Investigation of mechanisms of sulfamethoxazole adsorption on novel adsorbents developed from reed canary grass
Bibliographic record
Abstract
In this work, reed canary grass and activated carbons developed from this biomass were used as novel adsorbents to remove sulfamethoxazole (SMX) from water. Raw biomass adsorbent with the lowest surface area demonstrated the lowest SMX adoption capacity of 20.2 ± 1.6 mg/g and the activated carbon adsorbent with the highest surface area showed the highest adsorption capacity of 160.5 ± 4.2 mg/g. π-π, hydrogen bonding, Lewis acid base, and hydrophobic interactions are the possible mechanisms that could be responsible for adsorption of SMX on the adsorbents. Methanol, hydrochloric acid solution, sodium hydroxide solution, and deionized water were used to desorb SMX from the adsorbent. The results of using 20 mL of solvent at 35 °C showed that methanol could desorb loaded SMX with a higher desorption efficiency (80.1 ± 2%) than the aqueous solvents (9.5-46.5%). Decreasing the temperature to 25 °C decreased the desorption efficiency of methanol to 58.5 ± 1%. By decreasing the methanol volume to 5 mL, SMX desorption efficiency could remain comparable (59.1 ± 1.2%). Reusing the adsorbent in 4 adsorption desorption cycles showed that SMX adsorption capacity reduced from 124.9 ± 3.8 mg/g in the first adsorption cycle to 82.1 ± 2.2 mg/g in the second adsorption cycle and was stable in the third and fourth cycles.
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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".