Design of Sustainable Biomaterial Composite Adsorbents for Point-of-Use Removal of Lead Ions From Water
Bibliographic record
Abstract
The uncontrolled release of contaminants into aquatic environments has created the need for improved adsorbent materials for point-of-use (POU) treatment applications to address water security. The goal of this study was to prepare a low-cost sustainable adsorbent material with tailored Pb(II) adsorption properties in aqueous media. Several types of ternary composite adsorbents were prepared that contain chitosan, kaolinite, and a biomass additive (oat hulls or torrefied wheat straw), along with spectral characterization and thermal analysis of the adsorbents. The adsorption properties of the ternary composites with lead nitrate were studied at equilibrium using batch mode and dynamic conditions with a fixed bed column under variable experimental settings [flow rate, bed height, and Pb(II) concentration]. The adsorption capacity at equilibrium in synthetic or tap water was found to depend on the relative composition (wt.%) of additive components in the composite. The optimal composite adsorbent for maximum Pb(II) removal had the following composition (wt.%): chitosan (50%) + kaolinite (10%) + oat hulls (40%). Using this adsorbent, the dynamic adsorption properties with lead nitrate were studied in a fixed bed column at pH 6.5 and 295 K to reveal optimized Pb(II) removal that concur with the results obtained from batch studies. The sustainability of the biocomposite adsorbent was demonstrated with the use of relatively low-cost and locally available materials, whilst achieving favorable Pb(II) adsorption properties. The facile preparation of the optimal biocomposite adsorbent herein is proposed for use as a disposable POU filter media technology for the removal of lead and other multivalent heavy metal cations, including organic contaminants such as cationic dyes and agrochemicals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".