Synthesis and characterization of electrospun-based composite for the remediation of pharmaceutical pollutants in wastewater
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».