The effects of environmental transformation on the ecotoxicity of photoactive nanomaterials to freshwater algae Chlorella vulgaris
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".