Foam as a Vehicle for Nanoparticle Delivery in Unsaturated Porous Media
Notice bibliographique
Résumé
Accidental releases, spills, and leaks of petroleum hydrocarbons (PHCs), such as gasoline and jet fuel, can result in subsurface contamination. PHCs that become entrapped as a residual light non-aqueous phase liquid (LNAPL) in the unsaturated zone often serve as a long-term source to groundwater contamination depending on the composition. Technologies used to remediate these residuals are categorized according to where the treatment occurs (in situ vs ex situ). In situ treatment technologies are preferred by the remediation community since they are typically more economical, efficient, and environmentally friendly. Nanoremediation is an emerging in situ treatment technology that has received attention since it has the potential to reduce both the cost and the time to clean-up contaminated sites. Nanoremediation obtains its advantage from the use of nanoparticles that have higher reactivity owing to their increased surface area. Appropriate delivery of engineered nanoparticles (ENPs) into the contaminated sites is crucial for effective treatment. Liquid-based delivery of ENPs to the unsaturated zone is a challenge due to the limited lateral transport and restricted contact between ENPs and contaminants. Foam is potentially capable of overcoming some of the challenges associated with liquid-based delivery of ENPs. This thesis focuses on the investigation of foam capability as a vehicle to deliver Pluronic coated iron oxide core nanoparticles in unsaturated porous media. A multi-criteria decision analysis (MCDA) was used to select foaming agents (surfactants) which were then screened to determine foam properties, and used in a column transport study to investigate the capability of foam to transport ENPs in unsaturated media. Based on the MCDA results, five surfactants (Steol CS-460, Bioterge AS-40, SDS, Ammonyx LO, and Rhamnolipids) were selected. These surfactants were added to Milli-Q water or synthetic groundwater to prepare foaming solutions, and the foamability and foam stability of the generated foams were determined. Steol CS-460 and Bioterge AS-40 were shown to have the highest foamability and foam stability and therefore, were selected for the subsequent experimental efforts. Ammonyx LO was also used for comparison purposes. The three surfactants enhanced the foamabilitiy of the ENPs. Steol CS-460 and Bioterge AS-40 increased the foam stability of the ENPs, whereas Ammonyx LO did not noticeably change it. Foams generated by Steol CS-460, Bioterge AS-40, or Ammonyx LO were able to support the ENPs. It was demonstrated that for foam generation, the ENPs were compatible with the foamability of the surfactants and the foam stability of Bioterge AS-40, and Ammonyx LO. However, the stability of the foam generated by Steol CS-460 slightly decreased from 99.2% ± 0.9% to 90.5% ± 0.7% in the presence of the ENPs. The column experiments were designed and conducted to evaluate the capability of foam generated by Steol CS-460, Bioterge AS-40, and Ammonyx LO to transport the ENPs in unsaturated media. The impact of surfactant type and concentration, and initial volumetric water content of the unsaturated porous medium on the capability of foam to transport the ENPs were investigated. The surfactant type was found to affect the capability of foam to transport the ENPs as the ENP mass recovery by the Steol CS-460 foam (101%) and Bioterge AS-40 foam (93%) were significantly higher than the ENP mass recovery by the Ammonyx LO foam (54%), and the ratio of foam outflow rate to the average foam inflow rate was the lowest for Ammonyx LO. The ENP mass recovery remained essentially unchanged as the Steol CS-460 concentration decreased from 0.3 to 0.1 wt%. Furthermore, as the initial volumetric water content of the porous medium decreased from 0.2 to dry condition, the ENP and tracer mass recoveries by the Steol CS-460 foam slightly decreased. Overall, the results of the column experiments indicate that the foam generated by Steol CS-460 was successfully capable to deliver Pluronic coated iron oxide core nanoparticles in unsaturated porous media, and thus foam has the potential to deliver the ENPs in situ.
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 ».