Fast synthesis of high surface area bio-based porous carbons for organic pollutant removal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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) | 0.003 | 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 it