Waste sludge from shipping docks as a catalyst to remove amoxicillin in water with hydrogen peroxide and ultrasound
Bibliographic record
Abstract
The waste sludge from shipping docks contains important elements that can be used as a catalyst after proper processing. The purpose of this study was to remove of amoxicillin (AMX) from the aquatic environment using waste sludge from shipping docks as catalyst in the presence of hydrogen peroxide/ultrasound waves. The catalyst was produced by treating waste sludge at 400 °C for 2 h. N2 adsorption, SEM, XRD, XRF, and FTIR techniques characterized the structural and physical properties of the catalyst. The BET-specific surface area of the catalyst reduced after AMX removal from 4.4 m2/g to 3.6 m2/g. To determine the optimal removal conditions, the parameters of the design of experiments were pH (5–9), contaminant concentration (5–100 mg/L), catalyst dosage (0.5–6 g/L), and concentration of hydrogen peroxide (10–100 mM). The maximum removal of AMX (98%) was obtained in the catalyst/hydrogen peroxide/ultrasound system at pH 5, catalyst dose of 4.5 g/L, H2O2 concentration of 50 mM, AMX concentration of 5 mg/L, and contact time of 60 min. The kinetics of removal of AMX from urine (k = 0.026 1/min), hospital wastewater (k = 0.021 1/min), and distilled water (k = 0.067 1/min) followed a first-order kinetic model (R2>0.91). The catalyst was reused up to 8 times and the AMX removal decreased to 45% in the last use. The byproducts and reaction pathway of AMX degradation were also investigated. The results clearly show that to achieve high pollutant removal rate the H2O2/ultrasound and catalyst/ultrasound synergy plays a key role.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".