Removal of phenoxy acid herbicide from water by magnetic mesoporous silicate
Bibliographic record
Abstract
Superparamagnetic iron (II,III) oxide (Fe3O4)–MCM-41 was successfully synthesised using a direct hydrothermal route and was utilised as an excellent adsorbent for the removal of the herbicide 2,4-dichlorophenoxyacetic acid from aqueous solution. The superparamagnetic mesoporous adsorbent was fully characterised using X-ray diffraction, field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, nitrogen (N2) sorption and vibrating-sample magnetometry (VSM). The synthesised adsorbent exhibited a high specific surface area of 444 m2/g, making it a suitable candidate for adsorption, where it provided a higher adsorption capacity. Based on the VSM study, the recorded saturated magnetisation was 15.9 emu/g, which confirmed the viability of easily separating the adsorbent from an aqueous medium using a magnet. The ability of iron (II,III) oxide–MCM-41 to adsorb the herbicide 2,4-dichlorophenoxyacetic acid from water was tested considering various parameters, such as pH, adsorbent dosage, contact time and pollutant concentration. The effectiveness of iron (II,III) oxide–MCM-41 in removing the herbicide reached its highest peak at pH = 5, 0.05 g adsorbent, 60 min contact time and 10 mg/l 2,4-dichlorophenoxyacetic acid. The maximum removal efficiency under optimum conditions was 94.8%. Based on the experimental data and kinetic studies, the adsorption process followed a pseudo-second-order kinetic and Langmuir isotherm model.
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) | 0.000 | 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".