools for accelerating the design and optimization of next-generation reticular coagulants across large chemical spaces. Overall, this review establishes a mechanistic and design-oriented foundation for RFAs in coagulation-flocculation systems and identifies key challenges related to stability, mechanistic ambiguity, and scalability that must be addressed for practical deployment in advanced separation technologies.
dc.description.referencesUncontrolled Keywords: Reticular framework architectures, Metal-organic frameworks, Covalent organic frameworks, Coagulation-flocculation, Structure-performance relationships. Artificial intelligence
dc.description.statusPublished
dc.identifier.citationAsma, S., Mohamed Jan, B., Abdul Rahman, N., Diana Hamid, M., & Ikram, R. (2026). Reticular framework architectures for coagulation-flocculation and colloidal separation: Mechanistic insights, structure-performance correlation, and AI-guided discovery. Separation and Purification Technology, 416, 1–30. https://doi.org/10.1016/j.seppur.2026.139943
dc.identifier.doihttps://doi.org/10.1016/j.seppur.2026.139943
dc.identifier.emailarnazeri@unimas.my
dc.identifier.issn1383-5866
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S1383586626032090
dc.identifier.urihttps://scholarhub.unimas.my/handle/123456789/2177
dc.publisherElsevier B.V.
dc.relation.ispartofSeparation and Purification Technology
dc.titleReticular framework architectures for coagulation-flocculation and colloidal separation: Mechanistic insights, structure-performance correlation, and AI-guided discovery
dc.typeArticles
dc.type.statusYes

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