The effects of environmental transformation on the ecotoxicity of photoactive nanomaterials to freshwater algae Chlorella vulgaris
Notice bibliographique
Résumé
Photoactive nanomaterials (NM) are being applied for their optical properties in various consumer and industrial applications such as self-cleaning glass, photocatalysis, environmental remediation and electronic displays. Photoactive NM are activated when a photon of energy higher than their band gap is absorbed, triggering the promotion of an electron (e-) from the valence to the conduction band resulting in the generation of an electron-hole pair (e-h pair). These e-h pairs may then undertake different pathways, one of which being the formation of reactive oxygen species (ROS) by reacting with oxygen and water. While these photo-induced ROS are responsible for many desirable properties, such as self-cleaning abilities or photoremediation of organic pollutants, they can also cause adverse health effects on human and environmental systems. Given the popularity of photoactive NM, their leaching into the environment is inevitable, raising concerns about their effects on environmental health and safety. Being the ultimate sink for photoactive NM released into air and soil systems, aquatic systems are expected to be the most severely impacted among the different environmental matrices. Additionally, NM are likely to be transformed by environmental factors such as light and components of the aquatic system, adding complexity to their risk analysis. There are important knowledge gaps in the understanding of how environmental transformations impacts the ecotoxicity of photoactive NM. My PhD research addressed consequence of photoactive NM transformation on their ecotoxicity. Specifically, I studied (1) how the eco corona formed on the surface of TiO2 NM impacted their toxicity to freshwater algae in the presence and absence of light, (2) the role of natural organic matters (NOM) and light on the transformation of Quantum dots (QDs) and its consequence on their toxicity to freshwater algae, (3) the link between chemical composition, and environmental parameters on the hazard potential of different classes of QDs. In this thesis, the effects of environmental transformations of TiO2 and QDs were studied using a model organism of freshwater algae, Chlorella vulgaris. In chapter 3, I investigated the effects of eco coronas of lignin, tannic acid and humic acid on the phototoxicity of TiO2 NM. My studies showed that, surface adsorption of NOM reduced bandgap energies, surface oxygen vacancies and ROS generation. Notably however, phototoxicity of TiO2 NM were enhanced through the phenolic radicals formed by photodegradation of NOM. Chapter 4 then explored Cadmium based QDs as photoactive materials, their transformation when interacted with humic acid and light and its consequence on their toxicity to C. vulgaris. Light was found to accelerate the dissolution of QD and contribute to higher bioavailability if heavy metal ions that induce high toxicity. Humic acid, in combination with light, affected dissolution, and aggregation of QDs and sequestering of toxic metal ions. As a consequence, when toxic metals were involved, light increased and humic acid decreased toxicity. Building on the knowledge of chapter 3 and 4, chapter 5 explored the ecotoxicity under realistic conditions of more commercially relevant QDs, namely, Cadmium based QDs, Indium based QDs, Lead based Perovskite QDs and Carbon QDs, helping to fill the ecotoxicity data gap for QDs at their end of the life. A model was trained using Monte Carlo optimization to correlate the ecotoxicity of the 4 commercially relevant QDs, with the inclusion of physicochemical properties of QDs and environmental conditions of the study, which showed the possibility of deciphering the contributions of parallel conditions, offering an opportunity to explore ecotoxicity prediction. In summary, this thesis contributed to the knowledge gap of ecotoxicity studies with realistic conditions, improved understandings of environmental transformation mechanisms, established correlations between the fate and toxicity responses of C. vulgaris
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) | 0,001 | 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 ».