Biopolymers to composite adsorbents for sulfate removal: From conventional to sustainable systems
Bibliographic record
Abstract
Addressing elevated water salinity is a global water security issue listed among the UN's Sustainable Development Goals (UN-SDGs). Sulfate is a contributor to water salinity due to its high solubility and is a pollutant of increasing global concern. While various water treatment technologies are currently available, the high capital infrastructure and operational costs of such advanced methods have sustainability limits for their widespread adoption. By contrast, adsorption science and technology offers facile treatment and a sustainable mitigation strategy for the removal of oxyanions such as sulfate. A key challenge in adsorption science and technology relates to the molecular selective uptake of sulfate. This has catalysed significant effort towards achieving improved adsorption properties and the development of sustainable adsorbent technology. This review provides coverage of recent literature on synthetic adsorbents to current research on biosorbents that contain chitosan due to its multifunctional colloid and interface properties. The shift from conventional synthesis to green synthetic strategies are highlighted by the preparation of advanced biocomposite materials with unique sulfate adsorption properties. Diverse types of materials from inorganic minerals to polymer-based adsorbents (e.g., polycaprolactones, waste-based materials from fly ash, etc.) is described to highlight their sulfate adsorption properties. Specifically, chitosan and agricultural biomass waste in the form of lignocellulose materials are abundant and promising renewable platforms for the preparation of sulfate adsorbents. In particular, the adsorption properties of chitosan biocomposites are highlighted by its efficacy for adsorption-based remediation of sulfate oxyanions that reveal its promising utility as sulfate adsorbents with unique colloidal and interface properties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".