Conversion of Waste Corn Biomass to Activated Bio-Char for Applications in Wastewater Treatment
Bibliographic record
Abstract
This study proposes the conversion of waste corn grains contaminated by deoxynivalenol (also known as vomitoxin), a mycotoxin produced by plant pathogens, into a value-added product. Batches of 500 g of contaminated corn grains were pyrolyzed in a batch reactor by thermal treatment at temperatures up to 500°C with a 15°C/min heating rate and generating condensable vapors, gases and solid bio-char. The bio-char produced was subsequently activated in a furnace at 900°C, using CO2 as an activation agent, at different residence times. The effect of activation residence time on the characteristics of the activated bio-char, varying it from 0.5 to 3 h, was investigated. Characterization tests included BET surface area, SEM, TG-FTIR, pH, and XRD on both bio-char and activated bio-char. BET results illustrated a significant increase of the surface area from 63 to 419 m2g−1 and pore volume from 0.04 to 0.23 cm3g−1 by increasing the activation time from 0.5 to 3 h. SEM images visually confirmed a considerable increase in pore development. The pH significantly increased from 6 to 10 after activation, due to the elimination of acidic functional groups. The proximate analysis showed the stable carbon of the activated char reaching approximately 90 wt%, making it promising for catalyst/adsorbent applications. The adsorption performance of activated bio-char was tested by utilizing three different model molecules with different characteristics: methylene blue, methyl orange, and ibuprofen. Among all activated bio-char samples, activated bio-char with 3 h activation time showed the highest adsorption capacity, with a total adsorption (25 mg/g of activated bio-char) of methylene blue after 5 min. The results showed that the adsorption capacity of the activated bio-char was similar to that of valuable commercial activated carbon.
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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".