Sustainable carbon materials for magnetic adsorbent-based pentachlorophenol removal from wastewater
Bibliographic record
Abstract
The growing water scarcity caused by human activities and urbanization has made it essential to develop sustainable and cost-effective strategies for wastewater treatment and reuse. This study involved converting biomass waste from agricultural and forestry sources into magnetic carbon-based adsorbents through hydrothermal carbonization (HTC). During the HTC process, iron was incorporated to impart magnetic properties to the resulting hydrochar, enabling post-treatment recovery using an external magnet. Hydrochars derived from flax shives (FS-Fe-HC), and eucalyptus (ES-Fe-HC) were tested for their efficiency in adsorbing pentachlorophenol (PCP) from wastewater. The structural, morphological, and chemical characteristics of the prepared hydrochar were characterized through scanning electron microscopy-electron diffraction spectroscopy (SEM-EDS), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Brunauer-Emmett-Teller (BET), and vibrating sample magnetometry (VSM) techniques. Batch adsorption experiments revealed maximum PCP removal efficiencies of 95% for FS-Fe-HC, and 88.5% for ES-Fe-HC under optimized conditions. The adsorption performance was found to be influenced by surface functional groups, active adsorption sites, and pH-dependent surface charge. Both hydrochars exhibited excellent reusability over six consecutive adsorption-desorption cycles, with negligible iron leaching, confirming their stability and practical applicability. This study demonstrates the potential of HTC as a sustainable approach for valorizing lignocellulosic waste into effective bio-adsorbents for wastewater remediation, addressing both environmental and industrial challenges. The novelty lies in utilizing dual biomass waste, magnetic recovery capability, and high reusability with minimal iron leaching, thereby contributing to circular economy practices in water treatment.
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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 | 0.000 | 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".