Adsorption of methyl orange and methylene blue on activated biocarbon derived from birchwood pellets
Bibliographic record
Abstract
This study explored the adsorption capacity of a physically activated biocarbon derived from the pyrolysis of birchwood pellets (ABPB) towards two dyes - methyl orange (MO, anionic) and methylene blue (MB, cationic), at pH 2, 7 and 11. Compared to mineral commercial activated carbon (CAC mineral ), ABPB exhibited lower SSA, pores volume and surface; higher external surface and average pore diameter, similar ash content and aromaticity, and stronger hydrophilicity and polarity. The maximum adsorption capacity on ABPB was equal to 220 mg/g for MO and 91 mg/g for MB after 17 h. Batch tests with variable adsorbent amount (0.5–2.5 g/L) showed for both ABPB and CAC mineral better results for lower adsorbent dose. Under all tested conditions, ABPB showed higher or analogous adsorption capacity compared to CAC mineral . Based on the pK a of MB (3.80) and MO (3.46) and on the pH PZC of ABPB (5.3), the adsorption was favored at pH 2 for MO and at pH 11 for MB. Kinetics analysis and isotherm modelling revealed that, although many different physicochemical interactions occurred between ABPB and the dyes molecules, chemisorption is the rate-controlling step and prevalent mechanism. In conclusion, this study may provide support to further research aimed at exploring the effect of ABPB's physicochemical properties on the efficiency and mechanisms of dyes adsorption. • The adsorption capacity of a physically activated biochar from birchwood pellets has been investigated. • Methyl orange (anionic) and methylene blue (cationic) have been used as model adsorbates. • Activated birchwood pellets biochar showed higher or analogous adsorption capacity compared to commercial activated carbon. • Chemisorption is the rate-controlling step and prevalent adsorption mechanism.
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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".