Experimental, Machine-Learning, and Computational Studies of the Sequestration of Pharmaceutical Mixtures Using Lignin-Derived Magnetic Activated Carbon
Bibliographic record
Abstract
Pharmaceutical pollutants pose significant risks to human health and aquatic ecosystems. This study investigates lignin-derived magnetic carbon composite (L-MAC) for removing atenolol (ATN), carbamazepine (CBZ), diclofenac (DCF), and sulfamethoxazole (SMZ) from aqueous media. Characterization of L-MAC’s physicochemical properties, along with isotherm and kinetic studies, revealed that the Langmuir and pseudo-second-order models best describe sorbent–sorbate interactions, with maximum adsorption capacities ranging from 11.30 to 27.97 mg/g. The adsorption efficiency followed the order ATN < SMZ < CBZ < DCF, achieving over 99% removal under optimal conditions of 1–4 h contact time and pH 2–7. Strong π–π interactions, hydrogen bonding, and chemisorption contributed to sorption irreversibility. Artificial intelligence models predicted a material performance with high accuracy. The adaptive neuro-fuzzy inference system model outperformed others, achieving error coefficients of 5.745, 3.125, and 11.085 during training and 6.123, 4.974, and 12.456 during testing. Density functional theory analysis examined reactivity and binding strength using descriptors like HOMO–LUMO energy gaps. DCF showed the highest electron-donor capacity, followed by CBZ, ATN, and SMZ, confirming L-MAC’s high efficacy in removing pharmaceuticals. This study demonstrates L-MAC’s robustness for the adsorptive removal of contaminant mixtures.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".