Nano-enabled melamine sponge-based filters for continuous separation of varying size, charge and functionalized sub-micron plastics
Bibliographic record
Abstract
Despite significant progress in sub-micron plastics (SMPs) removal strategies, challenges remain in achieving effective, continuous separation across particle sizes and functionalities under diverse water conditions. Here, we report a nano-enabled filter fabricated by immobilizing alginate–layered double hydroxide (Alg-LDH) composites onto melamine sponges (MS), creating a robust, reusable, and multifunctional platform for removing SMPs from water matrices. Detailed surface characterization confirmed successful integration of LDH and alginate, yielding a highly porous, functionalized matrix with a zeta potential of + 22 mV for LDH, favoring electrostatic interactions with negatively charged plastics. Cyclic filtration tests demonstrated high adsorption capacities for 1000 nm amine-functionalized SMPs (4403.05 mg/kg in DI water and 4036.64 mg/kg in river water after 60 mL), attributed to strong electrostatic binding. Carboxylated SMPs (500 nm and 1000 nm) showed adsorption capacities of 2993.57 and 2340.89 mg/kg, respectively, in DI water. Filtration efficiency was influenced by water composition, with a slight reduction in river water, the performance trend remained consistent. The composite maintained performance across multiple cycles without saturation and showed minimal decrease of < 10 % in SMPs removal after regeneration. Mechanistic evaluation revealed that the composite integrates electrostatic attraction, hydrogen bonding, π–π stacking, and physical entrapment for robust SMP retention. This work presents a promising avenue for sustainable, size- and charge-selective filtration of SMPs under environmentally realistic conditions using scalable sponge-based materials. • A robust Alg-LDH-MS sponge filter was designed for sub-micron plastics (SMPs) removal. • Linear increase in retention capacity and no saturation up to 100 mg/L SMPs. • Composite retains removal performance under multicycle and matrix variations. • Charge and size of SMPs govern selective filtration efficiency of the sponge. • MS-LDH filter shows strong reusability and regeneration across multiple filtration cycles.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".