Functional hydrogels—enabling the gateway for sustainable water treatment and harvesting technologies
Bibliographic record
Abstract
In recent years, the combination of industrial growth and population expansion has intensified the global freshwater shortage, leading to extensive research into advanced water treatment and harvesting methods. Functional porous materials such as hydrogels and aerogels comprising polymer materials alone or with the addition of biomass, nanoparticles, and carbon-based materials having a 3D network structure, have gained significant attention in this field due to their superior water absorption, separation capability, and their ability to harvest water. This review examines techniques for hydrogel synthesis, including chemical and physical cross-linking. It highlights absorption/desorption of water, different water states in hydrogels, and the factors affecting these processes. Importantly, this review thoroughly covers the current developments in the application of hydrogels for water treatment techniques such as removal of organic pollutants, pharmaceuticals, and heavy metal removal, water disinfection, oil/water separation, and reverse osmosis. An assessment of existing shortcomings and potential future developments for hydrogel-based water treatment and purification systems rounds out the review.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".