KOH-Assisted Chemical Activation of Camelina Meal (Wild Flax) to Treat PFOA-Contaminated Wastewater
Bibliographic record
Abstract
This study is constituted of the chemical activation of camelina meal (CM) biochar and the utilization of these activated carbon for the adsorption of perfluorooctanoic acid (PFOA) from water. Camelina meal, a sustainable agro-based byproduct, underwent slow pyrolysis and subsequent chemical activation with potassium carbonate (K2CO3), potassium hydroxide (KOH), and sodium hydroxide (NaOH). Among these chemical activating agents, KOH emerged as the one of most efficient activating agents, yielding activated carbon with superior surface properties and significantly higher carbon content. After the screening of the activating agents, a central composite design (CCD) was employed to optimize the critical constraints like temperature (600–900 °C), activation time (60–120 min), and KOH-to-feed ratio (0.5–1.5), with the objective of maximizing the surface area and adsorption capacities of the activated carbon samples. The activated carbon exhibited a substantial enhancement in surface area and PFOA adsorption efficacy. Optimal adsorption of PFOA was achieved using activated carbon produced at 800 °C with an activation time of 60 min and a KOH-to-feed ratio of 1.5. This material exhibited a surface area of 1558.4 m2/g and demonstrated a PFOA removal efficiency of 92.3%. The findings underscore the efficacy of chemically activated camelina meal biochar as an ecological adsorbent for the remediation of PFOA-polluted 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) | 0.000 | 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".