Fast synthesis of high surface area bio-based porous carbons for organic pollutant removal
Bibliographic record
Abstract
A fast, facile and one-pot chemical activation method was used to develop porous carbons with high surface area and excellent phenolic micropollutant adsorption performance from renewable precursors. This method was applied to three precursors: naturally abundant, but often underestimated wildfire-damaged boreal peats, corn starch, and cellulose. Porous carbon formation was accomplished through precursor impregnation with ZnCl2 powder and their simultaneous pyrolysis under inert N2 flow at 400 or 600 °C for 1 h. The maximum adsorption capacities of these bio-sorbents towards a model contaminant, p-nitrophenol, in simulated wastewater were equal to or superior than using a commercial activated carbon (CAC), Norit GSX (> 530 mg/g) over wide initial concentration ranges (20–2000 mg/L). p-nitrophenol adsorption best fitted Freundlich and Redlich-Peterson isotherms, suggesting multilayer chemisorption. Low concentration p-nitrophenol (20 mg/L) adsorption into the bio-sorbents was rapid in the first 4 h, and could reach high removals (> 98%). The method presented here yielded bio-sorbents with similarly high adsorption performance regardless of the precursor type, while avoiding energy-intensive processing steps during sorbent production. This study gives a useful alternative for manufacturing new sorbents from other upcycled carbonaceous and/or bio-based materials to remove micropollutants and heavy metals. Fast, single-step chemical activation for manufacturing bio-based porous carbons. Efficient adsorption towards aqueous phenolic micropollutant from batch studies. A competitive substitute of charcoal activated carbons for water purification.
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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".