Aggregation and transport of nanoscale zerovalent iron particles in model groundwater systems: role of particle coatings and biofilms
Notice bibliographique
Résumé
Nanoscale zerovalent iron (NZVI) particles are promising engineered nanomaterials for the in situ remediation of various environmental contaminants into innocuous products. In field applications, direct injection of NZVI into the subsurface has been suggested as a promising technique for achieving rapid remediation. However, challenges have been encountered in field application that includes passivation, aggregation and limited transport. Therefore, the success of site remediation using NZVI depends on the progress made to increase nanoparticle reactivity, reduce aggregation and improve mobility. The overall objective of this research was to evaluate the aggregation and transport behavior of NZVI particles in environmentally relevant model groundwater environments. Palladium-doped NZVI (Pd-NZVI) was chosen to ensure heightened reactivity towards the contaminants to address the passivation problem whereas particle surface-modification with stabilizing polymers was investigated to reduce particle aggregation and concurrently improve transport. In this study, rhamnolipid biosurfactant was proposed for the first time as a novel stabilizing surface-modifier and its efficacy was compared with previously proposed surface-modifiers (carboxymethylcellulose and soy protein). By monitoring changes in particle hydrodynamic diameter as a function of time using dynamic light scattering followed by a systematic assessment of transport behavior in sand packed columns, it is shown that while bare Pd-NZVI is prone to rapid aggregation surface-modified Pd-NZVI exhibited good colloidal stability and improved transport at low ionic strengths (10 mM). In particular, rhamnolipid significantly enhanced transport even at much lower concentrations than the other surface modifiers (10 mg/L compared to 100 mg/L). However, an increase in solution ionic strength influenced both aggregation and transport behavior. Nonetheless, at the highest ionic strength tested, the transport of rhamnolipid-coated Pd-NZVI was significantly higher than that of Pd-NZVI coated with other surface modifiers suggesting that rhamnolipid is most suitable in field application.The transport potential of surface-modified Pd-NZVI was further examined in granular matrices of varied complexities: in quartz sand, in loamy sand and clay-amended quartz sand over a wide range of environmentally relevant ionic strengths. Data suggests that collector geochemical composition and heterogeneity can dramatically alter Pd-NZVI transport potential; markedly reduced transport potential was observed in loamy sand than in quartz sand.Given that microbes and biofilms are ubiquitous in the subsurface environment, the role of biofilm on Pd-NZVI transport was investigated by using biofilm-coated quartz sand to pack transport columns. Transport results showed heightened Pd-NZVI retention in the presence of biofilms suggesting that biofilms may act as a collector surface for nanoparticle retention in the groundwater environment. A viability assay suggests that the retained Pd-NZVI is non-toxic to the Pseudomonas aeruginosa cells in biofilms. However, some inhibitory effect was observed to planktonic bacterial cells. Finally, considering the cost associated with the use of Pd, an alternative reactive nanoparticle, sulfidated NZVI (S-NZVI), was also studied whereby the aggregation and transport behavior of S-NZVI was systematically investigated in a wide range of environmentally relevant water chemistries. Data suggests that sulfidation can influence NZVI surface electrokinetic properties, and thus its stability and transport in granular matrices.Overall, this study makes a major impact in the field of environmental remediation as it addresses key aspects of nanotechnology-enabled site remediation, particularly aspects pertaining to nanoparticle surface coating, collector grain physical, geochemical and biological heterogeneity, and groundwater chemistry.
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 | 0,000 | 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 | 0,000 | 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 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 ».