Superior removal of humic acid from aqueous stream using novel calf bones charcoal nanoadsorbent in a reversible process
Bibliographic record
Abstract
While the potable water disinfection regimen has significantly reduced waterborne diseases, development of disinfection byproducts (DBP) during this process has brought a global threat to the environment and human health. The most notorious water pollutant, humic acid (HA), transforms into carcinogenic byproducts during the disinfection process (chlorination) of water treatment. HA removal methods are neither economic nor widely available. This study addresses the most urgent global issue of HA removal by developing an innovative and self-regenerative process based on a low-cost and self-regenerative calf bone char (CBC) that removed 92.1–100% of HA. CBC-based HA removal has not been described yet. The developed CBC, as a super adsorbent of HA, was initially characterized by a scanning electron microscope. Various parameters of adsorption/desorption and self-regeneration of CBC adsorbent were experimentally determined. Results show that prepared CBC with a 112 m2/g surface area exhibited adsorption of 38.08 mg/g (HA = 20 mg/L, pH = 4.0) which is several folds higher than the typical amount of HA present in water. The 30 m reaction time was enough to remove HA which is the shorter HA time in comparison to other similar studies. The increase of HA from 0.5 to 5 g/L, raises % HA removal (36.7–99.8%) while a pH decrease (10–4) increases adsorption (12.3–98.3%). The adsorption data fitted well with the pseudo-second-order model and the Langmuir isotherm which demonstrate that adsorption takes place by a monolayer formation. Thermodynamic constants supported the endothermic, spontaneous and reversible nature of adsorption which can attain 100% HA removal. 100% regeneration of exhausted CBC by NaOH further supports the sustainability of the process. CBC as a new adsorbent material thus provides an economical and sustainable water pre-treatment procedure. The present study provides technical guidance for building a cost-effective and scalable process capable of providing clean water.
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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 | 0.000 | 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".