Synthesis and characterization of electrospun-based composite for the remediation of pharmaceutical pollutants in wastewater
Bibliographic record
Abstract
Pharmaceutical pollutants, including non-steroidal anti-inflammatory drugs (NSAIDs) and antiretroviral drugs (ARVs), pose a significant threat to aquatic environments, necessitating effective remediation strategies. This comprehensive study delves into the efficacy of nanotechnological approaches, with a special focus on adsorption, in addressing the persistent issue of pharmaceutical pollution in wastewater bodies. The research covers the synthesis and characterization of a multi-template molecularly imprinted polymer (MIP) targeting key pharmaceutical compounds, namely naproxen, ibuprofen, diclofenac, emtricitabine, tenofovir disoproxil, and efavirenz, for extraction from contaminated water sources. Comparative analyses between the synthesized MIP and a commercial Solid Phase Extraction (SPE) cartridge showed comparative performance of the MIP and SPE cartridge in quantifying pharmaceutical compounds present in wastewater samples. The results highlighted both materials' consistent efficiency in the removal of pollutants, with selective pharmaceuticals exhibiting varying levels of removal efficiency during different treatment stages. Regressions analysis showcased high linearity (R2 values ranging from 0.9980 to 0.9999), alongside remarkable recoveries (90.9 % to 100 %) for the MIP and method detection limits (MDLs) ranging from (0.14-1.08 μg L-1) for all target pollutants. Recoveries for SPE samples ranged from (62 % to 98 %) with method detection limits at (0.7-4.68 μg L-1). The optimal conditions for efficient extraction of pharmaceutical compounds using the MIP were determined through a series of experiments, considering factors such as pH, mass, concentration, and contact time. Results showed high extraction efficiencies (>96%) and a notable adsorption capacity (>0.91 mg. g-1) for both ARVs and NSAIDs, confirming the MIP's potential for successful removal of these pollutants from wastewater. Additionally, adsorption kinetics were studied, revealing a second-order rate model and adherence to the Freundlich adsorption isotherm. Furthermore, this study incorporates synthesized MIP into the electrospinning technique, utilizing various polymer blends and optimized solvents to enhance the remediation process. The study explores the electrospun mats morphology, particularly those composed of polyvinyl alcohol (PVA) and polyethylene terephthalate (PET), examining their structural characteristics using techniques such as Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and adsorption time studies. Through merging advanced nanotechnological techniques with electrospinning methodologies, this study presents a robust framework for combating pharmaceutical pollutants in wastewater. The incorporation of the MIP into electrospun mats, coupled with in-depth material characterization and adsorption studies, emphasizes the potential of this innovative approach for environmental remediation and drug purification processes. This research contributes valuable insights into the effective removal and quantification of pharmaceutical pollutants, emphasizing the pivotal role of electrospinning technologies in addressing environmental challenges. In conclusion, this study sheds light on the potential of a multi-template MIP for the removal of ARVs and NSAIDs from contaminated water sources, showcasing its versatility and efficacy in enhancing water treatment processes, as well as its utility in drug purification and recovery processes. Overall, the research provides valuable insights into the complexities of pharmaceutical pollutant removal, emphasizing the significance of selecting appropriate extraction methodologies in wastewater treatment processes to ensure efficient and sustainable remediation practices.
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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 | 0.001 | 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) | 0.001 | 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".