Synthesis of a Multi-Template Molecular Imprinted Bulk Polymer for the Adsorption of Non-Steroidal Inflammatory and Antiretroviral Drugs
Bibliographic record
Abstract
In this paper, we report the synthesis of a multi-template molecularly imprinted polymer (MIP) to target and extract naproxen, ibuprofen, diclofenac, emtricitabine, tenofovir disoproxil, and efavirenz from wastewater bodies. A bulk polymerization procedure was used to synthesize the MIP and non-imprinted polymer (NIP). The specific recognition sites for each target were obtained through the removal of the imprinted targeted compounds. The interaction of antiretroviral drugs (ARVs) and non-steroidal anti-inflammatory drugs (NSAIDs) compounds with the MIP was studied under various conditions such as pH, mass, concentration, and time factors. The results demonstrated the optimum conditions were 55 mg of MIP, pH 7.0, a concentration of 5 mg L−1, and a contact time of 10 min. For every compound studied, the extraction efficiencies for ARVs and NSAIDs in aqueous solutions was >96%. The adsorption capacity for the MIP was >0.91 mg·g−1. Adsorption obeys a second-order rate, and the Freundlich model explains the adsorption isotherm data. This study demonstrated that the synthesized multi-template MIP has huge potential to be employed for the removal of ARVs and NSAIDs from the environment as well as in drug purification or recovery processes.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".