Mechanistic study of the increased phototoxicity of titanium dioxide nanoparticles to Chlorella vulgaris in the presence of NOM eco-corona
Bibliographic record
Abstract
Widespread applications and release of photoactive nanoparticles (NPs) such as titanium dioxide (TiO2) into environmental matrices warrant mechanistic investigations addressing toxicity of NPs under environmentally relevant conditions. Accordingly, we investigated the effects of surface adsorbed natural organic matters (NOMs) such as humic acid, tannic acid and lignin on the band gap energy, abiotic reactive oxygen species (ROS) generation, surface chemistry and phototoxicity of TiO2 NPs. Initially, a liquid assisted grinding method was optimized to produce TiO2 NPs with a NOM layer of defined thickness for further analysis. Generally, adsorption of NOM reduced the band-gap energy of TiO2 NPs from 3.08 eV to 0.56 eV with humic acid, 1.92 eV with tannic acid and 2.48 eV with lignin. Light activated ROS generation by TiO2 NPs such as hydroxyl radicals, however, was reduced by 4, 2, 9 times in those coated with humic acid, tannic acid and lignin, respectively. This reduction in ROS despite decrease in band gap energy corroborated with the decreased surface oxygen vacancy (as revealed by X-ray Photoelectron Spectroscopy (XPS)) and quenching of ROS by surface adsorbed NOM. Despite the reduced ROS generation, the NOM-modified TiO2 NPs exhibited an increased phototoxicity to Chlorella vulgaris in comparison to pristine TiO2 NPs. Further analysis suggested that photoactivation of NOM modified TiO2 NPs releases toxic degradation products. Findings from our studies thus provide mechanistic insight into the ecotoxic potential of NOM-modified TiO2 NPs when exposed to light in the environment.
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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".