ADSORPTION OF PHARMACEUTICALS FROM CONTAMINATED WATER BY ADSORBENTS DEVELOPED FROM REED CANARY GRASS
Bibliographic record
Abstract
Discharge of pharmaceuticals into aquatic environments has been an emerging concern and removal of pharmaceuticals from water is very important. Sulfamethoxazole (SMX) is a frequently detected contaminant in wastewater and surface water and is a model of pharmaceutical contaminants because they have similar functional groups. Adsorption processes can be an effective technology to remove the pollutants. In this research, lignocellulosic material reed canary grass and activated carbons developed from this biomass were used as novel adsorbents to remove SMX. Surface area of the adsorbents played an important role in the adsorption process. The activated carbon with the highest surface area showed the highest adsorption capacity among the adsorbents. The SMX adsorption capacity favored acidic pH. Sips model simulated the experimental data well and was suitable since the adsorbent’s surface was heterogeneous. Existence of trimethoprim (TMP) in water affected SMX adsorption. Competitive adsorption of SMX and TMP decreased the adsorption capacity of the molecules compared to their single component adsorption. The change in enthalpy of SMX adsorption and activation energy were -45.5 and 35.2 kJ/mol, respectively, indicating the process was exothermic and primarily physisorption. Weighted mean of site energy distribution decreased from 11.21 kJ/mol at 15 °C and 11. 22 kJ/mol at 25 °C to 9.6 kJ/mol at 35 °C, showing stronger interactions at the lower temperature. π-π interactions, hydrogen bonding, Lewis acid base interactions, and hydrophobic interactions were the possible mechanisms that could be responsible for the adsorption. For the adsorbents with mean particle sizes of 84 and 216 μm, surface adsorption was rate controlling and the pseudo-second order model fitted the data better. For the adsorbent with larger particle size, diffusion was rate controlling and the pseudo-second order model simulated the data better. Desorbing the contaminant with methanol was more effective compared to aqueous solvents. The adsorbent could be reused for SMX adsorption during consecutive adsorption-desorption cycles. The adsorbents demonstrated capabilities to effectively adsorb SMX and showed a potential to remove SMX and similar pharmaceutical pollutants from real contaminated water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".