Sustainable removal of organic pollutants using flax shives-derived hydrochar
Bibliographic record
Abstract
This study explored the efficient and cost-effective removal of persistent pollutants, pentachlorophenol (PCP) and methylene blue (MB) dye, commonly found in wastewater using a biomass-derived adsorbent. Abundantly available biomass waste, flax shives, was used to synthesize a low-cost adsorbent via the hydrothermal carbonization process without modifications. Such prepared hydrochar was characterized by using different physicochemical techniques to assess its structural and chemical properties. Rapid adsorption within the first few minutes was observed for both PCP and MB dye in a batch-mode reaction system. The adsorbent achieved 81 % removal of PCP and 95 % removal of MB within 20 and 45 min, respectively. For both model compounds, the pseudo-second-order model demonstrated a strong fit to the experimental data. The dimensionless separation factor and Freundlich model constant were found to be less than 1, suggesting efficient pollutant removal. The results demonstrated that the point of zero charge ( pH PZC ) of the adsorbent played an important role in the removal efficiency. PCP adsorption was favored in an acidic pH range, while MB adsorption was more effective in a basic environment. The regeneration and reusability potential of synthesized adsorbent was also assessed. After six adsorption-desorption cycles, PCP removal efficiency declined by 23 %, while MB removal efficiency decreased by approximately 10 %. These findings demonstrate the promising potential of hydrothermally synthesized flax shive-based adsorbents for wastewater treatment applications. • Flax shives were converted into an environmentally friendly biosorbent through HTC. • Rapid removal of Pentachlorophenol and MB dye was observed. • The adsorption process was primarily driven by chemical adsorption. • Restricted reusability due to gradual adsorbent physical degradation after each cycle.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".