Geopolymeric membranes: A comprehensive review of emerging wastewater treatment solutions
Bibliographic record
Abstract
Abstract In today's world, wastewater treatment has become a critical challenge for environmental sustainability and public health, particularly due to the increasing presence of toxic metals and non‐biodegradable contaminants. Traditional methods such as adsorption, precipitation, ion exchange, membrane separation, and filtration categorized under chemical, physical, or biological approaches, are often limited by high costs, low efficiency, or negative environmental impacts. The selection of these techniques depends on effluent characteristics, operational conditions, and wastewater volume. Membrane‐based technologies have emerged as promising alternatives, offering higher efficiency, selectivity, and adaptability compared to conventional processes. Among these, geopolymer membranes represent a novel class of inorganic materials, synthesized through an eco‐friendly and versatile geopolymerization process. These membranes are typically fabricated from aluminosilicate precursors sourced from industrial byproducts like fly ash, rice husk ash, and phosphate tailings, thereby promoting waste valorization and sustainability. What distinguishes geopolymer membranes is their excellent thermal stability, robust chemical resistance, and highly tunable pore structure and surface properties. These characteristics enable them to function effectively under harsh conditions and selectively remove a broad spectrum of contaminants, potentially outperforming traditional polymeric and ceramic membranes. Their modular design also allows integration into customized advanced treatment systems tailored to specific pollutants. This review presents a comprehensive overview of the geopolymerization mechanism, key factors influencing membrane performance, and diverse applications in wastewater treatment. Special emphasis is placed on addressing current challenges such as scalability, fouling resistance, and long‐term durability, highlighting how geopolymer membranes can offer innovative solutions for sustainable water management and pollution control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".