Reticular framework architectures for coagulation-flocculation and colloidal separation: Mechanistic insights, structure-performance correlation, and AI-guided discovery
| dc.citation.epage | 30 | |
| dc.citation.spage | 1 | |
| dc.citation.volume | 416 | |
| dc.contributor.author | Asma Sohail | |
| dc.contributor.author | Badrul Mohamed Jan | |
| dc.contributor.author | Nazeri Abdul Rahman | |
| dc.contributor.author | Mahar Diana Hamid | |
| dc.contributor.author | Rabia Ikram | |
| dc.contributor.department | Faculty of Engineering | |
| dc.date.accessioned | 2026-09-10T08:41:10Z | |
| dc.date.issued | 2026-12-01 | |
| dc.description.abstract | The 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.references | Uncontrolled Keywords: Reticular framework architectures, Metal-organic frameworks, Covalent organic frameworks, Coagulation-flocculation, Structure-performance relationships. Artificial intelligence | |
| dc.description.status | Published | |
| dc.identifier.citation | Asma, 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.doi | https://doi.org/10.1016/j.seppur.2026.139943 | |
| dc.identifier.email | arnazeri@unimas.my | |
| dc.identifier.issn | 1383-5866 | |
| dc.identifier.uri | https://www.sciencedirect.com/science/article/abs/pii/S1383586626032090 | |
| dc.identifier.uri | https://scholarhub.unimas.my/handle/123456789/2177 | |
| dc.publisher | Elsevier B.V. | |
| dc.relation.ispartof | Separation and Purification Technology | |
| dc.title | Reticular framework architectures for coagulation-flocculation and colloidal separation: Mechanistic insights, structure-performance correlation, and AI-guided discovery | |
| dc.type | Articles | |
| dc.type.status | Yes |
