lUse of Green Biosurfactants and Nanomaterials for Mining Residue and Effluent Remediation
Notice bibliographique
Résumé
Removal of metals from mining effluents is challenging. Different novel approaches have used for remediation of the metal containing effluents. Although ultrafiltration can remove high molecular weight molecules, it is not effective for removal of low molecular weight pollutants. To increase the size of these pollutants, a rhamnolipid biosurfactant was utilized in micellar-enhanced ultrafiltration (MEUF) of heavy metals from contaminated waters. Various operating conditions were investigated and optimized for copper, zinc, nickel, lead and cadmium. Six contaminated wastewaters from metal refining industries were treated using two different membranes. The resulting heavy metal concentrations in the treated water were all significantly reduced to accord with the federal Canadian regulations. Another approach included an evaluation of the use of a biosurfactant, rhamnolipid, for the removal and reduction of hexavalent chromium from contaminated water. The initial chromium concentration, rhamnolipid concentration, pH and temperature affected the reduction efficiency. Complete reduction by rhamnolipid of initial Cr (VI) in water at optimum conditions (pH 6, 2% rhamnolipid concentration, 25oC) occurred at a low chromium concentration (10 ppm). Experiments were also conducted to investigate the effect of rhamnolipid on the remediation of chromium(VI) from water using iron nanoparticles. Iron nanoparticles were produced in the presence of different concentrations of rhamnolipid. Then, unmodified nanoparticles were treated with different concentrations of rhamnolipid and carboxymethyl cellulose. Furthermore the effect of the presence of rhamnolipid on reductive remediation of hexavalent chromium, Cr (VI), to trivalent, Cr (III), was investigated. At concentrations of 0.08 g/L iron and 2% (w/w) of rhamnolipid, the remediation of chromium increased by 123% in 15 hours compared with solutions containing only iron nanoparticles or only rhamnolipid. In addition, the applicability of iron/copper bimetallic nanoparticles for removal of arsenic from contaminated waters was investigated. Sorption tests in aqueous arsenic solutions at three different concentrations with various doses of nanoparticles were performed. Synthesized nanoparticles of hybrid Fe/Cu nanoparticles with a mean diameter of 13.17 nm were effective for removing arsenic from aqueous solutions. The Fe/Cu nanoparticle powder was found to be effective for removal of arsenic from water over a period of 18 months and has potential to be used for arsenic remediation from the aquatic environment in the long term. Overall, depending on the metal contamination, biosurfactants and/or nanoparticle addition may be effective for metal contamination effluent treatment.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».