Ultrasonic assisted removal of methyl orange and bovine serum albumin from wastewater using modified activated carbons: RSM optimization and reusability
Bibliographic record
Abstract
Abstract The removal of industrial pollutants from water remains a significant challenge in water treatment processes. This study investigated the efficacy of powder-activated carbon (PAC), thermally modified PAC (TPAC), and chemically modified PAC (CPAC) for removing bovine serum albumin (BSA) and methyl orange (MO) from simulated wastewater. After undergoing treatment, the BET surface area of TPAC increased to 823 m2 g−1, while that of CPAC increased to 657 m2 g−1 compared to the initial surface area of pristine PAC, which was 619 m2 g−1. Batch adsorption experiments assisted by ultrasonication were conducted to evaluate the impact of solution pH, initial concentration, and contact time on the adsorption capacities (q max) of BSA and MO. TPAC demonstrated superior performance, achieving q max values of 152 mg g−1 for MO and 133 mg g−1 for BSA, compared to PAC, which provided q max values of 124 mg g−1 and 112 mg g−1, respectively. Furthermore, pH levels of 3 and 5 were identified as highly effective for the removal of MO and BSA from water, respectively. The adsorption kinetics of both MO and BSA followed pseudo2nd-order (R 2 > 0.99) reaction kinetics under both batch and ultrasonic conditions, confirming the removal of contaminants through chemisorption. The adsorption trends also satisfied the Langmuir isothermal model, indicating the formation of a uniform monolayer during the adsorption process of these contaminants. To understand the simultaneous effect of all the variables, response surface methodology (RSM) using central composite design (CCD) was used to predict the adsorption capacities of CPAC. After five adsorption cycles, the removal efficiencies of MO (from 98% to 80%) and BSA (from 55% to 40%) decreased in the CPAC system. The results suggested that CPAC can be effectively utilized to remove MO from wastewater.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".